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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">The effects of past SARS experience and proximity on declines in numbers of travelers to the Republic of Korea during the 2015 MERS outbreak: A retrospective study</title><author><name sortKey="Joo, Heesoo" sort="Joo, Heesoo" uniqKey="Joo H" first="Heesoo" last="Joo">Heesoo Joo</name><affiliation><nlm:aff id="aff1">Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, GA, USA</nlm:aff></affiliation></author><author><name sortKey="Henry, Ronald E" sort="Henry, Ronald E" uniqKey="Henry R" first="Ronald E." last="Henry">Ronald E. Henry</name><affiliation><nlm:aff id="aff1">Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, GA, USA</nlm:aff></affiliation></author><author><name sortKey="Lee, Yeon Kyeng" sort="Lee, Yeon Kyeng" uniqKey="Lee Y" first="Yeon-Kyeng" last="Lee">Yeon-Kyeng Lee</name><affiliation><nlm:aff id="aff2">Korea Centers for Disease Control and Prevention, Cheongju-si, Chungcheongbuk-do, Republic of Korea</nlm:aff></affiliation></author><author><name sortKey="Berro, Andre D" sort="Berro, Andre D" uniqKey="Berro A" first="Andre D." last="Berro">Andre D. Berro</name><affiliation><nlm:aff id="aff1">Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, GA, USA</nlm:aff></affiliation></author><author><name sortKey="Maskery, Brian A" sort="Maskery, Brian A" uniqKey="Maskery B" first="Brian A." last="Maskery">Brian A. Maskery</name><affiliation><nlm:aff id="aff1">Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, GA, USA</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">31102656</idno><idno type="pmc">6688921</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688921</idno><idno type="RBID">PMC:6688921</idno><idno type="doi">10.1016/j.tmaid.2019.05.009</idno><date when="2019">2019</date><idno type="wicri:Area/Pmc/Corpus">001159</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001159</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">The effects of past SARS experience and proximity on declines in numbers of travelers to the Republic of Korea during the 2015 MERS outbreak: A retrospective study</title><author><name sortKey="Joo, Heesoo" sort="Joo, Heesoo" uniqKey="Joo H" first="Heesoo" last="Joo">Heesoo Joo</name><affiliation><nlm:aff id="aff1">Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, GA, USA</nlm:aff></affiliation></author><author><name sortKey="Henry, Ronald E" sort="Henry, Ronald E" uniqKey="Henry R" first="Ronald E." last="Henry">Ronald E. Henry</name><affiliation><nlm:aff id="aff1">Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, GA, USA</nlm:aff></affiliation></author><author><name sortKey="Lee, Yeon Kyeng" sort="Lee, Yeon Kyeng" uniqKey="Lee Y" first="Yeon-Kyeng" last="Lee">Yeon-Kyeng Lee</name><affiliation><nlm:aff id="aff2">Korea Centers for Disease Control and Prevention, Cheongju-si, Chungcheongbuk-do, Republic of Korea</nlm:aff></affiliation></author><author><name sortKey="Berro, Andre D" sort="Berro, Andre D" uniqKey="Berro A" first="Andre D." last="Berro">Andre D. Berro</name><affiliation><nlm:aff id="aff1">Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, GA, USA</nlm:aff></affiliation></author><author><name sortKey="Maskery, Brian A" sort="Maskery, Brian A" uniqKey="Maskery B" first="Brian A." last="Maskery">Brian A. Maskery</name><affiliation><nlm:aff id="aff1">Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, GA, USA</nlm:aff></affiliation></author></analytic><series><title level="j">Travel Medicine and Infectious Disease</title><idno type="ISSN">1477-8939</idno><idno type="eISSN">1873-0442</idno><imprint><date when="2019">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>The experience of previous sizable outbreaks may affect travelers’ decisions to travel to an area with an ongoing outbreak.</p></sec><sec><title>Methods</title><p>We estimated changes in monthly numbers of visitors to the Republic of Korea (ROK) in 2015 compared to projected values by selected areas. We tested whether areas’ experience of a previous SARS outbreak of ≥100 cases or distance to the ROK had a significant effect on travel to the ROK during the MERS outbreak using t-tests and regression models.</p></sec><sec><title>Results</title><p>The percentage changes in visitors from areas with a previous SARS outbreak of ≥100 cases decreased more than the percentage changes in visitors from their counterparts in June (52.4% vs. 23.3%) and July (60.0% vs. 31.4%) during the 2015 MERS outbreak. The percentage changes in visitors from the close and intermediate categories decreased more than the far category. The results from regression models and sensitivity analyses demonstrated that areas with ≥100 SARS cases and closer proximity to the ROK had significantly larger percentage decreases in traveler volumes during the outbreak.</p></sec><sec><title>Conclusions</title><p>During the 2015 MERS outbreak, areas with a previous sizable SARS outbreak and areas near the ROK showed greater decreases in percentage changes in visitors to the ROK.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Korea Centers For Disease Control And Prevention" uniqKey="Korea Centers For Disease Control And Prevention">Korea Centers for Disease Control and Prevention</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Korean Ministry Of Health And Welfare" uniqKey="Korean Ministry Of Health And Welfare">Korean Ministry of Health and Welfare</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="World Health Organization" uniqKey="World Health Organization">World Health Organization</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Korea Tourism Organization" uniqKey="Korea Tourism Organization">Korea Tourism Organization</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Joo, H" uniqKey="Joo H">H. Joo</name></author><author><name sortKey="Maskery, B A" uniqKey="Maskery B">B.A. Maskery</name></author><author><name sortKey="Berro, A D" uniqKey="Berro A">A.D. Berro</name></author><author><name sortKey="Rotz, L D" uniqKey="Rotz L">L.D. Rotz</name></author><author><name sortKey="Lee, Y K" uniqKey="Lee Y">Y.K. Lee</name></author><author><name sortKey="Brown, C M" uniqKey="Brown C">C.M. Brown</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Smith, R D" uniqKey="Smith R">R.D. Smith</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tourgo Tourism Knowledge And Information System" uniqKey="Tourgo Tourism Knowledge And Information System">TOURGO (Tourism Knowledge and Information System)</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="World Health Organization" uniqKey="World Health Organization">World Health Organization</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="World Health Organization" uniqKey="World Health Organization">World Health Organization</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hui, D S" uniqKey="Hui D">D.S. Hui</name></author><author><name sortKey="Memish, Z A" uniqKey="Memish Z">Z.A. Memish</name></author><author><name sortKey="Zumla, A" uniqKey="Zumla A">A. Zumla</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chowell, G" uniqKey="Chowell G">G. 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Steens</name></author><author><name sortKey="Tinoco, Y O" uniqKey="Tinoco Y">Y.O. Tinoco</name></author><author><name sortKey="Wallinga, J" uniqKey="Wallinga J">J. Wallinga</name></author><author><name sortKey="Yu, H" uniqKey="Yu H">H. Yu</name></author><author><name sortKey="Vong, S" uniqKey="Vong S">S. Vong</name></author><author><name sortKey="Bresee, J" uniqKey="Bresee J">J. Bresee</name></author><author><name sortKey="Widdowson, M A" uniqKey="Widdowson M">M.A. Widdowson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Welch, B L" uniqKey="Welch B">B.L. Welch</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rassy, D" uniqKey="Rassy D">D. Rassy</name></author><author><name sortKey="Smith, R D" uniqKey="Smith R">R.D. Smith</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Korea Tourism Organization" uniqKey="Korea Tourism Organization">Korea Tourism Organization</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="World Health Organization" uniqKey="World Health Organization">World Health Organization</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="World Health Organization" uniqKey="World Health Organization">World Health Organization</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Park, J" uniqKey="Park J">J. Park</name></author><author><name sortKey="Pomfret, J" uniqKey="Pomfret J">J. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Travel Med Infect Dis</journal-id><journal-id journal-id-type="iso-abbrev">Travel Med Infect Dis</journal-id><journal-title-group><journal-title>Travel Medicine and Infectious Disease</journal-title></journal-title-group><issn pub-type="ppub">1477-8939</issn><issn pub-type="epub">1873-0442</issn><publisher><publisher-name>Elsevier Science</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">31102656</article-id><article-id pub-id-type="pmc">6688921</article-id><article-id pub-id-type="publisher-id">S1477-8939(18)30392-2</article-id><article-id pub-id-type="doi">10.1016/j.tmaid.2019.05.009</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>The effects of past SARS experience and proximity on declines in numbers of travelers to the Republic of Korea during the 2015 MERS outbreak: A retrospective study</article-title></title-group><contrib-group><contrib contrib-type="author" id="au1"><name><surname>Joo</surname><given-names>Heesoo</given-names></name><email>hjoo@cdc.gov</email><xref rid="aff1" ref-type="aff">a</xref><xref rid="cor1" ref-type="corresp">∗</xref></contrib><contrib contrib-type="author" id="au2"><name><surname>Henry</surname><given-names>Ronald E.</given-names></name><xref rid="aff1" ref-type="aff">a</xref></contrib><contrib contrib-type="author" id="au3"><name><surname>Lee</surname><given-names>Yeon-Kyeng</given-names></name><xref rid="aff2" ref-type="aff">b</xref></contrib><contrib contrib-type="author" id="au4"><name><surname>Berro</surname><given-names>Andre D.</given-names></name><xref rid="aff1" ref-type="aff">a</xref></contrib><contrib contrib-type="author" id="au5"><name><surname>Maskery</surname><given-names>Brian A.</given-names></name><xref rid="aff1" ref-type="aff">a</xref></contrib></contrib-group><aff id="aff1"><label>a</label>Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, GA, USA</aff><aff id="aff2"><label>b</label>Korea Centers for Disease Control and Prevention, Cheongju-si, Chungcheongbuk-do, Republic of Korea</aff><author-notes><corresp id="cor1"><label>∗</label>Corresponding author. Division of Global Migration and Quarantine, United States Centers for Disease Control and Prevention, 1600 Clifton Road, MS H16-4, Atlanta, GA, 30333, USA. <email>hjoo@cdc.gov</email></corresp></author-notes><pub-date pub-type="pmc-release"><day>15</day><month>5</month><year>2019</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="ppub" iso-8601-date="2019-08-01"><season>July-August</season><year>2019</year></pub-date><pub-date pub-type="epub"><day>15</day><month>5</month><year>2019</year></pub-date><volume>30</volume><fpage>54</fpage><lpage>66</lpage><history><date date-type="received"><day>5</day><month>11</month><year>2018</year></date><date date-type="rev-recd"><day>9</day><month>5</month><year>2019</year></date><date date-type="accepted"><day>10</day><month>5</month><year>2019</year></date></history><permissions><license><license-p>Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.</license-p></license></permissions><abstract id="abs0010"><sec><title>Background</title><p>The experience of previous sizable outbreaks may affect travelers’ decisions to travel to an area with an ongoing outbreak.</p></sec><sec><title>Methods</title><p>We estimated changes in monthly numbers of visitors to the Republic of Korea (ROK) in 2015 compared to projected values by selected areas. We tested whether areas’ experience of a previous SARS outbreak of ≥100 cases or distance to the ROK had a significant effect on travel to the ROK during the MERS outbreak using t-tests and regression models.</p></sec><sec><title>Results</title><p>The percentage changes in visitors from areas with a previous SARS outbreak of ≥100 cases decreased more than the percentage changes in visitors from their counterparts in June (52.4% vs. 23.3%) and July (60.0% vs. 31.4%) during the 2015 MERS outbreak. The percentage changes in visitors from the close and intermediate categories decreased more than the far category. The results from regression models and sensitivity analyses demonstrated that areas with ≥100 SARS cases and closer proximity to the ROK had significantly larger percentage decreases in traveler volumes during the outbreak.</p></sec><sec><title>Conclusions</title><p>During the 2015 MERS outbreak, areas with a previous sizable SARS outbreak and areas near the ROK showed greater decreases in percentage changes in visitors to the ROK.</p></sec></abstract></article-meta></front><body><sec id="sec1"><label>1</label><title>Background</title><p id="p0010">The 2015 Middle East respiratory syndrome (MERS) outbreak in the Republic of Korea (ROK) was initiated by the arrival of a single infected international traveler who visited the Middle East [<xref rid="bib1" ref-type="bibr">1</xref>]. The first case was confirmed on May 20, 2015, and the outbreak resulted in 186 cases and 38 deaths before the government of the ROK and the World Health Organization (WHO) officially announced the end of the outbreak on December 23, 2015 [<xref rid="bib2" ref-type="bibr">2</xref>].</p><p id="p0015">During the 2015 MERS outbreak, the ROK government conducted entry screening and active monitoring of all travelers from the Middle East region to avoid additional importations of MERS cases [<xref rid="bib2" ref-type="bibr">2</xref>]. In addition, the ROK government restricted international departures by confirmed or suspected MERS patients or their contacts to prevent the global spread of MERS [<xref rid="bib2" ref-type="bibr">2</xref>]. Although WHO did not recommend any travel restrictions or entry screening for MERS coronavirus (MERS-CoV) [<xref rid="bib3" ref-type="bibr">3</xref>], the numbers of international travelers visiting the ROK decreased significantly during the 2015 MERS outbreak [<xref rid="bib4" ref-type="bibr">4</xref>,<xref rid="bib5" ref-type="bibr">5</xref>].</p><p id="p0020">Fear-induced behavioral changes during novel infectious disease outbreaks have been reported to depend on an individual's perception of risk rather than the actual risk [<xref rid="bib6" ref-type="bibr">6</xref>]. One factor that could affect an individual's perception of the risk of an infectious disease outbreak is the individual's previous experience of a similar infectious disease outbreak. Governments of countries that had a similar sizable outbreak, as well as individuals from those countries, may have responded differently to the ROK MERS outbreak than governments or individuals who had less exposure to a similar sizable outbreak.</p><p id="p0025">This evaluation estimates the changes in numbers of non-citizen short-term visitor arrivals from selected areas to the ROK during the 2015 MERS outbreak and examines the correlation between travel volume declines and previous experience of the most similar sizable outbreak, the 2003 severe acute respiratory syndrome (SARS) outbreak. We also examined the correlation between decrease in travel volume and proximity to the ROK.</p></sec><sec id="sec2"><label>2</label><title>Methods</title><p id="p0030">We used monthly international traveler statistics from TOURGO, the Korea tourism knowledge and information system, maintained by the Korea Culture and Tourism Institute [<xref rid="bib7" ref-type="bibr">7</xref>]. The data include numbers of inbound non-citizen short-term visitor arrivals by area following the international guideline from the United Nations World Tourism Organization [<xref rid="bib7" ref-type="bibr">7</xref>]. The numbers of arrivals reported in this analysis are limited to the numbers of non-citizen short-term visitor arrivals.</p><p id="p0035">Each area includes one country or political unit. Note that area information in the data set is based on travelers’ passports rather than the areas from which travelers departed. First, we selected areas comprising more than 0.5% of total non-citizen visitor arrivals, i.e. more than 70 000 visitors to the ROK during 2014. Selected areas in descending order of numbers of arrivals to the ROK during 2014 are China (mainland China excluding Hong Kong Special Administrative Region (SAR), Macau, and Taiwan), Japan, United States (USA), Taiwan–China, Hong Kong SAR–China, Thailand, Philippines, Malaysia, Russia, Indonesia, Singapore, India, Canada, Vietnam, Australia, United Kingdom (UK), Germany, and France. They combined to contribute 94% of the total number of arrivals to the ROK during 2014. Monthly actual numbers of arrivals in 2015 by area from TOURGO are shown in <xref rid="dtbl1" ref-type="table">Appendix Table 1</xref>.</p><p id="p0040">For each area, we compared monthly numbers of arrivals in 2015 to the monthly average numbers of arrivals in 2013 and 2014. The baseline percentage change in numbers of arrivals (y) of each area to the ROK by month was calculated from the projected values (averages of 2013 and 2014) subtracted from the actual values (2015) and then divided by the projected values as shown in the following equation:<disp-formula id="ufd1"><mml:math id="M1" altimg="si1.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo linebreak="badbreak">=</mml:mo><mml:mfrac><mml:mrow><mml:mi>A</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>u</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:msub><mml:mrow><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo linebreak="badbreak">−</mml:mo><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>o</mml:mi><mml:mi>j</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:msub><mml:mrow><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>w</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>o</mml:mi><mml:mi>j</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:msub><mml:mrow><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>w</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo linebreak="goodbreak">×</mml:mo><mml:mn>100</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math></disp-formula>where “<italic>i</italic>” stands for each area, while “<italic>j</italic>” stands for each month during the ROK MERS outbreak from June 2015 to December 2015. If the actual value is smaller than the projected value in a given area-month, i.e. decreasing arrivals associated with the MERS outbreak, the negative value <inline-formula><mml:math id="M2" altimg="si2.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> corresponds to the estimated percentage decrease in visitor arrivals. The number of travelers for each area in each month were treated as a single observation. We assumed that any changes of numbers of arrivals caused by the MERS outbreak would be observed starting in June because the first MERS case was confirmed on May 20, 2015.</p><p id="p0045">We chose two main variables that could affect individuals’ decisions to travel to an outbreak area. The first variable was whether an area had a sizable outbreak of limited duration of a novel viral respiratory disease before the 2015 MERS outbreak. The 2003 SARS outbreak was chosen because it was sizable including local transmission and its duration was limited [<xref rid="bib8" ref-type="bibr">8</xref>]. During the 2003 SARS outbreak, more than 8000 individuals were infected worldwide with a 9.6% average case fatality rate [<xref rid="bib9" ref-type="bibr">9</xref>]. In addition, SARS and MERS are both viral respiratory infections caused by coronaviruses, although the case fatality rates reported for SARS were lower than for MERS [<xref rid="bib10" ref-type="bibr">10</xref>]. Community transmission of SARS was more commonly reported, while MERS transmission in the ROK occurred primarily in healthcare settings or through close contact, e.g. caring for or living with an infected person [<xref rid="bib10" ref-type="bibr">10</xref>,<xref rid="bib11" ref-type="bibr">11</xref>]. The 2009 H1N1 influenza pandemic was also caused by viral respiratory infection; however, country-specific H1N1 attack rates were orders of magnitude higher and its case fatality rate was much lower compared to the limited-duration outbreaks of SARS and MERS [<xref rid="bib10" ref-type="bibr">10</xref>,<xref rid="bib12" ref-type="bibr">12</xref>,<xref rid="bib13" ref-type="bibr">13</xref>].</p><p id="p0050">Using WHO data for reported numbers of probable SARS cases by area during the 2003 outbreak [<xref rid="bib9" ref-type="bibr">9</xref>], we hypothesized that individuals from areas with ≥100 SARS cases during the 2003 outbreak may have been significantly more or less likely to visit the ROK during the 2015 MERS outbreak. China, Taiwan–China, Singapore, Canada, and Hong Kong SAR–China each had ≥100 SARS cases during the 2003 outbreak, and all of those areas were included in the current analyses. Although the threshold was set at 100 probable SARS cases, each of the five areas experienced at least 238 probable SARS cases, while the country/area with next highest number of probable SARS cases, Vietnam, had 64 cases (<xref rid="appsec3" ref-type="sec">Appendix Figure 3</xref>) [<xref rid="bib9" ref-type="bibr">9</xref>]. Thus, our results would be consistent with any threshold set between 65 cases and 237 WHO-reported probable cases.</p><p id="p0055">The second variable we considered was travel distance to the ROK from each area. Since travel time cost is likely to be correlated to travel distance between two areas, the opportunity cost to change travel plans from a more distant place to the ROK would be expected to be higher. Thus, individuals from more distant areas might be less likely to cancel trips to the ROK during the outbreak period. We subdivided selected areas into three groups based on distance to the ROK: (1) close (China, Japan, Taiwan–China, and Hong Kong SAR–China), (2) intermediate (Thailand, Philippines, Malaysia, Indonesia, Singapore, India, and Vietnam), and (3) far (USA, Russia, Canada, Australia, UK, Germany, and France). More details about how we defined distance categories for analyses are shown in <xref rid="dtbl2" ref-type="table">Appendix Table 2</xref>, <xref rid="dtbl3" ref-type="table">Appendix Table 3</xref></p><p id="p0060">For both variables, we performed Welch's t-tests for two samples with unequal variances [<xref rid="bib14" ref-type="bibr">14</xref>] to examine whether the monthly average percentage decreases in numbers of arrivals from each category differed significantly. For the SARS variable, the null hypothesis is that the simple average percentage change of arrivals from areas with <100 SARS cases (reference) was the same as the percentage change of arrivals for areas with ≥100 SARS cases. The alternative hypothesis is that the percentage decreases for arrivals from areas with ≥100 SARS cases were larger than for areas with <100 SARS cases. For the distance categories, the null hypothesis is that the simple average percentage change of arrivals from the far category (reference) was the same as the percentage change of arrivals from the intermediate or the close category. The alternative hypothesis is that the percentage decreases for arrivals from areas in the far category were smaller than for arrivals from the areas in the intermediate or the close categories. When <italic>p</italic>-values from Welch's t-tests are <0.05, we reject the null hypotheses in favor of the alternative hypotheses.</p><p id="p0065">We used ordinary least squares (OLS) regression models to examine the impact of each factor on percentage changes in numbers of visitor arrivals for each month during the ROK MERS outbreak (June–December 2015). The following equation was used in an OLS regression model.<disp-formula id="ufd2"><mml:math id="M3" altimg="si3.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo linebreak="badbreak">=</mml:mo><mml:mi>α</mml:mi><mml:mi>S</mml:mi><mml:mi>A</mml:mi><mml:mi>R</mml:mi><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo linebreak="goodbreak">+</mml:mo><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mi>C</mml:mi><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>s</mml:mi><mml:msub><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo linebreak="goodbreak">+</mml:mo><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mi>I</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>m</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mi>i</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:msub><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo linebreak="goodbreak">+</mml:mo><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></disp-formula>“<italic>y</italic><sub><italic>ij</italic></sub>” is a dependent variable that shows percentage changes in numbers of arrivals by area-month. “<italic>SARS</italic><sub><italic>i</italic></sub>” is a dummy variable, which is one an area “<italic>i</italic>” reported ≥100 SARS cases during the 2003 outbreak, otherwise zero. “<italic>Close</italic><sub><italic>i</italic></sub>” is a dummy variable, which is one when an area “<italic>i</italic>” is in the “Close” category, otherwise zero. “<italic>Intermediate</italic><sub><italic>i</italic></sub>” is an additional distance dummy variable. When an area “<italic>i</italic>” is in the “Intermediate” category, the variable is one, otherwise zero. In summary, the OLS model included two independent variables: 1) a binary variable to identify areas with ≥100 SARS cases during the 2003 outbreak (reference category: <100 SARS cases during the 2003 outbreak), and 2) a categorical variable summarizing the distance between an area and the ROK that was transformed into two binary variables (reference category: “far”).</p><p id="p0070">We conducted sensitivity analyses using alternative projected values. The baseline model used the average numbers of arrivals in 2013 and 2014 by area to project the expected numbers of arrivals to the ROK in the absence of the outbreak. The baseline model, however, may not account for year-to-year increasing or decreasing trends in the numbers of arrivals to the ROK. That omission could result in underestimated or overestimated projected values in the absence of the MERS outbreak, causing biased estimates from regression models. Thus, we examined two alternative models.</p><p id="p0075">Alternative 1 used projected value based on the monthly average numbers of arrivals from each area in 2014 and 2016; i.e. the predicted value in June 2015 is the average number of arrivals in June 2014 and June 2016. This alternative assumes that neither 2014 nor 2016 travel volumes were affected by the MERS outbreak and that the expected monthly travel volumes in 2015 in the absence of the outbreak could have approximated the average volumes of 2014 and 2016.</p><p id="p0080">For Alternative 2, we again started with the monthly average numbers of arrivals in 2013 and 2014. Since the ROK MERS outbreak was not expected to affect travel volumes during the pre-outbreak period in 2015, we compared the arrivals from January to May 2015 to the average number of arrivals from January to May in 2013 and 2014 to calculate an area-specific adjustment to the projected numbers of arrivals from June to December 2015 in the absence of the outbreak. Alternative 2 used the same method that was used to compare projected and actual numbers of arrivals in Mexico during the 2009 H1N1 pandemic [<xref rid="bib15" ref-type="bibr">15</xref>].</p><p id="p0085">To avoid bias, we conducted additional sensitivity analyses by excluding outliers. Potential outliers were flagged if any area-month (<italic>y</italic><sub><italic>ij</italic></sub>) observation was ≥100% or ≤ -100%. We only identified one observation that met these outlier criteria. We excluded the observation for India in August 2015, when an exceptional increase in the number of crewmembers arriving at seaports [<xref rid="bib16" ref-type="bibr">16</xref>] resulted in an increase of 106.4% relative to the average of arrivals for India in August 2013 and 2014.</p></sec><sec id="sec3"><label>3</label><title>Results</title><p id="p0090">Decreases in numbers of arrivals to the ROK were observed during June and July 2015 across all examined areas compared to the average numbers of arrivals in 2013 and 2014 (<xref rid="tbl1" ref-type="table">Table 1</xref>). In June, the number of arrivals decreased by 7.5% from the least affected area and 74.2% from the most affected area. The simple average decrease across 17 areas was 31.4%. In July, the simple average decrease in numbers of arrivals was 39.3% (range 4.8%–82.0% across areas). Although travel volumes declined across all areas during June and July, actual traveler volumes for some areas exceeded projected values starting in August and continuing through December 2015 (<xref rid="appsec3" ref-type="sec">Appendix Figures 1 and 2</xref>).<table-wrap position="float" id="tbl1"><label>Table 1</label><caption><p>Percentage changes between 2015 actual and projected (average of 2013 and 2014) monthly non-citizen arrivals to the Republic of Korea (ROK) by area (%).</p></caption><alt-text id="alttext0075">Table 1</alt-text><table frame="hsides" rules="groups"><thead><tr><th rowspan="2">Area</th><th>Jan<hr></hr></th><th>Feb<hr></hr></th><th>Mar<hr></hr></th><th>Apr<hr></hr></th><th>May<hr></hr></th><th>Jun<hr></hr></th><th>Jul<hr></hr></th><th>Aug<hr></hr></th><th>Sep<hr></hr></th><th>Oct<hr></hr></th><th>Nov<hr></hr></th><th>Dec<hr></hr></th></tr><tr><th colspan="4">Before the Middle East respiratory syndrome (MERS) outbreak</th><th>First MERS case reported May 20, 2015</th><th colspan="2">Peak months of the MERS outbreak</th><th colspan="5">Non-peak months of the MERS outbreak</th></tr></thead><tbody><tr><td align="left">China</td><td align="left">60.0</td><td align="left">79.4</td><td align="left">47.1</td><td align="left">48.0</td><td align="left">55.3</td><td align="left">−35.2</td><td align="left">−59.5</td><td align="left">−26.7</td><td align="left">12.9</td><td align="left">43.6</td><td align="left">37.7</td><td align="left">33.5</td></tr><tr><td align="left">Japan</td><td align="left">−26.2</td><td align="left">−29.9</td><td align="left">−18.3</td><td align="left">−19.2</td><td align="left">−11.2</td><td align="left">−45.9</td><td align="left">−57.0</td><td align="left">−37.9</td><td align="left">−29.0</td><td align="left">−17.1</td><td align="left">−18.1</td><td align="left">−17.1</td></tr><tr><td align="left">USA</td><td align="left">2.4</td><td align="left">4.1</td><td align="left">4.1</td><td align="left">18.3</td><td align="left">6.5</td><td align="left">−13.8</td><td align="left">−4.8</td><td align="left">3.0</td><td align="left">−3.6</td><td align="left">3.8</td><td align="left">10.6</td><td align="left">6.8</td></tr><tr><td align="left">Taiwan-China</td><td align="left">10.1</td><td align="left">16.6</td><td align="left">17.3</td><td align="left">40.5</td><td align="left">16.4</td><td align="left">−74.2</td><td align="left">−82.0</td><td align="left">−54.0</td><td align="left">−7.1</td><td align="left">−9.5</td><td align="left">3.4</td><td align="left">6.3</td></tr><tr><td align="left">Hong Kong SAR-China</td><td align="left">19.4</td><td align="left">40.6</td><td align="left">47.8</td><td align="left">56.5</td><td align="left">53.1</td><td align="left">−68.9</td><td align="left">−81.1</td><td align="left">−23.6</td><td align="left">53.3</td><td align="left">12.5</td><td align="left">13.8</td><td align="left">18.0</td></tr><tr><td align="left">Thailand</td><td align="left">19.9</td><td align="left">3.9</td><td align="left">8.2</td><td align="left">0.2</td><td align="left">−1.6</td><td align="left">−32.0</td><td align="left">−55.3</td><td align="left">−43.1</td><td align="left">−42.1</td><td align="left">−21.7</td><td align="left">−16.4</td><td align="left">4.2</td></tr><tr><td align="left">Philippines</td><td align="left">−3.2</td><td align="left">4.9</td><td align="left">2.2</td><td align="left">12.0</td><td align="left">2.2</td><td align="left">−26.9</td><td align="left">−42.8</td><td align="left">−20.1</td><td align="left">7.5</td><td align="left">13.8</td><td align="left">16.2</td><td align="left">13.1</td></tr><tr><td align="left">Malaysia</td><td align="left">11.1</td><td align="left">6.0</td><td align="left">20.9</td><td align="left">23.2</td><td align="left">3.4</td><td align="left">−29.1</td><td align="left">−42.8</td><td align="left">−23.0</td><td align="left">−8.7</td><td align="left">−7.7</td><td align="left">6.8</td><td align="left">−3.3</td></tr><tr><td align="left">Russia</td><td align="left">−9.5</td><td align="left">−11.1</td><td align="left">−1.4</td><td align="left">−2.1</td><td align="left">6.4</td><td align="left">−18.2</td><td align="left">−21.9</td><td align="left">−7.0</td><td align="left">5.7</td><td align="left">3.0</td><td align="left">8.0</td><td align="left">8.4</td></tr><tr><td align="left">Indonesia</td><td align="left">8.0</td><td align="left">30.0</td><td align="left">4.8</td><td align="left">13.2</td><td align="left">8.4</td><td align="left">−26.7</td><td align="left">−44.3</td><td align="left">−23.6</td><td align="left">6.2</td><td align="left">15.2</td><td align="left">13.0</td><td align="left">−4.0</td></tr><tr><td align="left">Singapore</td><td align="left">11.7</td><td align="left">1.9</td><td align="left">13.6</td><td align="left">15.5</td><td align="left">11.1</td><td align="left">−59.7</td><td align="left">−55.8</td><td align="left">−32.5</td><td align="left">−19.2</td><td align="left">−17.3</td><td align="left">−7.9</td><td align="left">−19.0</td></tr><tr><td align="left">India</td><td align="left">−6.1</td><td align="left">14.7</td><td align="left">−4.1</td><td align="left">6.5</td><td align="left">22.0</td><td align="left">−22.2</td><td align="left">−40.6</td><td align="left">106.4</td><td align="left">5.6</td><td align="left">29.3</td><td align="left">28.3</td><td align="left">11.2</td></tr><tr><td align="left">Canada</td><td align="left">7.1</td><td align="left">17.1</td><td align="left">16.2</td><td align="left">31.9</td><td align="left">14.9</td><td align="left">−23.8</td><td align="left">−21.6</td><td align="left">−5.9</td><td align="left">−6.4</td><td align="left">3.0</td><td align="left">10.1</td><td align="left">8.7</td></tr><tr><td align="left">Vietnam</td><td align="left">46.0</td><td align="left">−11.0</td><td align="left">67.2</td><td align="left">56.5</td><td align="left">28.4</td><td align="left">−7.5</td><td align="left">−26.3</td><td align="left">6.2</td><td align="left">21.2</td><td align="left">49.7</td><td align="left">65.6</td><td align="left">35.7</td></tr><tr><td align="left">Australia</td><td align="left">10.9</td><td align="left">2.9</td><td align="left">−14.8</td><td align="left">28.6</td><td align="left">30.6</td><td align="left">−28.3</td><td align="left">−19.0</td><td align="left">−5.7</td><td align="left">−11.6</td><td align="left">11.7</td><td align="left">−1.9</td><td align="left">9.0</td></tr><tr><td align="left">UK</td><td align="left">−4.4</td><td align="left">1.7</td><td align="left">6.6</td><td align="left">31.9</td><td align="left">2.5</td><td align="left">−28.9</td><td align="left">−26.9</td><td align="left">−8.4</td><td align="left">−12.4</td><td align="left">4.5</td><td align="left">7.4</td><td align="left">1.9</td></tr><tr><td align="left">Germany</td><td align="left">−13.3</td><td align="left">−12.6</td><td align="left">10.8</td><td align="left">6.5</td><td align="left">1.1</td><td align="left">−13.8</td><td align="left">−15.3</td><td align="left">0.3</td><td align="left">−0.4</td><td align="left">13.4</td><td align="left">4.8</td><td align="left">3.0</td></tr><tr><td align="left">France</td><td align="left">3.7</td><td align="left">19.1</td><td align="left">11.0</td><td align="left">24.0</td><td align="left">21.4</td><td align="left">−9.7</td><td align="left">−10.5</td><td align="left">3.1</td><td align="left">4.9</td><td align="left">7.7</td><td align="left">9.0</td><td align="left">4.6</td></tr><tr><td align="left">Average</td><td align="left">8.2</td><td align="left">9.9</td><td align="left">13.3</td><td align="left">21.8</td><td align="left">15.1</td><td align="left">−31.4</td><td align="left">−39.3</td><td align="left">−10.7</td><td align="left">−1.3</td><td align="left">7.7</td><td align="left">10.6</td><td align="left">6.7</td></tr></tbody></table><table-wrap-foot><fn><p>Notes: Non-citizen arrivals are limited to non-citizen short-term visitor arrivals. All numbers are from the following equation.<inline-formula><mml:math id="M4" altimg="si4.svg"><mml:mrow><mml:mfrac><mml:mrow><mml:mi>A</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>u</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow><mml:mo linebreak="badbreak">−</mml:mo><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>o</mml:mi><mml:mi>j</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>w</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>o</mml:mi><mml:mi>j</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>w</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow></mml:mrow></mml:mfrac><mml:mo linebreak="goodbreak" linebreakstyle="after">×</mml:mo><mml:mn>100</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math></inline-formula></p></fn></table-wrap-foot></table-wrap></p><p id="p0095"><xref rid="tbl2" ref-type="table">Table 2</xref>shows that the percentage decrease in numbers of arrivals from areas with ≥100 SARS cases during the 2003 SARS outbreak was significantly greater than the decrease from areas with <100 SARS cases in June (52.4% vs. 23.3%), July (60.0% vs. 31.4%), and August 2015 (28.5% vs. 3.8%). In comparison, during the pre-outbreak January to May period, the simple averages from both groups showed higher numbers of arrivals than the averages for 2013 and 2014 (<xref rid="fig1" ref-type="fig">Fig. 1</xref>). The numbers of arrivals from areas with a previous SARS outbreak with ≥100 cases were 21.7%–38.5% higher in January–May 2015 than in January–May averages for 2013 and 2014, while the simple averages from areas with <100 SARS cases were only 1.7%–15.4% higher in January–May 2015 (<xref rid="tbl2" ref-type="table">Table 2</xref>).<table-wrap position="float" id="tbl2"><label>Table 2</label><caption><p>Average monthly percentage changes between 2015 actual and projected (average of 2013 and 2014) non-citizen arrivals to the Republic of Korea (ROK) by whether the country had 100 or more severe acute respiratory syndrome (SARS) cases during the 2003 outbreak.</p></caption><alt-text id="alttext0080">Table 2</alt-text><table frame="hsides" rules="groups"><thead><tr><th rowspan="2">Months</th><th rowspan="2">The 2015 Middle East respiratory syndrome (MERS) outbreak status</th><th colspan="2">SARS ≥100 cases (n = 5)<hr></hr></th><th colspan="2">SARS <100 cases (n = 13)<hr></hr></th><th rowspan="2"><italic>p-value</italic></th></tr><tr><th>%</th><th>SE</th><th>%</th><th>SE</th></tr></thead><tbody><tr><td align="left">Jan</td><td rowspan="4" align="left">Before the MERS outbreak<hr></hr></td><td align="left">21.7</td><td align="left">9.8</td><td align="left">3.0</td><td align="left">4.9</td><td align="left">0.934</td></tr><tr><td align="left">Feb</td><td align="left">31.1</td><td align="left">13.6</td><td align="left">1.7</td><td align="left">4.3</td><td align="left">0.954</td></tr><tr><td align="left">Mar</td><td align="left">28.4</td><td align="left">7.8</td><td align="left">7.5</td><td align="left">5.8</td><td align="left">0.973</td></tr><tr><td align="left">Apr</td><td align="left">38.5</td><td align="left">7.0</td><td align="left">15.4</td><td align="left">5.2</td><td align="left">0.988</td></tr><tr><td colspan="7"><hr></hr></td></tr><tr><td align="left">May</td><td align="left">First MERS case reported May 20, 2015</td><td align="left">30.2</td><td align="left">9.9</td><td align="left">9.2</td><td align="left">3.5</td><td align="left">0.952</td></tr><tr><td colspan="7"><hr></hr></td></tr><tr><td align="left">Jun</td><td rowspan="2" align="left">Peak months of the MERS outbreak<hr></hr></td><td align="left">−52.4</td><td align="left">9.8</td><td align="left">−23.3</td><td align="left">2.9</td><td align="left"><bold>0.018</bold></td></tr><tr><td align="left">Jul<hr></hr></td><td align="left">−60.0</td><td align="left">11.0</td><td align="left">−31.4</td><td align="left">4.7</td><td align="left"><bold>0.026</bold></td></tr><tr><td colspan="7"><hr></hr></td></tr><tr><td align="left">Aug</td><td rowspan="5" align="left">Non-peak months of the MERS outbreak</td><td align="left">−28.5</td><td align="left">7.8</td><td align="left">−3.8</td><td align="left">10.2</td><td align="left"><bold>0.035</bold></td></tr><tr><td align="left">Sep</td><td align="left">6.7</td><td align="left">12.7</td><td align="left">−4.4</td><td align="left">4.7</td><td align="left">0.776</td></tr><tr><td align="left">Oct</td><td align="left">6.5</td><td align="left">10.6</td><td align="left">8.1</td><td align="left">5.1</td><td align="left">0.446</td></tr><tr><td align="left">Nov</td><td align="left">11.4</td><td align="left">7.5</td><td align="left">10.3</td><td align="left">5.7</td><td align="left">0.548</td></tr><tr><td align="left">Dec</td><td align="left">9.5</td><td align="left">8.6</td><td align="left">5.7</td><td align="left">3.3</td><td align="left">0.655</td></tr></tbody></table><table-wrap-foot><fn id="tspara0105"><p>Notes: Non-citizen arrivals are limited to non-citizen short-term visitor arrivals. Monthly percentage changes were calculated by the following equation. <inline-formula><mml:math id="M5" altimg="si4.svg"><mml:mrow><mml:mfrac><mml:mrow><mml:mi>A</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>u</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow><mml:mo linebreak="badbreak">−</mml:mo><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>o</mml:mi><mml:mi>j</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>w</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>o</mml:mi><mml:mi>j</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>w</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow></mml:mrow></mml:mfrac><mml:mo linebreak="goodbreak" linebreakstyle="after">×</mml:mo><mml:mn>100</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math></inline-formula></p></fn><fn id="tspara0110"><p>SE stands for standard error. Boldface indicates statistical significance (<italic>p</italic> < 0.05). Areas in the ≥100 SARS cases category are China, Taiwan–China, Hong Kong SAR–China, Singapore, and Canada.</p></fn></table-wrap-foot></table-wrap><fig id="fig1"><label>Fig. 1</label><caption><p>Monthly percentage changes of non-citizen arrivals to the Republic of Korea (ROK) in 2015 compared with the average of monthly non-citizen arrivals in 2013 and 2014; areas are subdivided by whether each had 100 or more severe acute respiratory syndrome (SARS) cases during the 2003 SARS outbreak.</p><p>Notes: Non-citizen arrivals are limited to non-citizen short-term visitor arrivals. The solid line shows the average percentage changes in monthly numbers of arrivals among areas with ≥100 SARS cases, while the dotted line is the average percentage changes in numbers of arrivals among areas with <100 SARS cases. The shaded area indicates the period between the confirmation of the first Middle East respiratory syndrome (MERS) case in the ROK (May 20, 2015) and the declaration of the end of the outbreak by the government of the ROK and the World Health Organization (December 23, 2015). The shaded dark gray area indicates the peak period of the MERS outbreak, and the shaded light gray area indicates the non-peak period. Note that increases in arrivals (i.e. positive values) before the outbreak reflect the general trend of increasing numbers of arrivals to the ROK.</p></caption><alt-text id="alttext0065">Fig. 1</alt-text><graphic xlink:href="gr1_lrg"></graphic></fig></p><p id="p0100">The percentage changes in arrivals from areas in the close category decreased more than the percentage changes in arrivals in the far category in June (56.1% vs. 19.5%), July (69.9% vs. 17.1%), and August (35.6% vs. 2.9%) (<xref rid="fig2" ref-type="fig">Fig. 2</xref>and <xref rid="tbl3" ref-type="table">Table 3</xref>). In addition, the percentage changes in arrivals in the intermediate category decreased more than the percentage changes in arrivals in the far category in July (44.0% vs. 17.1%). Changes in all other months did not show significant differences across distance categories.<fig id="fig2"><label>Fig. 2</label><caption><p>Monthly average percentage changes between actual and projected non-citizen arrivals to the Republic of Korea (ROK) during 2015 grouped by distance to the ROK.</p><p>Notes: Non-citizen arrivals are limited to non-citizen short-term visitor arrivals. The solid line with circles shows the average monthly percentage changes in numbers of arrivals from areas in the close category, while the dotted line with x is the average percentage changes in numbers of arrivals from areas in the intermediate category. The dotted line with triangles shows the average percentage changes in numbers of visitor arrivals from areas in the far category. The shaded area indicates the period between the confirmation of the first Middle East respiratory syndrome (MERS) case in the ROK (May 20, 2015) and the declaration of the end of the outbreak by the government of the ROK and the World Health Organization (December 23, 2015). The shaded dark gray area indicates the peak period of the MERS outbreak, and the shaded light gray area indicates the non-peak period. Note that increases in arrivals (i.e. positive values) before the outbreak reflect the general trend of increasing numbers of arrivals to the ROK.</p></caption><alt-text id="alttext0070">Fig. 2</alt-text><graphic xlink:href="gr2_lrg"></graphic></fig><table-wrap position="float" id="tbl3"><label>Table 3</label><caption><p>Average monthly percentage changes between actual (2015) and baseline projected (average of 2013 and 2014) non-citizen arrivals to the Republic of Korea (ROK) by distance from the ROK.</p></caption><alt-text id="alttext0085">Table 3</alt-text><table frame="hsides" rules="groups"><thead><tr><th rowspan="2">Months</th><th rowspan="2">The 2015 Middle East respiratory syndrome (MERS) outbreak status</th><th colspan="2">Far (n = 7)<hr></hr></th><th colspan="3">Intermediate (n = 7)<hr></hr></th><th colspan="3">Close (n = 4)<hr></hr></th></tr><tr><th>%</th><th>SE</th><th>%</th><th>SE</th><th><italic>p-value</italic></th><th>%</th><th>SE</th><th><italic>p-value</italic></th></tr></thead><tbody><tr><td align="left">Jan</td><td rowspan="4" align="left">Before the MERS outbreak</td><td align="left">−0.4</td><td align="left">3.4</td><td align="left">12.5</td><td align="left">6.5</td><td align="left">0.946</td><td align="left">15.8</td><td align="left">17.7</td><td align="left">0.787</td></tr><tr><td align="left">Feb</td><td align="left">3.0</td><td align="left">4.6</td><td align="left">7.2</td><td align="left">4.8</td><td align="left">0.730</td><td align="left">26.7</td><td align="left">22.9</td><td align="left">0.811</td></tr><tr><td align="left">Mar</td><td align="left">4.6</td><td align="left">3.9</td><td align="left">16.1</td><td align="left">9.0</td><td align="left">0.863</td><td align="left">23.5</td><td align="left">15.6</td><td align="left">0.843</td></tr><tr><td align="left">Apr</td><td align="left">19.9</td><td align="left">5.0</td><td align="left">18.2</td><td align="left">6.9</td><td align="left">0.422</td><td align="left">31.5</td><td align="left">17.2</td><td align="left">0.723</td></tr><tr><td align="left">May</td><td align="left">First MERS case reported May 20, 2015</td><td align="left">11.9</td><td align="left">4.1</td><td align="left">10.6</td><td align="left">4.1</td><td align="left">0.410</td><td align="left">28.4</td><td align="left">15.9</td><td align="left">0.811</td></tr><tr><td align="left">Jun</td><td rowspan="2" align="left">Peak months of the MERS outbreak</td><td align="left">−19.5</td><td align="left">2.9</td><td align="left">−29.2</td><td align="left">5.9</td><td align="left">0.087</td><td align="left">−56.1</td><td align="left">9.3</td><td align="left"><bold>0.010</bold></td></tr><tr><td align="left">Jul</td><td align="left">−17.1</td><td align="left">2.8</td><td align="left">−44.0</td><td align="left">3.9</td><td align="left"><bold><0.001</bold></td><td align="left">−69.9</td><td align="left">6.7</td><td align="left"><bold><0.001</bold></td></tr><tr><td align="left">Aug</td><td rowspan="5" align="left">Non-peak months of the MERS outbreak</td><td align="left">−2.9</td><td align="left">1.9</td><td align="left">−4.2</td><td align="left">19.3</td><td align="left">0.474</td><td align="left">−35.6</td><td align="left">6.9</td><td align="left"><bold>0.006</bold></td></tr><tr><td align="left">Sep</td><td align="left">−3.4</td><td align="left">2.8</td><td align="left">−4.2</td><td align="left">8.0</td><td align="left">0.463</td><td align="left">7.5</td><td align="left">17.5</td><td align="left">0.711</td></tr><tr><td align="left">Oct</td><td align="left">6.7</td><td align="left">1.6</td><td align="left">8.8</td><td align="left">9.8</td><td align="left">0.578</td><td align="left">7.4</td><td align="left">13.6</td><td align="left">0.517</td></tr><tr><td align="left">Nov</td><td align="left">6.9</td><td align="left">1.6</td><td align="left">15.1</td><td align="left">10.1</td><td align="left">0.774</td><td align="left">9.2</td><td align="left">11.6</td><td align="left">0.573</td></tr><tr><td align="left">Dec</td><td align="left">6.1</td><td align="left">1.1</td><td align="left">5.4</td><td align="left">6.5</td><td align="left">0.463</td><td align="left">10.2</td><td align="left">10.7</td><td align="left">0.637</td></tr></tbody></table><table-wrap-foot><fn id="tspara0120"><p>Notes: Non-citizen arrivals are limited to non-citizen short-term visitor arrivals. SE stands for standard error. Boldface indicates statistical significance (<italic>p-values</italic> < 0.05). The <italic>p-values</italic> are from Welch's t-tests for two samples with unequal variances. The reference group is far. The <italic>p-values</italic> in the intermediate column shows <italic>p-values</italic> to evaluate whether differences between the far and intermediate groups are significant, while <italic>p-values</italic> in the close column shows <italic>p-values</italic> to evaluate whether differences between the far and close groups are significant.</p></fn><fn id="tspara0125"><p>Close: China, Japan, Taiwan–China, and Hong Kong Special Administrative Region (SAR)–China; Intermediate: Thailand, Philippines, Malaysia, Indonesia, Singapore, India, and Vietnam; Far: USA, Russia, Canada, Australia, UK, Germany, and France.</p></fn></table-wrap-foot></table-wrap></p><p id="p0105">The results from baseline OLS regression models also identified statistically significant correlations between the percentage decrease of numbers of arrivals and previous SARS outbreak of ≥100 cases (<xref rid="tbl4" ref-type="table">Table 4</xref>). Areas with a previous SARS outbreak of ≥100 cases showed significant correlations in June (<italic>p</italic>-value = 0.032). Also, the correlation between the percentage decrease of numbers of arrivals and distance from the ROK was statistically significant. The significant percentage decreases in numbers of visitor arrivals occurred in areas in the close category in June (<italic>p</italic>-value = 0.012) and July (<italic>p</italic>-value≤ 0.001), and in the intermediate category in July (<italic>p</italic>-value≤ 0.001).<table-wrap position="float" id="tbl4"><label>Table 4</label><caption><p>Monthly marginal percentage change between actual (2015) and baseline projected (average of 2013 and 2014) non-citizen arrivals to the Republic of Korea (ROK) associated with areas that experienced ≥100 probable severe acute respiratory syndrome (SARS) cases and distance to the Republic of Korea.</p></caption><alt-text id="alttext0090">Table 4</alt-text><table frame="hsides" rules="groups"><thead><tr><th>Months</th><th colspan="2">Independent variables</th><th>Coef.</th><th><italic>p-value</italic></th><th>R-squared</th></tr></thead><tbody><tr><td rowspan="3" align="left">Jun<hr></hr></td><td align="left">2003 SARS outbreak size (cases)</td><td align="left">SARS ≥100</td><td align="left">−18.3</td><td align="left"><bold>0.032</bold></td><td rowspan="3" align="left">0.68<hr></hr></td></tr><tr><td rowspan="2" align="left">Distance<hr></hr></td><td align="left">Close</td><td align="left">−25.5</td><td align="left"><bold>0.012</bold></td></tr><tr><td align="left">Intermediate<hr></hr></td><td align="left">−9.7<hr></hr></td><td align="left">0.155<hr></hr></td></tr><tr><td rowspan="3" align="left">Jul<hr></hr></td><td align="left">2003 SARS outbreak size (cases)</td><td align="left">SARS ≥100</td><td align="left">−11.8</td><td align="left">0.058</td><td rowspan="3" align="left">0.87<hr></hr></td></tr><tr><td rowspan="2" align="left">Distance<hr></hr></td><td align="left">Close</td><td align="left">−45.6</td><td align="left"><bold><0.001</bold></td></tr><tr><td align="left">Intermediate<hr></hr></td><td align="left">−26.8<hr></hr></td><td align="left"><bold><0.001</bold><hr></hr></td></tr><tr><td rowspan="3" align="left">Aug<hr></hr></td><td align="left">2003 SARS outbreak size (cases)</td><td align="left">SARS ≥100</td><td align="left">−11.7</td><td align="left">0.595</td><td rowspan="3" align="left">0.41<hr></hr></td></tr><tr><td rowspan="2" align="left">Distance<hr></hr></td><td align="left">Close</td><td align="left">−25.5</td><td align="left">0.323</td></tr><tr><td align="left">Intermediate<hr></hr></td><td align="left">−1.3<hr></hr></td><td align="left">0.944<hr></hr></td></tr><tr><td rowspan="3" align="left">Sep<hr></hr></td><td align="left">2003 SARS outbreak size (cases)</td><td align="left">SARS ≥100</td><td align="left">7.5</td><td align="left">0.593</td><td rowspan="3" align="left">0.08<hr></hr></td></tr><tr><td rowspan="2" align="left">Distance<hr></hr></td><td align="left">Close</td><td align="left">6.4</td><td align="left">0.695</td></tr><tr><td align="left">Intermediate<hr></hr></td><td align="left">−0.8<hr></hr></td><td align="left">0.945<hr></hr></td></tr><tr><td rowspan="3" align="left">Oct<hr></hr></td><td align="left">2003 SARS outbreak size (cases)</td><td align="left">SARS ≥100</td><td align="left">−2.2</td><td align="left">0.876</td><td rowspan="3" align="left"><0.01<hr></hr></td></tr><tr><td rowspan="2" align="left">Distance<hr></hr></td><td align="left">Close</td><td align="left">2.0</td><td align="left">0.903</td></tr><tr><td align="left">Intermediate<hr></hr></td><td align="left">2.0<hr></hr></td><td align="left">0.861<hr></hr></td></tr><tr><td rowspan="3" align="left">Nov<hr></hr></td><td align="left">2003 SARS outbreak size (cases)</td><td align="left">SARS ≥100</td><td align="left">3.1</td><td align="left">0.820</td><td rowspan="3" align="left">0.04<hr></hr></td></tr><tr><td rowspan="2" align="left">Distance<hr></hr></td><td align="left">Close</td><td align="left">0.5</td><td align="left">0.975</td></tr><tr><td align="left">Intermediate<hr></hr></td><td align="left">8.2<hr></hr></td><td align="left">0.470<hr></hr></td></tr><tr><td rowspan="3" align="left">Dec</td><td align="left">2003 SARS outbreak size (cases)</td><td align="left">SARS ≥100</td><td align="left">2.2</td><td align="left">0.819</td><td rowspan="3" align="left">0.02</td></tr><tr><td rowspan="2" align="left">Distance</td><td align="left">Close</td><td align="left">2.8</td><td align="left">0.807</td></tr><tr><td align="left">Intermediate</td><td align="left">−0.6</td><td align="left">0.937</td></tr></tbody></table><table-wrap-foot><fn><p>Notes: Non-citizen arrivals are limited to non-citizen short-term visitor arrivals. Boldface indicates statistical significance (<italic>p</italic> < 0.05). The results were from baseline ordinary least squares (OLS) regression models using percentage changes between 2015 actual and the average of 2013 and 2014 monthly arrivals as dependent variables. The reference category for the 2003 SARS outbreak areas with <100 probable SARS cases, and the reference for distance is the far category.</p></fn></table-wrap-foot></table-wrap></p><p id="p0110">Results from sensitivity analyses were consistent with the baseline analysis. For Baseline and Alternatives 1, the percentage changes of arrivals from areas with a previous SARS outbreak of ≥100 cases showed greater decreases than the percentage changes in arrivals from their counterparts in June, July, and August 2015 (<xref rid="dtbl4" ref-type="table">Appendix Table 4</xref>). For Alternative 2, the differences between areas with and without a SARS outbreak of ≥100 cases were significant in every month from June through December 2015, except for September.</p><p id="p0115">For both Alternatives, declines in numbers of arrivals from areas in the close category were larger than for areas in the far category from June to August 2015 (<xref rid="dtbl5" ref-type="table">Appendix Table 5</xref>). For Alternative 1, the declines in arrivals in the intermediate category were significantly larger than the declines in arrivals from the far category from April through December 2015, except for August. However, if we instead used Alternative 2, the decline in numbers of arrivals from the intermediate distance category was statistically significantly greater than the far category only in April, June, and July 2015.</p><p id="p0120">The results from sensitivity analyses of regression models were also consistent with the results from baseline models. Using all three projected values, the percentage decrease of arrivals in June and July showed some significant correlations with proximity and previous SARS outbreaks of ≥100 cases (<xref rid="dtbl6" ref-type="table">Appendix Table 6</xref>). When excluding outliers, the observation for India in August 2015, the difference in the percentage changes in arrivals between the intermediate and far categories was significant; however, when the India, August 2015 observation was included in the analysis (i.e. not excluded), the difference between the intermediate and far categories was no longer significant (<xref rid="dtbl7" ref-type="table">Appendix Table 7</xref>).</p></sec><sec id="sec4"><label>4</label><title>Discussion</title><p id="p0125">Results from the baseline and sensitivity analyses found that the 2015 MERS outbreak in the ROK appeared to have a greater effect on travel volumes from areas with ≥100 SARS cases in 2003, especially in June and July 2015. Most of the ROK MERS cases were confirmed during those 2 months, and the ROK government declared a de facto end to the outbreak on July 28, 2015 by which time no individuals were quarantined because of MERS, nor were there any newly confirmed cases [<xref rid="bib2" ref-type="bibr">2</xref>]. The decrease of arrivals during the 2015 MERS outbreak is underscored when we compare the trends of numbers of arrivals before and during the 2015 MERS outbreak. Positive values in <xref rid="tbl1" ref-type="table">Table 1</xref>, <xref rid="tbl2" ref-type="table">Table 2</xref>, <xref rid="tbl3" ref-type="table">Table 3</xref> (i.e. increases in arrivals) before the 2015 MERS outbreak reflect the general trend that the number of arrivals to the ROK would be increasing without the outbreak.</p><p id="p0130">Visitors from areas with a previous SARS outbreak of ≥100 cases may have been more likely to change their travel plans because of similarities between SARS and MERS, which are both caused by coronaviruses and capable of causing serious respiratory impairment. In addition, the governments in areas that experienced previous SARS outbreaks may have had stronger restrictions or recommendations about travel to the ROK during the MERS outbreak period. Although WHO did not recommend any travel restrictions to the ROK during the 2015 MERS outbreak [<xref rid="bib17" ref-type="bibr">17</xref>], WHO noted that raising awareness about MERS among those traveling to affected areas was good public health practice [<xref rid="bib18" ref-type="bibr">18</xref>]. Some areas issued travel notices to inform travelers about the potential risks and risk mitigation strategies. Although the intent of notices was not to restrict travel, such notices may have raised additional concern among travelers, who may then have decided to cancel their travel plans.</p><p id="p0135">For instance, Hong Kong SAR, which showed the biggest percentage difference between projected and actual arrivals to the ROK, had experienced a sizable SARS outbreak in 2003 with 1755 cases [<xref rid="bib9" ref-type="bibr">9</xref>]. Hong Kong SAR issued a Red Outbound Travel Alert (OTA) for the ROK on June 9, 2015 [<xref rid="bib19" ref-type="bibr">19</xref>]. Hong Kong's Red OTA recommends to adjust travel plans or avoid non-essential travel and is the second highest level travel notice [<xref rid="bib20" ref-type="bibr">20</xref>]. The alert was lifted on August 1, 2015 [<xref rid="bib21" ref-type="bibr">21</xref>]. In addition, the Travel Industry Council of Hong Kong canceled all tours, excluding cruises, to the ROK that were scheduled to depart from June 9 to the end of June in 2015 [<xref rid="bib22" ref-type="bibr">22</xref>]. Since only about 2% of travelers from Hong Kong SAR to the ROK arrive via cruise [<xref rid="bib7" ref-type="bibr">7</xref>], most tourists from Hong Kong SAR were affected by the cancellation. In addition, some local governments in mainland China (Sichuan and Shandong provinces, and Guangzhou, the capital of Guangdong province) issued travel notices to the ROK because of the 2015 MERS outbreak [<xref rid="bib23" ref-type="bibr">23</xref>]. Also, some flights between China and Jeju international airport in the ROK were cancelled [<xref rid="bib24" ref-type="bibr">24</xref>].</p><p id="p0140">Taiwan also experienced a sizable SARS outbreak in 2003, and the Taiwan Centers for Disease Control (CDC) issued a level 2 travel notice (Alert) for Seoul and a level 1 travel notice (Watch) for all other areas in the ROK on June 2, 2015 [<xref rid="bib25" ref-type="bibr">25</xref>]. The level 2 travel notice was extended to the entire ROK on June 9, 2015 [<xref rid="bib26" ref-type="bibr">26</xref>]. The notice included a recommendation that travelers avoid unnecessary hospital visits in the ROK [<xref rid="bib26" ref-type="bibr">26</xref>]. The Taiwan CDC lowered its travel notice to level 1 on July 7, 2015 [<xref rid="bib27" ref-type="bibr">27</xref>], then removed the notice on July 28, 2015 [<xref rid="bib28" ref-type="bibr">28</xref>]. The Taiwan Ministry of Foreign Affairs also issued a travel alert for MERS to the ROK [<xref rid="bib29" ref-type="bibr">29</xref>].</p><p id="p0145">The experience with SARS outbreaks in 2003 is only one potential reason that governments with previous SARS outbreak experiences may have issued stronger restrictions or recommendations about travel to the ROK during the MERS outbreak than governments without previous SARS outbreak experiences. For example, a Korean traveler who was experiencing symptoms consistent with MERS pushed ahead with his plan to travel to Guangdong, China with a layover in Hong Kong SAR [<xref rid="bib17" ref-type="bibr">17</xref>]. When this traveler was diagnosed with MERS after arrival in China, a public health response was required in China [<xref rid="bib2" ref-type="bibr">2</xref>]. Although there was no further MERS transmission in China or Hong Kong SAR, public awareness of this imported case may have exacerbated fear among individuals in Hong Kong SAR and Guangdong as well as their local governments. In Taiwan, a patient with suspected MERS was reported on May 30, 2015, although this patient was later confirmed to have influenza B virus infection [<xref rid="bib30" ref-type="bibr">30</xref>]. This suspected case was one of 17 suspected MERS cases reported in Taiwan between September 2012 and May 2015 [<xref rid="bib30" ref-type="bibr">30</xref>]. The 17th suspected case was in a person who was neither a ROK citizen nor had visited the ROK [<xref rid="bib30" ref-type="bibr">30</xref>]. However, the patient could have increased public awareness of MERS in Taiwan. Although countries issued varied levels of travel notices, no countries enforced travel bans or strict quarantine requirements for travelers from the ROK as some did for travelers from countries with Ebola during the West African epidemic [<xref rid="bib31" ref-type="bibr">31</xref>].</p><p id="p0150">Since the 2015 MERS outbreak ended, the ROK government has continuously made efforts to prevent future MERS outbreaks in the ROK. The Korea Centers for Disease Control & Prevention updated its national prevention and control guidelines for MERS in April 2016 [<xref rid="bib32" ref-type="bibr">32</xref>]. These guidelines include response plans at airports, hospitals, and local health departments to expedite the identification of individuals suspected of MERS-CoV infection after arriving from MERS-affected areas [<xref rid="bib32" ref-type="bibr">32</xref>]. Following the guidelines, virological testing for MERS-CoV and other respiratory viruses has been implemented to confirm whether individuals with recent travel to MERS-affected areas and symptoms consistent with MERS are infected with MERS-CoV or not [<xref rid="bib32" ref-type="bibr">32</xref>,<xref rid="bib33" ref-type="bibr">33</xref>]. Also, the ROK's amended quarantine act specifies MERS as a quarantinable infectious disease [<xref rid="bib34" ref-type="bibr">34</xref>]. These efforts may decrease the risk of future MERS outbreaks in the ROK.</p><p id="p0155">This analysis has some limitations. First, we assumed that the nationality of an individual was a proxy for the area of departure for that individual. For instance, a Japanese national may live in the United States before visiting the ROK but would be counted as if traveling from Japan (close category) for our analysis. Such discrepancies between citizenship and residency might cause some bias in examining the effect of distance on travel volumes to the ROK during the MERS outbreak.</p><p id="p0160">Next, we were limited in our ability to assess the mechanism by which a previous sizable outbreak experience would affect travel to the ROK. As we discussed, issuance of travel notices may correlate with past outbreak experience in that governments in countries or localities that previously experienced outbreaks may be more likely to issue stronger notices. In addition, stronger travel notices in some countries may have correlated with greater decreases in travel volumes during the MERS outbreak in the ROK. However, we were unable to create categorical variables for issuance of travel notices because each country has its own process and terminology for travel notices. Although many countries have similar structures for level of travel notices, they are not easily comparable. In addition to level, each travel notice contains outbreak-specific recommendations. Recommendations, such as avoiding unnecessary hospital visits while in the ROK, further complicate comparisons of the intensity of travel health notices among countries. Thus, we were unable to quantify differences between stronger and weaker travel notices across countries. Also, there could be other country- or area-specific factors, which could affect travel volumes during a disease outbreak, but were not considered in the current analyses.</p><p id="p0165">Last, we chose only one previous outbreak, the 2003 SARS outbreak as an indicator for previous outbreak experiences. While SARS was also caused by a novel coronavirus resulting in respiratory disease similar to MERS, exposure to other sizable outbreaks, such as the 2009 H1N1 pandemic or the 2013–2015 Ebola epidemic, may also have affected governments' and individuals' perceptions of risk from the 2015 MERS outbreak. Although there was a large difference between the top five areas with at least 238 probable SARS cases for each and the next highest country with 63 probable SARS cases during the 2013 outbreak (<xref rid="appsec3" ref-type="sec">Appendix Figure 3</xref>), a single threshold of ≥100 SARS cases were used. Also, a threshold of ≥100 SARS cases was used without considering other potentially critical factors, such as population size, the extent of geographic area, the time duration of the SARS outbreak, or extent of unlinked community transmission. For instance, both Canada and Singapore had ≥100 SARS cases in 2003. However, more than 95% of probable SARS cases in Canada (n = 247) were observed in the greater Toronto area [<xref rid="bib35" ref-type="bibr">35</xref>], a small part of Canada, while the SARS outbreak in Singapore (n = 238) was over the entire country [<xref rid="bib9" ref-type="bibr">9</xref>]. Thus, Canadian governments' and individuals’ perceptions of risk from the 2015 MERS outbreak associated with their SARS outbreak experience could be different from the perception of risk from the 2015 MERS outbreak in Singapore.</p></sec><sec id="sec5"><label>5</label><title>Conclusions</title><p id="p0170">During the 2015 MERS outbreak, the numbers of arrivals to the ROK from around the world decreased. Countries with a previous sizable SARS outbreak and countries closer to the ROK had the greatest percentage decreases in travel volumes to the ROK. This is the first analysis that has examined area-specific impacts on travel volume to an outbreak area as we know. Future research using different outbreak situations may improve our understanding of the effects of outbreaks on travel volume.</p></sec><sec id="sec6"><title>Disclaimer</title><p id="p0175">The findings and conclusions of this article are those of the authors and do not necessarily represent the official position of the United States Centers for Disease Control and Prevention or the Korea Centers for Disease Control & Prevention.</p></sec></body><back><ref-list id="cebib0010"><title>References</title><ref id="bib1"><label>1</label><element-citation publication-type="journal" id="sref1"><person-group person-group-type="author"><name><surname>Korea Centers for Disease Control and Prevention</surname></name></person-group><article-title>Middle East respiratory syndrome coronavirus outbreak in the Republic of Korea, 2015</article-title><source>Osong Public Health Res Perspect</source><volume>6</volume><issue>4</issue><year>2015</year><fpage>269</fpage><lpage>278</lpage><pub-id pub-id-type="pmid">26473095</pub-id></element-citation></ref><ref id="bib2"><label>2</label><element-citation publication-type="book" id="sref2"><person-group person-group-type="author"><name><surname>Korean Ministry of Health and Welfare</surname></name></person-group><chapter-title>The 2015 MERS outbreak in the Republic of Korea: learning from MERS</chapter-title><year>2016</year><comment>Available from:</comment><ext-link ext-link-type="uri" xlink:href="http://www.mohw.go.kr/react/gm/sgm0601vw.jsp?PAR_MENU_ID=13&MENU_ID=1304021002&CONT_SEQ=337684" id="intref0025">http://www.mohw.go.kr/react/gm/sgm0601vw.jsp?PAR_MENU_ID=13&MENU_ID=1304021002&CONT_SEQ=337684</ext-link></element-citation></ref><ref id="bib3"><label>3</label><element-citation publication-type="book" id="sref3"><person-group person-group-type="author"><name><surname>World Health Organization</surname></name></person-group><chapter-title>Middle East respiratory syndrome coronavirus (MERS-CoV) Fact Sheet. 2015 June</chapter-title><year>2015</year><comment>[cited 2017 Feb. 27]; 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person-group-type="author"><name><surname>Skowronski</surname><given-names>D.M.</given-names></name><name><surname>Petric</surname><given-names>M.</given-names></name><name><surname>Daly</surname><given-names>P.</given-names></name><name><surname>Parker</surname><given-names>R.A.</given-names></name><name><surname>Bryce</surname><given-names>E.</given-names></name><name><surname>Doyle</surname><given-names>P.W.</given-names></name><name><surname>Noble</surname><given-names>M.A.</given-names></name><name><surname>Roscoe</surname><given-names>D.L.</given-names></name><name><surname>Tomblin</surname><given-names>J.</given-names></name><name><surname>Yang</surname><given-names>T.C.</given-names></name><name><surname>Krajden</surname><given-names>M.</given-names></name><name><surname>Patrick</surname><given-names>D.M.</given-names></name><name><surname>Pourbohloul</surname><given-names>B.</given-names></name><name><surname>Goh</surname><given-names>S.H.</given-names></name><name><surname>Bowie</surname><given-names>W.R.</given-names></name><name><surname>Booth</surname><given-names>T.F.</given-names></name><name><surname>Tweed</surname><given-names>S.A.</given-names></name><name><surname>Perry</surname><given-names>T.L.</given-names></name><name><surname>McGeer</surname><given-names>A.</given-names></name><name><surname>Brunham</surname><given-names>R.C.</given-names></name></person-group><article-title>Coordinated response to SARS, Vancouver, Canada</article-title><source>Emerg Infect Dis</source><volume>12</volume><issue>1</issue><year>2006</year><fpage>155</fpage><lpage>158</lpage><pub-id pub-id-type="pmid">16494736</pub-id></element-citation></ref></ref-list><sec id="appsec3"><label>Appendix B</label><p id="p0190"><fig id="dfig1" position="anchor"><label>Appendix Fig. 1</label><caption><p>Percentage changes between 2015 actual and baseline projected monthly non-citizen arrivals to the Republic of Korea by area (%).</p><p>Notes: Non-citizen arrivals refers to non-citizen short-term visitor arrivals. Projected values are estimated from the average of 2013 and 2014 monthly average numbers of non-citizen arrivals. Percentage changes in numbers of non-citizen arrivals are calculated by the following equation.<inline-formula><mml:math id="M6" altimg="si4.svg"><mml:mrow><mml:mfrac><mml:mrow><mml:mi>A</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>u</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow><mml:mo linebreak="badbreak">−</mml:mo><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>o</mml:mi><mml:mi>j</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>w</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>o</mml:mi><mml:mi>j</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>w</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow></mml:mrow></mml:mfrac><mml:mo linebreak="goodbreak" linebreakstyle="after">×</mml:mo><mml:mn>100</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math></inline-formula></p></caption><alt-text id="alttext0015">Appendix Fig. 1</alt-text><graphic xlink:href="fx1_lrg"></graphic></fig><fig id="dfig2" position="anchor"><label>Appendix Fig. 2</label><caption><p>Maps of percentage changes between 2015 actual and 2015 baseline projected monthly non-citizen arrivals to the Republic of Korea by area (%).</p><p>Notes: Non-citizen arrivals are limited to non-citizen short-term visitor arrivals. Projected values are from the average of 2013 and 2014 monthly averages of non-citizen arrivals. Percentage changes in numbers of non-citizen arrivals are calculated using the following equation: <inline-formula><mml:math id="M7" altimg="si4.svg"><mml:mrow><mml:mfrac><mml:mrow><mml:mi>A</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>u</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow><mml:mo linebreak="badbreak">−</mml:mo><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>o</mml:mi><mml:mi>j</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>w</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>o</mml:mi><mml:mi>j</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mrow><mml:mo stretchy="true">(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>w</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>M</mml:mi><mml:mi>E</mml:mi><mml:mi>R</mml:mi><mml:mi>S</mml:mi><mml:mspace width="0.25em"></mml:mspace><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>b</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo stretchy="true">)</mml:mo></mml:mrow></mml:mrow></mml:mfrac><mml:mo linebreak="goodbreak" linebreakstyle="after">×</mml:mo><mml:mn>100</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math></inline-formula>Areas colored green represent areas with positive percentage changes from the index. In other words, the actual non-citizen arrivals in 2015 are bigger than the projected values using the monthly average of 2013 and 2014 for the green areas. The red-colored areas indicate that the actual non-citizen arrivals values in 2015 are smaller than the projected values.</p></caption><alt-text id="alttext0020">Appendix Fig. 2</alt-text><graphic xlink:href="fx2a_lrg"></graphic><graphic xlink:href="fx2b_lrg"></graphic><graphic xlink:href="fx2c_lrg"></graphic></fig><fig id="dfig3" position="anchor"><label>Appendix Fig. 3</label><caption><p>Numbers of probable severe acute respiratory syndrome (SARS) cases by area (November 1, 2002–July 31, 2003).</p><p>Notes: Numbers of probable SARS cases by area are from the World Health Organization (<ext-link ext-link-type="uri" xlink:href="https://www.who.int/csr/sars/country/table2004_04_21/en/" id="intref0015">https://www.who.int/csr/sars/country/table2004_04_21/en/</ext-link>).</p></caption><alt-text id="alttext0025">Appendix Fig. 3</alt-text><graphic xlink:href="fx3_lrg"></graphic></fig><table-wrap position="anchor" id="dtbl1"><label>Appendix Table 1</label><caption><p>Actual numbers of monthly non-citizen arrivals in the Republic of Korea in 2015 by area</p></caption><alt-text id="alttext0030">Appendix Table 1</alt-text><table frame="hsides" rules="groups"><thead><tr><th>Month</th><th>China</th><th>Japan</th><th>USA</th><th>Taiwan–China</th><th>Hong Kong SAR–China</th><th>Thailand</th><th>Philippines</th><th>Malaysia</th><th>Russia</th><th>Indonesia</th></tr></thead><tbody><tr><td align="left">Jan</td><td align="left">394 345</td><td align="left">139 632</td><td align="left">50 896</td><td align="left">49 798</td><td align="left">35 407</td><td align="left">40 880</td><td align="left">24 716</td><td align="left">12 396</td><td align="left">13 957</td><td align="left">12 120</td></tr><tr><td align="left">Feb</td><td align="left">516 787</td><td align="left">142 587</td><td align="left">48 728</td><td align="left">53 719</td><td align="left">50 992</td><td align="left">34 861</td><td align="left">23 740</td><td align="left">14 213</td><td align="left">11 734</td><td align="left">12 092</td></tr><tr><td align="left">Mar</td><td align="left">515 130</td><td align="left">218 932</td><td align="left">66 755</td><td align="left">52 304</td><td align="left">51 876</td><td align="left">46 868</td><td align="left">33 763</td><td align="left">21 493</td><td align="left">15 915</td><td align="left">15 667</td></tr><tr><td align="left">Apr</td><td align="left">641 610</td><td align="left">154 402</td><td align="left">80 120</td><td align="left">60 752</td><td align="left">67 465</td><td align="left">43 851</td><td align="left">45 128</td><td align="left">28 330</td><td align="left">17 831</td><td align="left">21 092</td></tr><tr><td align="left">May</td><td align="left">618 083</td><td align="left">188 420</td><td align="left">70 891</td><td align="left">59 061</td><td align="left">54 095</td><td align="left">35 020</td><td align="left">41 141</td><td align="left">19 463</td><td align="left">18 663</td><td align="left">20 136</td></tr><tr><td align="left">Jun</td><td align="left">315 095</td><td align="left">101 206</td><td align="left">62 550</td><td align="left">14 816</td><td align="left">13 949</td><td align="left">16 749</td><td align="left">31 427</td><td align="left">10 283</td><td align="left">14 368</td><td align="left">14 881</td></tr><tr><td align="left">Jul</td><td align="left">255 632</td><td align="left">81 748</td><td align="left">62 627</td><td align="left">9879</td><td align="left">7761</td><td align="left">12 130</td><td align="left">23 529</td><td align="left">5893</td><td align="left">13 524</td><td align="left">10 418</td></tr><tr><td align="left">Aug</td><td align="left">513 275</td><td align="left">148 632</td><td align="left">63 522</td><td align="left">25 622</td><td align="left">33 752</td><td align="left">13 634</td><td align="left">32 801</td><td align="left">9709</td><td align="left">15 936</td><td align="left">14 555</td></tr><tr><td align="left">Sep</td><td align="left">591 242</td><td align="left">157 313</td><td align="left">63 793</td><td align="left">46 353</td><td align="left">52 400</td><td align="left">15 907</td><td align="left">36 605</td><td align="left">17 570</td><td align="left">15 904</td><td align="left">15 393</td></tr><tr><td align="left">Oct</td><td align="left">650 174</td><td align="left">180 348</td><td align="left">77 878</td><td align="left">48 929</td><td align="left">53 648</td><td align="left">36 102</td><td align="left">43 018</td><td align="left">23 767</td><td align="left">17 638</td><td align="left">20 166</td></tr><tr><td align="left">Nov</td><td align="left">507 579</td><td align="left">164 685</td><td align="left">62 273</td><td align="left">47 463</td><td align="left">47 329</td><td align="left">31 627</td><td align="left">34 745</td><td align="left">28 090</td><td align="left">16 472</td><td align="left">16 513</td></tr><tr><td align="left">Dec</td><td align="left">465 218</td><td align="left">159 877</td><td align="left">57 580</td><td align="left">49 494</td><td align="left">54 753</td><td align="left">44 140</td><td align="left">33 009</td><td align="left">32 143</td><td align="left">16 164</td><td align="left">20 557</td></tr></tbody></table><table frame="hsides" rules="groups"><thead><tr><th>Month</th><th>Singapore</th><th>India</th><th>Canada</th><th>Vietnam</th><th>Australia</th><th>UK</th><th>Germany</th><th>France</th></tr></thead><tbody><tr><td align="left">Jan</td><td align="left">8934</td><td align="left">7228</td><td align="left">9898</td><td align="left">7895</td><td align="left">9997</td><td align="left">9715</td><td align="left">6316</td><td align="left">5282</td></tr><tr><td align="left">Feb</td><td align="left">8166</td><td align="left">7983</td><td align="left">10 537</td><td align="left">9809</td><td align="left">7010</td><td align="left">8051</td><td align="left">6046</td><td align="left">5978</td></tr><tr><td align="left">Mar</td><td align="left">15 541</td><td align="left">10 206</td><td align="left">14 085</td><td align="left">18 309</td><td align="left">11 005</td><td align="left">12 866</td><td align="left">10 123</td><td align="left">6898</td></tr><tr><td align="left">Apr</td><td align="left">18 331</td><td align="left">11 977</td><td align="left">16 447</td><td align="left">18 937</td><td align="left">15 526</td><td align="left">14 556</td><td align="left">9371</td><td align="left">8816</td></tr><tr><td align="left">May</td><td align="left">16 747</td><td align="left">14 160</td><td align="left">14 541</td><td align="left">14 485</td><td align="left">14 409</td><td align="left">10 512</td><td align="left">8770</td><td align="left">8171</td></tr><tr><td align="left">Jun</td><td align="left">7926</td><td align="left">11 903</td><td align="left">8909</td><td align="left">10 840</td><td align="left">8504</td><td align="left">7632</td><td align="left">6922</td><td align="left">5744</td></tr><tr><td align="left">Jul</td><td align="left">4439</td><td align="left">8551</td><td align="left">9496</td><td align="left">9172</td><td align="left">9017</td><td align="left">7921</td><td align="left">7008</td><td align="left">6467</td></tr><tr><td align="left">Aug</td><td align="left">6972</td><td align="left">31 461</td><td align="left">11 881</td><td align="left">12 453</td><td align="left">8458</td><td align="left">10 223</td><td align="left">8787</td><td align="left">8678</td></tr><tr><td align="left">Sep</td><td align="left">11 867</td><td align="left">14 150</td><td align="left">11 870</td><td align="left">14 052</td><td align="left">12 273</td><td align="left">10 163</td><td align="left">9525</td><td align="left">6889</td></tr><tr><td align="left">Oct</td><td align="left">15 672</td><td align="left">15 476</td><td align="left">14 505</td><td align="left">18 740</td><td align="left">14 002</td><td align="left">13 345</td><td align="left">12 248</td><td align="left">8620</td></tr><tr><td align="left">Nov</td><td align="left">18 224</td><td align="left">11 644</td><td align="left">12 345</td><td align="left">16 257</td><td align="left">9759</td><td align="left">10 346</td><td align="left">8953</td><td align="left">6784</td></tr><tr><td align="left">Dec</td><td align="left">27 334</td><td align="left">8863</td><td align="left">11 033</td><td align="left">11 816</td><td align="left">13 306</td><td align="left">7944</td><td align="left">6113</td><td align="left">5505</td></tr></tbody></table><table-wrap-foot><fn id="tspara0015"><p>Note: Non-citizen arrivals refers to non-citizen short-term visitor arrivals.</p></fn><fn id="tspara0020"><p>Data source: TOURGO (Tourism Knowledge and Information System). <italic>Tourism arrival statistics</italic>. [cited 2017 Aug. 24]; Available from: <ext-link ext-link-type="uri" xlink:href="http://know.tour.go.kr/stat/tourStatSearchDis.do;jsessionid=8B29C231492BA57E875081E0A6D4C762" id="intref0020">http://know.tour.go.kr/stat/tourStatSearchDis.do;jsessionid=8B29C231492BA57E875081E0A6D4C762</ext-link>.</p></fn></table-wrap-foot></table-wrap><table-wrap position="anchor" id="dtbl2"><label>Appendix Table 2</label><caption><p>Categorization of areas based on distance and flight time from capital cities to Seoul (Republic of Korea), and flight frequency</p></caption><alt-text id="alttext0035">Appendix Table 2</alt-text><table frame="hsides" rules="groups"><thead><tr><th>Category</th><th>Country or political unit</th><th>Capital</th><th>Distance (km)</th><th>Flight time to Seoul</th><th>Numbers of direct flight per day</th></tr></thead><tbody><tr><td rowspan="4" align="left">Close</td><td align="left">China</td><td align="left">Beijing</td><td align="left">952</td><td align="left">2hr 5min</td><td align="left">12</td></tr><tr><td align="left">Japan</td><td align="left">Tokyo</td><td align="left">1152</td><td align="left">2hr 30min</td><td align="left">31</td></tr><tr><td align="left">Taiwan</td><td align="left">Taipei</td><td align="left">1484</td><td align="left">2hr 30min</td><td align="left">15</td></tr><tr><td align="left">Hong Kong SAR</td><td align="left">Hong Kong</td><td align="left">2087</td><td align="left">3hr 40min</td><td align="left">23</td></tr><tr><td rowspan="7" align="left">Intermediate</td><td align="left">Philippines</td><td align="left">Manila</td><td align="left">2620</td><td align="left">4hr 5min</td><td align="left">9</td></tr><tr><td align="left">Vietnam</td><td align="left">Hanoi</td><td align="left">2740</td><td align="left">4hr 15min</td><td align="left">12</td></tr><tr><td align="left">Thailand</td><td align="left">Bangkok</td><td align="left">3720</td><td align="left">5hr 30min</td><td align="left">15</td></tr><tr><td align="left">Malaysia</td><td align="left">Kuala Lumpur</td><td align="left">4611</td><td align="left">6hr 30min</td><td align="left">4</td></tr><tr><td align="left">Singapore</td><td align="left">Singapore</td><td align="left">4669</td><td align="left">6hr 30min</td><td align="left">7</td></tr><tr><td align="left">India</td><td align="left">New Delhi</td><td align="left">4684</td><td align="left">6hr 50min</td><td align="left">1</td></tr><tr><td align="left">Indonesia</td><td align="left">Jakarta</td><td align="left">5289</td><td align="left">7hr 10min</td><td align="left">3</td></tr><tr><td rowspan="7" align="left">Far</td><td align="left">Russia</td><td align="left">Moscow</td><td align="left">6606</td><td align="left">8hr 30min</td><td align="left">1</td></tr><tr><td align="left">Germany</td><td align="left">Berlin</td><td align="left">8122</td><td align="left">12hr 5min</td><td align="left">0</td></tr><tr><td align="left">UK</td><td align="left">London</td><td align="left">8852</td><td align="left">11hr</td><td align="left">3</td></tr><tr><td align="left">France</td><td align="left">Paris</td><td align="left">8960</td><td align="left">11hr</td><td align="left">2</td></tr><tr><td align="left">Australia</td><td align="left">Canberra</td><td align="left">8411</td><td align="left">12hr 45min</td><td align="left">0</td></tr><tr><td align="left">Canada</td><td align="left">Ottawa</td><td align="left">10 505</td><td align="left">16hr 30min</td><td align="left">0</td></tr><tr><td align="left">USA</td><td align="left">Washington DC</td><td align="left">11 157</td><td align="left">14hr 30min</td><td align="left">1</td></tr></tbody></table><table-wrap-foot><fn><p>Notes: Distance, flight time to Seoul, and numbers of direct flights per day between Seoul and each capital city came from Google search in April 2018.</p></fn></table-wrap-foot></table-wrap><table-wrap position="anchor" id="dtbl3"><label>Appendix Table 3</label><caption><p>Distance category by distance and flight time to Seoul (Republic of Korea)</p></caption><alt-text id="alttext0040">Appendix Table 3</alt-text><table frame="hsides" rules="groups"><thead><tr><th></th><th>Distance to Seoul (km)</th><th>Distance to Seoul (miles)</th><th>Flight time to Seoul</th></tr></thead><tbody><tr><td align="left">Close</td><td align="left">0 to 2500</td><td align="left">0 to 1500</td><td align="left">Less than 4 h</td></tr><tr><td align="left">Intermediate</td><td align="left">2501 to 6000</td><td align="left">1501 to 4000</td><td align="left">4–8 h</td></tr><tr><td align="left">Far</td><td align="left">6001 or more</td><td align="left">4001 or more</td><td align="left">More than 8 h</td></tr></tbody></table></table-wrap><table-wrap position="anchor" id="dtbl4"><label>Appendix Table 4</label><caption><p>Monthly percentage changes between actual and projected non-citizen arrivals to the Republic of Korea (ROK) during 2015 by whether an area had 100 or more severe acute respiratory syndrome (SARS) cases during the 2003 outbreak—sensitivity analyses using different projected values for the number of expected travelers in the absence of a Middle East respiratory syndrome (MERS) outbreak</p></caption><alt-text id="alttext0045">Appendix Table 4</alt-text><table frame="hsides" rules="groups"><thead><tr><th rowspan="3">Months</th><th colspan="5">Baseline (2013 and 2014)<hr></hr></th><th colspan="5">Alternative 1 (2014 and 2016)<hr></hr></th><th colspan="5">Alternative 2 (2013 and 2014 adjustment)<hr></hr></th></tr><tr><th colspan="2">SARS ≥100 cases (n = 5)<hr></hr></th><th colspan="2">SARS <100 cases (n = 13)<hr></hr></th><th rowspan="2"><italic>p</italic>-value</th><th colspan="2">SARS ≥100 cases (n = 5)<hr></hr></th><th colspan="2">SARS <100 cases (n = 13)<hr></hr></th><th rowspan="2"><italic>p</italic>-value</th><th colspan="2">SARS ≥100 cases (n = 5)<hr></hr></th><th colspan="2">SARS <100 cases (n = 13)<hr></hr></th><th rowspan="2"><italic>p</italic>-value</th></tr><tr><th>%</th><th>SE</th><th>%</th><th>SE</th><th>%</th><th>SE</th><th>%</th><th>SE</th><th>%</th><th>SE</th><th>%</th><th>SE</th></tr></thead><tbody><tr><td align="left">Jan</td><td align="left">21.7</td><td align="left">9.8</td><td align="left">3.0</td><td align="left">4.9</td><td align="left">0.934</td><td align="left">−0.04</td><td align="left">1.5</td><td align="left">−5.7</td><td align="left">2.7</td><td align="left">0.957</td><td align="left">−6.4</td><td align="left">3.4</td><td align="left">−4.4</td><td align="left">2.0</td><td align="left">0.314</td></tr><tr><td align="left">Feb</td><td align="left">31.1</td><td align="left">13.6</td><td align="left">1.7</td><td align="left">4.3</td><td align="left">0.954</td><td align="left">9.7</td><td align="left">4.1</td><td align="left">−9.9</td><td align="left">2.9</td><td align="left">0.999</td><td align="left">0.1</td><td align="left">3.6</td><td align="left">−4.9</td><td align="left">3.3</td><td align="left">0.833</td></tr><tr><td align="left">Mar</td><td align="left">28.4</td><td align="left">7.8</td><td align="left">7.5</td><td align="left">5.8</td><td align="left">0.973</td><td align="left">1.4</td><td align="left">2.0</td><td align="left">−3.8</td><td align="left">2.9</td><td align="left">0.921</td><td align="left">−1.0</td><td align="left">1.8</td><td align="left">−0.1</td><td align="left">3.0</td><td align="left">0.404</td></tr><tr><td align="left">Apr</td><td align="left">38.5</td><td align="left">7.0</td><td align="left">15.4</td><td align="left">5.2</td><td align="left">0.988</td><td align="left">8.0</td><td align="left">5.3</td><td align="left">−1.1</td><td align="left">3.3</td><td align="left">0.912</td><td align="left">7.3</td><td align="left">4.0</td><td align="left">7.1</td><td align="left">2.1</td><td align="left">0.510</td></tr><tr><td align="left">May</td><td align="left">30.2</td><td align="left">9.9</td><td align="left">9.2</td><td align="left">3.5</td><td align="left">0.952</td><td align="left">−0.1</td><td align="left">2.8</td><td align="left">−7.9</td><td align="left">2.9</td><td align="left">0.963</td><td align="left">0.02</td><td align="left">1.8</td><td align="left">2.2</td><td align="left">2.4</td><td align="left">0.242</td></tr><tr><td align="left">Jun</td><td align="left">−52.4</td><td align="left">9.8</td><td align="left">−23.3</td><td align="left">2.9</td><td align="left"><bold>0.018</bold></td><td align="left">−60.7</td><td align="left">8.8</td><td align="left">−32.1</td><td align="left">2.6</td><td align="left"><bold>0.013</bold></td><td align="left">−62.9</td><td align="left">7.9</td><td align="left">−28.3</td><td align="left">2.3</td><td align="left"><bold>0.004</bold></td></tr><tr><td align="left">Jul</td><td align="left">−60.0</td><td align="left">11.0</td><td align="left">−31.4</td><td align="left">4.7</td><td align="left"><bold>0.026</bold></td><td align="left">−66.7</td><td align="left">9.7</td><td align="left">−41.0</td><td align="left">4.5</td><td align="left"><bold>0.024</bold></td><td align="left">−67.9</td><td align="left">9.9</td><td align="left">−35.8</td><td align="left">4.3</td><td align="left"><bold>0.012</bold></td></tr><tr><td align="left">Aug</td><td align="left">−28.5</td><td align="left">7.8</td><td align="left">−3.8</td><td align="left">10.2</td><td align="left"><bold>0.035</bold></td><td align="left">−37.5</td><td align="left">7.6</td><td align="left">−12.9</td><td align="left">7.9</td><td align="left"><bold>0.020</bold></td><td align="left">−44.2</td><td align="left">7.2</td><td align="left">−10.1</td><td align="left">9.4</td><td align="left"><bold>0.005</bold></td></tr><tr><td align="left">Sep</td><td align="left">6.7</td><td align="left">12.7</td><td align="left">−4.4</td><td align="left">4.7</td><td align="left">0.776</td><td align="left">−11.5</td><td align="left">10.1</td><td align="left">−18.6</td><td align="left">3.1</td><td align="left">0.737</td><td align="left">−18.3</td><td align="left">6.5</td><td align="left">−10.6</td><td align="left">3.9</td><td align="left">0.167</td></tr><tr><td align="left">Oct</td><td align="left">6.5</td><td align="left">10.6</td><td align="left">8.1</td><td align="left">5.1</td><td align="left">0.446</td><td align="left">−14.1</td><td align="left">6.0</td><td align="left">−9.5</td><td align="left">3.0</td><td align="left">0.260</td><td align="left">−18.6</td><td align="left">3.3</td><td align="left">0.9</td><td align="left">3.6</td><td align="left"><bold>0.001</bold></td></tr><tr><td align="left">Nov</td><td align="left">11.4</td><td align="left">7.5</td><td align="left">10.3</td><td align="left">5.7</td><td align="left">0.548</td><td align="left">−10.0</td><td align="left">4.2</td><td align="left">−9.7</td><td align="left">2.4</td><td align="left">0.477</td><td align="left">−14.1</td><td align="left">2.4</td><td align="left">2.6</td><td align="left">3.4</td><td align="left"><bold><0.001</bold></td></tr><tr><td align="left">Dec</td><td align="left">9.5</td><td align="left">11.1</td><td align="left">5.7</td><td align="left">3.3</td><td align="left">0.655</td><td align="left">−11.7</td><td align="left">4.1</td><td align="left">−10.1</td><td align="left">2.2</td><td align="left">0.368</td><td align="left">−15.8</td><td align="left">2.3</td><td align="left">−1.1</td><td align="left">2.3</td><td align="left"><bold>0.002</bold></td></tr></tbody></table><table-wrap-foot><fn><p>Notes: Non-citizen arrivals are limited to non-citizen short-term visitor arrivals. SE stands for standard error. Boldface indicates statistical significance (<italic>p-value</italic> <0.05). <italic>Baseline</italic> columns showed percentage changes of monthly non-citizen arrivals by area for actual 2015 actual compared to the averages of 2013 and 2014 non-citizen arrivals to project the number of monthly non-citizen arrivals in the absence of a MERS outbreak. <italic>Alternative 1</italic> columns used the averages of 2014 and 2016 monthly arrivals to project values. <italic>Alternative 2</italic> columns project the expected number of non-citizen arrivals using the average monthly change in visitor arrivals from each area occurring between January and May 2015 (pre-outbreak) compared to the monthly averages from January through May 2013 and 2014. Area-specific projections were made in each of the models.</p></fn></table-wrap-foot></table-wrap><table-wrap position="anchor" id="dtbl5"><label>Appendix Table 5</label><caption><p>Monthly percentage changes of non-citizen arrivals to the Republic of Korea</p><p>(ROK) during 2015 based on actual versus projected values by distance to ROK—sensitivity analyses using different projected values to model the number of expected travelers in the absence of a Middle East respiratory syndrome outbreak</p></caption><alt-text id="alttext0050">Appendix Table 5</alt-text><table frame="hsides" rules="groups"><thead><tr><th rowspan="3"></th><th colspan="8">Alternative 1 (2014 and 2016)<hr></hr></th><th colspan="8">Alternative 2 (2013 and 2014 adjustment)<hr></hr></th></tr><tr><th colspan="2">Far (n = 7)<hr></hr></th><th colspan="3">Intermediate (n = 7)<hr></hr></th><th colspan="3">Close (n = 4)<hr></hr></th><th colspan="2">Far (n = 7)<hr></hr></th><th colspan="3">Intermediate (n = 7)<hr></hr></th><th colspan="3">Close (n = 4)<hr></hr></th></tr><tr><th>%</th><th>SE</th><th>%</th><th>SE</th><th><italic>p-value</italic></th><th>%</th><th>SE</th><th><italic>p-value</italic></th><th>%</th><th>SE</th><th>%</th><th>SE</th><th><italic>p-value</italic></th><th>%</th><th>SE</th><th><italic>p-value</italic></th></tr></thead><tbody><tr><td align="left">Jan</td><td align="left">−4.3</td><td align="left">2.5</td><td align="left">−4.4</td><td align="left">4.8</td><td align="left">0.488</td><td align="left">−3.4</td><td align="left">2.6</td><td align="left">0.595</td><td align="left">−7.7</td><td align="left">1.6</td><td align="left">−0.6</td><td align="left">3.1</td><td align="left">0.963</td><td align="left">−7.7</td><td align="left">3.7</td><td align="left">0.501</td></tr><tr><td align="left">Feb</td><td align="left">−3.2</td><td align="left">3.2</td><td align="left">−12.0</td><td align="left">4.6</td><td align="left">0.072</td><td align="left">6.6</td><td align="left">8.1</td><td align="left">0.842</td><td align="left">−4.7</td><td align="left">1.8</td><td align="left">−3.9</td><td align="left">6.0</td><td align="left">0.546</td><td align="left">−0.7</td><td align="left">5.2</td><td align="left">0.747</td></tr><tr><td align="left">Mar</td><td align="left">−1.9</td><td align="left">2.4</td><td align="left">−4.0</td><td align="left">5.2</td><td align="left">0.358</td><td align="left">−0.3</td><td align="left">2.7</td><td align="left">0.662</td><td align="left">−2.6</td><td align="left">4.1</td><td align="left">2.1</td><td align="left">3.9</td><td align="left">0.789</td><td align="left">−0.8</td><td align="left">2.4</td><td align="left">0.644</td></tr><tr><td align="left">Apr</td><td align="left">7.2</td><td align="left">4.9</td><td align="left">−6.0</td><td align="left">1.8</td><td align="left"><bold>0.018</bold></td><td align="left">4.5</td><td align="left">7.7</td><td align="left">0.389</td><td align="left">11.0</td><td align="left">2.5</td><td align="left">4.3</td><td align="left">2.5</td><td align="left"><bold>0.039</bold></td><td align="left">5.5</td><td align="left">4.9</td><td align="left">0.179</td></tr><tr><td align="left">May</td><td align="left">−0.1</td><td align="left">3.0</td><td align="left">−15.0</td><td align="left">2.2</td><td align="left"><bold>0.001</bold></td><td align="left">0.9</td><td align="left">3.1</td><td align="left">0.593</td><td align="left">3.9</td><td align="left">2.9</td><td align="left">−1.9</td><td align="left">3.0</td><td align="left">0.092</td><td align="left">3.6</td><td align="left">3.6</td><td align="left">0.471</td></tr><tr><td align="left">Jun</td><td align="left">−25.7</td><td align="left">2.8</td><td align="left">−41.4</td><td align="left">3.9</td><td align="left"><bold>0.003</bold></td><td align="left">−62.9</td><td align="left">9.1</td><td align="left"><bold>0.009</bold></td><td align="left">−24.9</td><td align="left">3.8</td><td align="left">−37.3</td><td align="left">4.6</td><td align="left"><bold>0.028</bold></td><td align="left">−61.9</td><td align="left">11.1</td><td align="left"><bold>0.016</bold></td></tr><tr><td align="left">Jul</td><td align="left">−26.6</td><td align="left">3.3</td><td align="left">−55.2</td><td align="left">1.7</td><td align="left"><bold><0.001</bold></td><td align="left">−73.6</td><td align="left">7.6</td><td align="left"><bold>0.001</bold></td><td align="left">−22.8</td><td align="left">3.3</td><td align="left">−50.5</td><td align="left">2.4</td><td align="left"><bold><0.001</bold></td><td align="left">−73.0</td><td align="left">9.5</td><td align="left"><bold>0.003</bold></td></tr><tr><td align="left">Aug</td><td align="left">−11.0</td><td align="left">1.9</td><td align="left">−15.6</td><td align="left">15.2</td><td align="left">0.385</td><td align="left">−42.3</td><td align="left">7.1</td><td align="left"><bold>0.008</bold></td><td align="left">−9.6</td><td align="left">3.0</td><td align="left">−14.5</td><td align="left">18.3</td><td align="left">0.399</td><td align="left">−45.9</td><td align="left">8.7</td><td align="left"><bold>0.008</bold></td></tr><tr><td align="left">Sep</td><td align="left">−12.9</td><td align="left">2.7</td><td align="left">−25.5</td><td align="left">4.2</td><td align="left"><bold>0.014</bold></td><td align="left">−7.8</td><td align="left">12.1</td><td align="left">0.649</td><td align="left">−9.9</td><td align="left">4.4</td><td align="left">−15.2</td><td align="left">6.4</td><td align="left">0.249</td><td align="left">−13.6</td><td align="left">7.8</td><td align="left">0.342</td></tr><tr><td align="left">Oct</td><td align="left">−4.0</td><td align="left">3.0</td><td align="left">−17.6</td><td align="left">4.0</td><td align="left"><bold>0.009</bold></td><td align="left">−10.6</td><td align="left">6.5</td><td align="left">0.197</td><td align="left">−0.5</td><td align="left">3.4</td><td align="left">−4.0</td><td align="left">7.2</td><td align="left">0.336</td><td align="left">−12.7</td><td align="left">6.7</td><td align="left">0.081</td></tr><tr><td align="left">Nov</td><td align="left">−6.2</td><td align="left">2.9</td><td align="left">−14.0</td><td align="left">3.0</td><td align="left"><bold>0.039</bold></td><td align="left">−8.6</td><td align="left">4.8</td><td align="left">0.338</td><td align="left">−0.4</td><td align="left">3.1</td><td align="left">1.4</td><td align="left">6.4</td><td align="left">0.598</td><td align="left">−11.0</td><td align="left">5.2</td><td align="left">0.064</td></tr><tr><td align="left">Dec</td><td align="left">−3.7</td><td align="left">1.8</td><td align="left">−18.2</td><td align="left">1.9</td><td align="left"><bold><0.001</bold></td><td align="left">−9.1</td><td align="left">2.5</td><td align="left">0.063</td><td align="left">−1.1</td><td align="left">2.9</td><td align="left">−6.5</td><td align="left">4.8</td><td align="left">0.177</td><td align="left">−10.0</td><td align="left">5.1</td><td align="left">0.091</td></tr></tbody></table><table-wrap-foot><fn id="tspara0060"><p>Notes: Non-citizen arrivals are limited to non-citizen short-term visitor arrivals. SE stands for standard error. Boldface indicates statistical significance (<italic>p</italic>-value < 0.05).</p></fn><fn id="tspara0065"><p><italic>Alternative 1</italic> columns used the average of 2014 and 2016 monthly non-citizen arrivals as projected numbers of non-citizen arrivals in the absence of the MERS outbreak. <italic>Alternative 2</italic> columns project the expected number of non-citizen arrivals using the average monthly change in visitor arrivals from each area occurring between January and May 2015 (pre-outbreak) compared to the monthly averages from January through May 2013 and 2014. Area-specific projections were made in each of the models. <italic>p</italic>-values in the intermediate column shows <italic>p</italic>-values to evaluate significant differences between the far and intermediate groups, while <italic>p-values</italic> in the close column shows <italic>p</italic>-values to evaluate significant differences between the far and close groups.</p></fn></table-wrap-foot></table-wrap><table-wrap position="anchor" id="dtbl6"><label>Appendix Table 6</label><caption><p>Monthly marginal percentage change of non-citizen arrivals to the Republic of Korea (ROK) during 2015 from areas associated with ≥100 probable severe acute respiratory syndrome (SARS) cases and by distance to the ROK based on projected values from regression models—sensitivity analyses using different models to project values.</p></caption><alt-text id="alttext0055">Appendix Table 6</alt-text><table frame="hsides" rules="groups"><thead><tr><th rowspan="2">Months</th><th rowspan="2">Independent variables</th><th colspan="3">Baseline (2013 and 2014)<hr></hr></th><th colspan="3">Alternative 1 (2014 and 2016)<hr></hr></th><th colspan="3">Alternative 2 (2013 and 2014 adjustment)<hr></hr></th></tr><tr><th>Coef.</th><th><italic>p</italic>-value</th><th>R-squared</th><th>Coef.</th><th><italic>p</italic>-value</th><th>R-squared</th><th>Coef.</th><th><italic>p</italic>-value</th><th>R-squared</th></tr></thead><tbody><tr><td rowspan="3" align="left">Jun</td><td align="left">SARS ≥100</td><td align="left">−18.3</td><td align="left"><bold>0.032</bold></td><td rowspan="3" align="left">0.68</td><td align="left">−19.3</td><td align="left"><bold>0.003</bold></td><td rowspan="3" align="left">0.81</td><td align="left">−27.1</td><td align="left"><bold><0.001</bold></td><td rowspan="3" align="left">0.82</td></tr><tr><td align="left">Close</td><td align="left">−25.5</td><td align="left"><bold>0.012</bold></td><td align="left">−25.5</td><td align="left"><bold><0.001</bold></td><td align="left">−20.5</td><td align="left"><bold>0.008</bold></td></tr><tr><td align="left">Intermediate<hr></hr></td><td align="left">−9.7<hr></hr></td><td align="left">0.155<hr></hr></td><td align="left">−15.7<hr></hr></td><td align="left"><bold>0.004</bold><hr></hr></td><td align="left">−12.4<hr></hr></td><td align="left">0.022<hr></hr></td></tr><tr><td rowspan="3" align="left">Jul</td><td align="left">SARS ≥100</td><td align="left">−11.8</td><td align="left">0.058</td><td rowspan="3" align="left">0.87</td><td align="left">−12.5</td><td align="left"><bold>0.027</bold></td><td rowspan="3" align="left">0.88</td><td align="left">−19.2</td><td align="left"><bold>0.002</bold></td><td rowspan="3" align="left">0.90</td></tr><tr><td align="left">Close</td><td align="left">−45.6</td><td align="left"><bold><0.001</bold></td><td align="left">−39.3</td><td align="left"><bold><0.001</bold></td><td align="left">−38.5</td><td align="left"><bold><0.001</bold></td></tr><tr><td align="left">Intermediate<hr></hr></td><td align="left">−26.8<hr></hr></td><td align="left"><bold><0.001</bold><hr></hr></td><td align="left">−28.5<hr></hr></td><td align="left"><bold><0.001</bold><hr></hr></td><td align="left">−27.7<hr></hr></td><td align="left"><bold><0.001</bold><hr></hr></td></tr><tr><td rowspan="3" align="left">Aug</td><td align="left">SARS ≥100</td><td align="left">−11.7</td><td align="left">0.595</td><td rowspan="3" align="left">0.41</td><td align="left">−13.7</td><td align="left">0.433</td><td rowspan="3" align="left">0.24</td><td align="left">−24.0</td><td align="left">0.252</td><td rowspan="3" align="left">0.27</td></tr><tr><td align="left">Close</td><td align="left">−25.5</td><td align="left">0.323</td><td align="left">−23.0</td><td align="left">0.261</td><td align="left">−21.7</td><td align="left">0.366</td></tr><tr><td align="left">Intermediate<hr></hr></td><td align="left">−1.3<hr></hr></td><td align="left">0.944<hr></hr></td><td align="left">−4.7<hr></hr></td><td align="left">0.749<hr></hr></td><td align="left">−4.9<hr></hr></td><td align="left">0.774<hr></hr></td></tr><tr><td rowspan="3" align="left">Sep</td><td align="left">SARS ≥100</td><td align="left">7.5</td><td align="left">0.593</td><td rowspan="3" align="left">0.08</td><td align="left">1.8</td><td align="left">0.847</td><td rowspan="3" align="left">0.23</td><td align="left">−10.4</td><td align="left">0.282</td><td rowspan="3" align="left">0.11</td></tr><tr><td align="left">Close</td><td align="left">6.4</td><td align="left">0.695</td><td align="left">4.0</td><td align="left">0.704</td><td align="left">2.5</td><td align="left">0.820</td></tr><tr><td align="left">Intermediate<hr></hr></td><td align="left">−0.8<hr></hr></td><td align="left">0.945<hr></hr></td><td align="left">−12.6<hr></hr></td><td align="left">0.120<hr></hr></td><td align="left">−5.4<hr></hr></td><td align="left">0.500<hr></hr></td></tr><tr><td rowspan="3" align="left">Oct</td><td align="left">SARS ≥100</td><td align="left">−2.2</td><td align="left">0.876</td><td rowspan="3" align="left"><0.01</td><td align="left">−6.8</td><td align="left">0.311</td><td rowspan="3" align="left">0.35</td><td align="left">−20.5</td><td align="left"><bold>0.022</bold></td><td rowspan="3" align="left">0.40</td></tr><tr><td align="left">Close</td><td align="left">2.0</td><td align="left">0.903</td><td align="left">−2.5</td><td align="left">0.743</td><td align="left">−12.2</td><td align="left">0.205</td></tr><tr><td align="left">Intermediate<hr></hr></td><td align="left">2.0<hr></hr></td><td align="left">0.861<hr></hr></td><td align="left">−13.7<hr></hr></td><td align="left"><bold>0.025</bold><hr></hr></td><td align="left">−3.5<hr></hr></td><td align="left">0.662<hr></hr></td></tr><tr><td rowspan="3" align="left">Nov</td><td align="left">SARS ≥100</td><td align="left">3.1</td><td align="left">0.820</td><td rowspan="3" align="left">0.04</td><td align="left">−1.5</td><td align="left">0.777</td><td rowspan="3" align="left">0.39</td><td align="left">−15.7</td><td align="left">0.051</td><td rowspan="3" align="left">0.36</td></tr><tr><td align="left">Close</td><td align="left">0.5</td><td align="left">0.975</td><td align="left">−1.5</td><td align="left">0.810</td><td align="left">−1.0</td><td align="left">0.908</td></tr><tr><td align="left">Intermediate<hr></hr></td><td align="left">8.2<hr></hr></td><td align="left">0.470<hr></hr></td><td align="left">−7.9<hr></hr></td><td align="left">0.101<hr></hr></td><td align="left">1.8<hr></hr></td><td align="left">0.773<hr></hr></td></tr><tr><td rowspan="3" align="left">Dec</td><td align="left">SARS ≥100</td><td align="left">2.2</td><td align="left">0.819</td><td rowspan="3" align="left">0.02</td><td align="left">−3.9</td><td align="left">0.233</td><td rowspan="3" align="left">0.70</td><td align="left">−16.8</td><td align="left"><bold>0.006</bold></td><td rowspan="3" align="left">0.50</td></tr><tr><td align="left">Close</td><td align="left">2.8</td><td align="left">0.807</td><td align="left">−3.0</td><td align="left">0.427</td><td align="left">1.4</td><td align="left">0.820</td></tr><tr><td align="left">Intermediate</td><td align="left">−0.6</td><td align="left">0.937</td><td align="left">−14.5</td><td align="left"><bold><0.001</bold></td><td align="left">−5.4</td><td align="left">0.235</td></tr></tbody></table><table-wrap-foot><fn><p>Notes: Non-citizen arrivals are limited to non-citizen short-term visitor arrivals. Boldface indicates statistical significance (<italic>p</italic>-value < 0.05). <italic>Baseline</italic> columns showed percentage changes of monthly non-citizen arrivals by area for actual 2015 actual compared to the averages of 2013 and 2014 monthly non-citizen arrivals to project the number of arrivals in the absence of a MERS outbreak. <italic>Alternative 1</italic> columns used the average of 2014 and 2016 monthly non-citizen arrivals to project values. <italic>Alternative 2</italic> columns project the expected number of non-citizen arrivals using the average monthly change in non-citizen arrivals from each area occurring between January and May 2015 (pre-outbreak) compared to the monthly averages from January through May 2013 and 2014. Area-specific projections were made in each of the models.</p></fn></table-wrap-foot></table-wrap><table-wrap position="anchor" id="dtbl7"><label>Appendix Table 7</label><caption><p>Sensitivity analyses with and without the area-month observation for India, August 2015</p></caption><alt-text id="alttext0060">Appendix Table 7</alt-text><table frame="hsides" rules="groups"><thead><tr><th colspan="16">Percentage changes in non-citizen arrivals to the Republic of Korea (ROK) in August 2015 between actual and projected values by whether an area had ≥100 probable severe acute respiratory syndrome (SARS) cases during the 2003 outbreak<hr></hr></th></tr><tr><th rowspan="3"></th><th colspan="5">Baseline (2013 and 2014)<hr></hr></th><th colspan="5">Alternative 1 (2014 and 2016)<hr></hr></th><th colspan="5">Alternative 2 (2013 and 2014 adjustment)<hr></hr></th></tr><tr><th colspan="2">SARS ≥100 cases<hr></hr></th><th colspan="2">SARS <100 cases<hr></hr></th><th rowspan="2"><italic>p</italic>-value</th><th colspan="2">SARS ≥100 cases<hr></hr></th><th colspan="2">SARS <100 cases<hr></hr></th><th rowspan="2"><italic>p</italic>-value</th><th colspan="2">SARS ≥100 cases<hr></hr></th><th colspan="2">SARS <100 cases<hr></hr></th><th rowspan="2"><italic>p</italic>-value</th></tr><tr><th>%</th><th>SE</th><th>%</th><th>SE</th><th>%</th><th>SE</th><th>%</th><th>SE</th><th>%</th><th>SE</th><th>%</th><th>SE</th></tr></thead><tbody><tr><td align="left">Without India</td><td align="left">−28.5</td><td align="left">7.8</td><td align="left">−13.0</td><td align="left">4.8</td><td align="left">0.062</td><td align="left">−37.5</td><td align="left">7.6</td><td align="left">−20.2</td><td align="left">3.6</td><td align="left"><bold>0.040</bold></td><td align="left">−44.2</td><td align="left">7.2</td><td align="left">−18.7</td><td align="left">4.0</td><td align="left"><bold>0.007</bold></td></tr><tr><td align="left">With India</td><td align="left">−28.5</td><td align="left">7.8</td><td align="left">−3.8</td><td align="left">10.2</td><td align="left"><bold>0.035</bold></td><td align="left">−37.5</td><td align="left">7.6</td><td align="left">−12.9</td><td align="left">7.9</td><td align="left"><bold>0.020</bold></td><td align="left">−44.2</td><td align="left">7.2</td><td align="left">−10.1</td><td align="left">9.4</td><td align="left"><bold>0.005</bold></td></tr></tbody></table><table frame="hsides" rules="groups"><thead><tr><th rowspan="3"></th><th colspan="5">Baseline (2013 and 2014)<hr></hr></th><th colspan="5">Alternative 1 (2014 and 2016)<hr></hr></th><th colspan="5">Alternative 2 (2013 and 2014 adjustment)<hr></hr></th></tr><tr><th colspan="2">Far<hr></hr></th><th colspan="2">Intermediate<hr></hr></th><th rowspan="2"><italic>p</italic>-value</th><th colspan="2">Far<hr></hr></th><th colspan="2">Intermediate<hr></hr></th><th rowspan="2"><italic>p</italic>-value</th><th colspan="2">Far<hr></hr></th><th colspan="2">Intermediate<hr></hr></th><th rowspan="2"><italic>p</italic>-value</th></tr><tr><th>%</th><th>SE</th><th>%</th><th>SE</th><th>%</th><th>SE</th><th>%</th><th>SE</th><th>%</th><th>SE</th><th>%</th><th>SE</th></tr></thead><tbody><tr><td align="left">Without India</td><td align="left">−2.9</td><td align="left">1.9</td><td align="left">−22.7</td><td align="left">6.7</td><td align="left"><bold>0.015</bold></td><td align="left">−11.0</td><td align="left">1.9</td><td align="left">−30.5</td><td align="left">8.6</td><td align="left"><bold><0.001</bold></td><td align="left">−9.6</td><td align="left">3.0</td><td align="left">−32.5</td><td align="left">3.8</td><td align="left"><bold><0.001</bold></td></tr><tr><td align="left">With India</td><td align="left">−2.9</td><td align="left">1.9</td><td align="left">−4.2</td><td align="left">19.3</td><td align="left">0.474</td><td align="left">−11.0</td><td align="left">1.9</td><td align="left">−15.6</td><td align="left">15.2</td><td align="left">0.385</td><td align="left">−9.6</td><td align="left">3.0</td><td align="left">−14.5</td><td align="left">18.3</td><td align="left">0.399</td></tr></tbody></table><table frame="hsides" rules="groups"><thead><tr><th rowspan="2"></th><th rowspan="2">Independent variables</th><th colspan="3">Baseline (2013 and 2014)<hr></hr></th><th colspan="3">Alternative 1 (2014 and 2016)<hr></hr></th><th colspan="3">Alternative 2 (2013 and 2014 adjustment)<hr></hr></th></tr><tr><th>Coef.</th><th><italic>p</italic>-value</th><th>R-squared</th><th>Coef.</th><th><italic>p</italic>-value</th><th>R-squared</th><th>Coef.</th><th><italic>p</italic>-value</th><th>R-squared</th></tr></thead><tbody><tr><td rowspan="3" align="left">Without India</td><td align="left">SARS ≥100</td><td align="left">−4.3</td><td align="left">0.602</td><td rowspan="3" align="left">0.60</td><td align="left">−7.7</td><td align="left">0.186</td><td rowspan="3" align="left">0.75</td><td align="left">−16.9</td><td align="left"><bold>0.010</bold></td><td rowspan="3" align="left">0.81</td></tr><tr><td align="left">Close</td><td align="left">−30.0</td><td align="left"><bold>0.006</bold></td><td align="left">−26.6</td><td align="left"><bold>0.001</bold></td><td align="left">−26.0</td><td align="left"><bold>0.002</bold></td></tr><tr><td align="left">Intermediate<hr></hr></td><td align="left">−19.6<hr></hr></td><td align="left"><bold>0.014</bold><hr></hr></td><td align="left">−19.4<hr></hr></td><td align="left"><bold>0.001</bold><hr></hr></td><td align="left">−22.6<hr></hr></td><td align="left"><bold>0.001</bold><hr></hr></td></tr><tr><td rowspan="3" align="left">With India</td><td align="left">SARS ≥100</td><td align="left">−11.7</td><td align="left">0.595</td><td rowspan="3" align="left">0.41</td><td align="left">−13.7</td><td align="left">0.433</td><td rowspan="3" align="left">0.24</td><td align="left">−24.0</td><td align="left">0.252</td><td rowspan="3" align="left">0.27</td></tr><tr><td align="left">Close</td><td align="left">−25.5</td><td align="left">0.323</td><td align="left">−23.0</td><td align="left">0.261</td><td align="left">−21.7</td><td align="left">0.366</td></tr><tr><td align="left">Intermediate</td><td align="left">−1.3</td><td align="left">0.944</td><td align="left">−4.7</td><td align="left">0.749</td><td align="left">−4.9</td><td align="left">0.774</td></tr></tbody></table><table-wrap-foot><fn><p>Notes: Non-citizen arrivals are limited to non-citizen short-term visitor arrivals. Boldface indicates statistical significance (<italic>p</italic>-value < 0.05).</p></fn></table-wrap-foot></table-wrap></p></sec><sec id="appsec1" sec-type="supplementary-material"><label>Appendix A</label><title>Supplementary data</title><p id="p0180">The following is the supplementary data to this article:<supplementary-material content-type="local-data" id="mmc1"><caption><title>Multimedia component 1</title></caption><media xlink:href="mmc1.xml"><alt-text>Multimedia component 1</alt-text></media></supplementary-material></p></sec><fn-group><fn id="appsec2" fn-type="supplementary-material"><label>Appendix A</label><p id="p0185">Supplementary data to this article can be found online at <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.1016/j.tmaid.2019.05.009" id="intref0010">https://doi.org/10.1016/j.tmaid.2019.05.009</ext-link>.</p></fn></fn-group></back></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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