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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Comparative epidemiology of Middle East respiratory syndrome
coronavirus (MERS-CoV) in Saudi Arabia and South Korea</title>
<author><name sortKey="Chen, Xin" sort="Chen, Xin" uniqKey="Chen X" first="Xin" last="Chen">Xin Chen</name>
<affiliation><nlm:aff id="AFF0001"><institution>School of Public Health and Community MedicineUniversity of New South Wales</institution>
, Sydney, NSW 2052,<country>Australia</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Chughtai, Abrar Ahmad" sort="Chughtai, Abrar Ahmad" uniqKey="Chughtai A" first="Abrar Ahmad" last="Chughtai">Abrar Ahmad Chughtai</name>
<affiliation><nlm:aff id="AFF0001"><institution>School of Public Health and Community MedicineUniversity of New South Wales</institution>
, Sydney, NSW 2052,<country>Australia</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Dyda, Amalie" sort="Dyda, Amalie" uniqKey="Dyda A" first="Amalie" last="Dyda">Amalie Dyda</name>
<affiliation><nlm:aff id="AFF0001"><institution>School of Public Health and Community MedicineUniversity of New South Wales</institution>
, Sydney, NSW 2052,<country>Australia</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Macintyre, Chandini Raina" sort="Macintyre, Chandini Raina" uniqKey="Macintyre C" first="Chandini Raina" last="Macintyre">Chandini Raina Macintyre</name>
<affiliation><nlm:aff id="AFF0001"><institution>School of Public Health and Community MedicineUniversity of New South Wales</institution>
, Sydney, NSW 2052,<country>Australia</country>
</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="AFF0002"><institution>College of Public Service and Community SolutionsArizona State University</institution>
, Tempe, AZ 85287,<country>USA</country>
</nlm:aff>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PMC</idno>
<idno type="pmid">28588290</idno>
<idno type="pmc">5520315</idno>
<idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5520315</idno>
<idno type="RBID">PMC:5520315</idno>
<idno type="doi">10.1038/emi.2017.40</idno>
<date when="2017">2017</date>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Comparative epidemiology of Middle East respiratory syndrome
coronavirus (MERS-CoV) in Saudi Arabia and South Korea</title>
<author><name sortKey="Chen, Xin" sort="Chen, Xin" uniqKey="Chen X" first="Xin" last="Chen">Xin Chen</name>
<affiliation><nlm:aff id="AFF0001"><institution>School of Public Health and Community MedicineUniversity of New South Wales</institution>
, Sydney, NSW 2052,<country>Australia</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Chughtai, Abrar Ahmad" sort="Chughtai, Abrar Ahmad" uniqKey="Chughtai A" first="Abrar Ahmad" last="Chughtai">Abrar Ahmad Chughtai</name>
<affiliation><nlm:aff id="AFF0001"><institution>School of Public Health and Community MedicineUniversity of New South Wales</institution>
, Sydney, NSW 2052,<country>Australia</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Dyda, Amalie" sort="Dyda, Amalie" uniqKey="Dyda A" first="Amalie" last="Dyda">Amalie Dyda</name>
<affiliation><nlm:aff id="AFF0001"><institution>School of Public Health and Community MedicineUniversity of New South Wales</institution>
, Sydney, NSW 2052,<country>Australia</country>
</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Macintyre, Chandini Raina" sort="Macintyre, Chandini Raina" uniqKey="Macintyre C" first="Chandini Raina" last="Macintyre">Chandini Raina Macintyre</name>
<affiliation><nlm:aff id="AFF0001"><institution>School of Public Health and Community MedicineUniversity of New South Wales</institution>
, Sydney, NSW 2052,<country>Australia</country>
</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="AFF0002"><institution>College of Public Service and Community SolutionsArizona State University</institution>
, Tempe, AZ 85287,<country>USA</country>
</nlm:aff>
</affiliation>
</author>
</analytic>
<series><title level="j">Emerging Microbes & Infections</title>
<idno type="eISSN">2222-1751</idno>
<imprint><date when="2017">2017</date>
</imprint>
</series>
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<front><div type="abstract" xml:lang="en"><p>MERS-CoV infection emerged in the Kingdom of Saudi Arabia (KSA) in 2012 and has spread to
26 countries. However, 80% of all cases have occurred in KSA. The largest outbreak outside
KSA occurred in South Korea (SK) in 2015. In this report, we describe an epidemiological
comparison of the two outbreaks. Data from 1299 cases in KSA (2012–2015) and 186 cases in
SK (2015) were collected from publicly available resources, including FluTrackers, the
World Health Organization (WHO) outbreak news and the Saudi MOH (MOH). Descriptive
analysis, <italic>t</italic>
-tests, Chi-square tests and binary logistic regression were
conducted to compare demographic and other characteristics (comorbidity, contact history)
of cases by nationality. Epidemic curves of the outbreaks were generated. The mean age of
cases was 51 years in KSA and 54 years in SK. Older males (⩾70 years) were more likely to
be infected or to die from MERS-CoV infection, and males exhibited increased rates of
comorbidity in both countries. The epidemic pattern in KSA was more complex, with
animal-to-human, human-to-human, nosocomial and unknown exposure, whereas the outbreak in
SK was more clearly nosocomial. Of the 1186 MERS cases in KSA with reported risk factors,
158 (13.3%) cases were hospital associated compared with 175 (94.1%) in SK, and an
increased proportion of cases with unknown exposure risk was found in KSA (710, 59.9%). In
a globally connected world, travel is a risk factor for emerging infections, and health
systems in all countries should implement better triage systems for potential imported
cases of MERS-CoV to prevent large epidemics.</p>
<p><italic>Emerging Microbes & Infections</italic>
(2017) <bold>6,</bold>
e51;
doi:10.1038/emi.2017.40; published online 7 June 2017</p>
</div>
</front>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Emerg Microbes Infect</journal-id>
<journal-id journal-id-type="iso-abbrev">Emerg Microbes Infect</journal-id>
<journal-id journal-id-type="publisher-id">TEMI</journal-id>
<journal-id journal-id-type="publisher-id">temi20</journal-id>
<journal-title-group><journal-title>Emerging Microbes & Infections</journal-title>
</journal-title-group>
<issn pub-type="epub">2222-1751</issn>
<publisher><publisher-name>Taylor & Francis</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">28588290</article-id>
<article-id pub-id-type="pmc">5520315</article-id>
<article-id pub-id-type="publisher-id">12040147</article-id>
<article-id pub-id-type="doi">10.1038/emi.2017.40</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Original Articles</subject>
</subj-group>
</article-categories>
<title-group><article-title>Comparative epidemiology of Middle East respiratory syndrome
coronavirus (MERS-CoV) in Saudi Arabia and South Korea</article-title>
<alt-title alt-title-type="running-title">Epidemiology of MERS in Saudi Arabia and South
Korea</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Chen</surname>
<given-names>Xin</given-names>
</name>
<xref ref-type="aff" rid="AFF0001"><sup>1</sup>
</xref>
<xref ref-type="corresp" rid="AN0001"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chughtai</surname>
<given-names>Abrar Ahmad</given-names>
</name>
<xref ref-type="aff" rid="AFF0001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Dyda</surname>
<given-names>Amalie</given-names>
</name>
<xref ref-type="aff" rid="AFF0001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>MacIntyre</surname>
<given-names>Chandini Raina</given-names>
</name>
<xref ref-type="aff" rid="AFF0001"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="AFF0002"><sup>2</sup>
</xref>
</contrib>
<aff id="AFF0001"><label>1</label>
<institution>School of Public Health and Community MedicineUniversity of New South Wales</institution>
, Sydney, NSW 2052,<country>Australia</country>
</aff>
<aff id="AFF0002"><label>2</label>
<institution>College of Public Service and Community SolutionsArizona State University</institution>
, Tempe, AZ 85287,<country>USA</country>
</aff>
</contrib-group>
<author-notes><corresp id="AN0001">X Chen E-mail: <email>z5031597@unsw.edu.au</email>
</corresp>
</author-notes>
<pub-date pub-type="collection"><year>2017</year>
</pub-date>
<pub-date pub-type="epub"><day>7</day>
<month>6</month>
<year>2017</year>
</pub-date>
<volume>6</volume>
<issue>1</issue>
<fpage seq="53">1</fpage>
<lpage>6</lpage>
<history><date date-type="received"><day>14</day>
<month>6</month>
<year>2016</year>
</date>
<date date-type="rev-recd"><day>17</day>
<month>1</month>
<year>2017</year>
</date>
<date date-type="accepted"><day>26</day>
<month>3</month>
<year>2017</year>
</date>
</history>
<permissions><copyright-statement>© The Author(s) 2017</copyright-statement>
<copyright-year>2017</copyright-year>
<copyright-holder>The Author(s)</copyright-holder>
<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/"><license-p><bold>Open Access</bold>
This work is licensed under a Creative Commons
Attribution 4.0 International License. The images or other third party material in this
article are included in the article’s Creative Commons license, unless indicated
otherwise in the credit line; if the material is not included under the Creative Commons
license, users will need to obtain permission from the license holder to reproduce the
material. To view a copy of this license, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link>
</license-p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:href="TEMI_6_12040147.pdf"></self-uri>
<abstract id="Abs1"><p>MERS-CoV infection emerged in the Kingdom of Saudi Arabia (KSA) in 2012 and has spread to
26 countries. However, 80% of all cases have occurred in KSA. The largest outbreak outside
KSA occurred in South Korea (SK) in 2015. In this report, we describe an epidemiological
comparison of the two outbreaks. Data from 1299 cases in KSA (2012–2015) and 186 cases in
SK (2015) were collected from publicly available resources, including FluTrackers, the
World Health Organization (WHO) outbreak news and the Saudi MOH (MOH). Descriptive
analysis, <italic>t</italic>
-tests, Chi-square tests and binary logistic regression were
conducted to compare demographic and other characteristics (comorbidity, contact history)
of cases by nationality. Epidemic curves of the outbreaks were generated. The mean age of
cases was 51 years in KSA and 54 years in SK. Older males (⩾70 years) were more likely to
be infected or to die from MERS-CoV infection, and males exhibited increased rates of
comorbidity in both countries. The epidemic pattern in KSA was more complex, with
animal-to-human, human-to-human, nosocomial and unknown exposure, whereas the outbreak in
SK was more clearly nosocomial. Of the 1186 MERS cases in KSA with reported risk factors,
158 (13.3%) cases were hospital associated compared with 175 (94.1%) in SK, and an
increased proportion of cases with unknown exposure risk was found in KSA (710, 59.9%). In
a globally connected world, travel is a risk factor for emerging infections, and health
systems in all countries should implement better triage systems for potential imported
cases of MERS-CoV to prevent large epidemics.</p>
<p><italic>Emerging Microbes & Infections</italic>
(2017) <bold>6,</bold>
e51;
doi:10.1038/emi.2017.40; published online 7 June 2017</p>
</abstract>
<kwd-group kwd-group-type="author"><kwd>Middle East respiratory syndrome coronavirus</kwd>
<kwd>epidemiology</kwd>
<kwd>emerging infectious disease</kwd>
</kwd-group>
<counts><fig-count count="2"></fig-count>
<table-count count="1"></table-count>
<equation-count count="0"></equation-count>
<ref-count count="42"></ref-count>
<page-count count="6"></page-count>
</counts>
</article-meta>
</front>
<body><sec id="sec1"><title>Introduction</title>
<p>MERS-CoV first emerged in the Kingdom of Saudi Arabia (KSA) in 2012<sup><xref rid="bib1" ref-type="bibr">1</xref>
</sup>
and has since spread to 26
countries.<sup><xref rid="bib2" ref-type="bibr">2</xref>
</sup>
By far, the greatest
burden of disease is located in KSA, and most cases in other countries have not resulted in
large satellite epidemics. The exact origin of MERS-CoV remains unknown, but the
transmission pattern and evidence from virological studies suggest that dromedary camels are
the major reservoir host,<sup><xref rid="bib3" ref-type="bibr">3</xref>
, <xref rid="bib4" ref-type="bibr">4</xref>
, <xref rid="bib5" ref-type="bibr">5</xref>
, <xref rid="bib6" ref-type="bibr">6</xref>
</sup>
from which human infections may sporadically
occur through zoonotic transmission. Human-to-human transmission also occurs in healthcare
facilities and communities.<sup><xref rid="bib7" ref-type="bibr">7</xref>
, <xref rid="bib8" ref-type="bibr">8</xref>
, <xref rid="bib9" ref-type="bibr">9</xref>
</sup>
Globally, as of 16 January, 2017, a total of 1879 laboratory-confirmed cases of MERS-CoV and
at least 659 deaths have been reported to the WHO.<sup><xref rid="bib10" ref-type="bibr">10</xref>
, <xref rid="bib11" ref-type="bibr">11</xref>
</sup>
The case fatality rate
(CFR) in patients (35%) is higher than that of Severe Acute Respiratory Syndrome (SARS)
(9.6%).<sup><xref rid="bib12" ref-type="bibr">12</xref>
, <xref rid="bib13" ref-type="bibr">13</xref>
</sup>
In contrast to MERS-CoV, the SARS epidemic, which exhibited
an increased estimated reproductive number <italic>R</italic>
<sub>0</sub>
of ~2, peaked,
waned and ended within 8 months. MERS has a lower <italic>R</italic>
<sub>0</sub>
, estimated
to be closer to 1. In addition, MERS has paradoxically persisted with a largely sporadic
pattern for over four years.<sup><xref rid="bib14" ref-type="bibr">14</xref>
, <xref rid="bib15" ref-type="bibr">15</xref>
</sup>
Among the global MERS cases, males
(63%)<sup><xref rid="bib16" ref-type="bibr">16</xref>
</sup>
are more affected than
females, with a male to female ratio of 1.7:1.<sup><xref rid="bib17" ref-type="bibr">17</xref>
</sup>
The mean age of all cases reported worldwide is 49 years, and most
cases are between 50 and 59 years of age.<sup><xref rid="bib17" ref-type="bibr">17</xref>
</sup>
</p>
<p>The largest MERS outbreak to date outside KSA occurred in SK,<sup><xref rid="bib18" ref-type="bibr">18</xref>
, <xref rid="bib19" ref-type="bibr">19</xref>
</sup>
with 186 cases
and 39 deaths (CFR: 21%) reported from May to July in 2015. All cases (excluding the index
case, who had traveled from the Middle East) were linked to a single-chain of transmission
and were associated with healthcare facilities, owing to the lack of awareness of MERS-CoV
at the index hospital and inappropriate triage and infection control.<sup><xref rid="bib20" ref-type="bibr">20</xref>
</sup>
Males were more affected than females in SK,
comprising 59% of total laboratory-confirmed cases<sup><xref rid="bib21" ref-type="bibr">21</xref>
</sup>
and 66.7% case fatalities.<sup><xref rid="bib22" ref-type="bibr">22</xref>
</sup>
Prevention measures, including placing over 3000 people in quarantine
and closing 700 schools, were implemented for outbreak control.<sup><xref rid="bib23" ref-type="bibr">23</xref>
</sup>
The aim of this study was to compare the epidemiology of
MERS-CoV in KSA and SK before 2016.</p>
</sec>
<sec id="sec2"><title>Materials and Methods</title>
<p>A database of MERS cases was created by using data collected from the onset of the first
case in June 2012 to December 2015 in KSA and SK. The confirmed cases of MERS-CoV in KSA and
SK were sourced from FluTrackers. To validate the data, we assessed and enhanced the data
set with more detailed information from reports published by the WHO, Promed Mail and local
organizations, such as the Saudi MOH, during the outbreak. The data collected included case
list number, demographic characteristics (e.g., age, sex and healthcare worker status), date
of notification, comorbidity, date of symptoms onset, date of first hospitalization, date of
laboratory confirmation, current status, date of outcome, contact history and nationality.
We categorized cases into six age groups as follows: <30 years, 30–39 years, 40–49 years,
50–59 years, 60–69 years and ⩾70 years. The proportion of cases in each age group was
calculated by dividing the number of cases in each age group by the number of total cases,
then multiplying by 100. The sex-specific CFR per age group was calculated by dividing the
number of deaths among males or females in an age group by the total number of cases limited
to the one sex in that age group, then multiplying by 100. On the basis of different contact
or exposure risk factors, the contact history was classified into camel-linked (contact with
camels and camel products), sheep-linked (contact with sheep and sheep products),
hospital-linked (contact with diseased patients or healthcare workers, or healthcare
facilities which had MERS-CoV outbreak), community-linked (contact with diseased family
members or friends) and unknown (no contact history or investigation ongoing).</p>
<p>Cases with missing values for age, sex, healthcare worker, comorbidity, date of symptoms
onset and contact history were excluded from the analysis. Data were missing for 113 cases
for all variables,<sup><xref rid="bib24" ref-type="bibr">24</xref>
</sup>
36 cases for age,
49 cases for sex, 19 cases for fatality, 20 cases for healthcare worker status, 121 cases
for comorbidity and six cases for contact history in KSA. In addition, data were missing for
16 cases regarding healthcare worker status and 145 cases regarding comorbidity in SK. The
data were used to plot epidemic curves. Descriptive analysis was conducted to calculate the
mean value of continuous variables (e.g., age). <italic>t</italic>
-tests, Chi-square tests
and binary logistic regression were used to compare the mean values of age and the
proportions of categorical variables (e.g., sex, healthcare worker, comorbidity, contact
history) on the basis of nationality. The analysis was conducted in SPSS version 22. A
<italic>P</italic>
-value<0.05 was considered statistically significant.</p>
</sec>
<sec id="sec3"><title>Results</title>
<p>A total of 1299 cases (1186 cases in analysis) from KSA and 186 cases from SK were reported
from 2012 to 2015. On the basis of the available data, the comparison of demographics and
other characteristics between KSA and SK is presented in Table <xref rid="T0001" ref-type="table">1</xref>
. The mean age of MERS cases was 51 years in KSA, and 54 years in
SK. In both countries, most cases were reported in the age group ⩾70 years (KSA: 227 of 1186
cases, 19.1% and SK: 40 of 186 cases, 21.5%) followed by the age group 50–59 years (KSA: 215
of 1186 cases, 18.1% and SK: 38 of 186 cases, 20.4%) (<xref rid="F0001" ref-type="fig">Figure 1A</xref>
). Overall, there was a higher frequency of males among cases in KSA (741
of 1137, 65.2%) than in SK (110 of 186, 59.1%); however, no significant differences in sex
distribution were found between the two countries. The overall CFR was 19.2% (228 fatalities
of 1186 cases) in KSA and 19.4% (36 fatalities of 186 cases) in SK. The age-specific CFR
increased by age in both counties, and the highest values were reported in the age group ⩾70
years (<xref rid="F0001" ref-type="fig">Figures 1B and 1C</xref>
). In terms of sex-specific
CFR, there was no significant difference between KSA and SK. No significant difference was
found in the proportions of infected healthcare workers between KSA (157 of 1166, 13.5%) and
SK (26 of 170, 15.3%). Rates of MERS cases with comorbidity were also higher in males in
both KSA (460 of 657, 70.0%) and SK (21 of 35, 60.0%). No significant differences in
sex-specific comorbidity rates were observed between KSA and SK.<fig id="F0001" orientation="portrait" position="float"><label>Figure 1</label>
<caption><p>The distribution of MERS cases and deaths in KSA
(2012-2015) and SK (2015)<sup>#</sup>
. (<bold>A</bold>
) Number and proportion of MERS
cases by sex and age group; (<bold>B</bold>
) Number of deaths by sex and age group;
(<bold>C</bold>
) Sex-specific CFR by age group; (<bold>D</bold>
) MERS cases by
contact risk factors. *Others include contact risk factors of sheep-linked, camel and
sheep-linked, camel and community-linked and camel, sheep and
hospital-linked.</p>
</caption>
<graphic xlink:href="TEMI_A_12040147_F0001_OC"></graphic>
</fig>
<table-wrap id="T0001" orientation="portrait" position="float"><label>Table 1</label>
<caption><p>Demographics and other characteristics of
laboratory-confirmed MERS cases in the Kingdom of Saudi Arabia (2012–2015) and South
Korea (2015)</p>
</caption>
<pmc-comment>OASIS TABLE HERE</pmc-comment>
<table frame="hsides" rules="groups"><colgroup><col width="1*" align="left"></col>
<col width="1*" align="center"></col>
<col width="1*" align="center"></col>
<col width="1*" align="center"></col>
</colgroup>
<thead><tr><th align="left"><bold>Variables</bold>
</th>
<th align="center"><bold>Kingdom of Saudi Arabia
(<italic>n</italic>
=1186)<xref ref-type="fn" rid="t1-fn2"><sup>a</sup>
</xref>
</bold>
</th>
<th align="center"><bold>South Korea
(<italic>n</italic>
=186)</bold>
</th>
<th align="center"><bold><italic>P</italic>
-value</bold>
</th>
</tr>
</thead>
<tbody><tr><td align="left">Mean age (range)<xref ref-type="fn" rid="t1-fn3"><sup>b</sup>
</xref>
</td>
<td align="center">51 (0–109) years</td>
<td align="center">54 (16–87) years</td>
<td align="center">0.023</td>
</tr>
<tr><td align="left"> </td>
<td align="center"> </td>
<td align="center"> </td>
<td align="center"> </td>
</tr>
<tr><td align="left"><italic>Sex</italic>
<xref ref-type="fn" rid="t1-fn4"><sup>c</sup>
</xref>
</td>
<td align="center"> </td>
<td align="center"> </td>
<td align="center"> </td>
</tr>
<tr><td align="left"> Male</td>
<td align="center">741/1137 (65.2%)</td>
<td align="center">110/186 (59.1%)</td>
<td align="center">0.111</td>
</tr>
<tr><td align="left"> Female</td>
<td align="center">396/1137 (34.8%)</td>
<td align="center">76/186 (40.9%)</td>
<td align="center"> </td>
</tr>
<tr><td align="left"> </td>
<td align="center"> </td>
<td align="center"> </td>
<td align="center"> </td>
</tr>
<tr><td align="left"><italic>Sex-specific fatalities</italic>
<xref ref-type="fn" rid="t1-fn5"><sup>d</sup>
</xref>
</td>
<td align="center"> </td>
<td align="center"> </td>
<td align="center"> </td>
</tr>
<tr><td align="left"> Male</td>
<td align="center">157/741 (21.2%)</td>
<td align="center">24/110 (21.8%)</td>
<td align="center">0.484</td>
</tr>
<tr><td align="left"> Female</td>
<td align="center">60/396 (15.2%)</td>
<td align="center">12/76 (15.8%)</td>
<td align="center">0.550</td>
</tr>
<tr><td align="left">Healthcare worker<xref ref-type="fn" rid="t1-fn6"><sup>e</sup>
</xref>
</td>
<td align="center">157/1166 (13.5%)</td>
<td align="center">26/170 (15.3%)</td>
<td align="center"> </td>
</tr>
<tr><td align="left"> </td>
<td align="center"> </td>
<td align="center"> </td>
<td align="center"> </td>
</tr>
<tr><td align="left"><italic>Comorbidity</italic>
<xref ref-type="fn" rid="t1-fn7"><sup>f</sup>
</xref>
</td>
<td align="center"> </td>
<td align="center"> </td>
<td align="center"> </td>
</tr>
<tr><td align="left"> Male</td>
<td align="center">460/657 (70.0%)</td>
<td align="center">21/35 (60.0%)</td>
<td align="center">0.211</td>
</tr>
<tr><td align="left"> Female</td>
<td align="center">197/657 (30.0%)</td>
<td align="center">14/35 (40.0%)</td>
<td align="center"> </td>
</tr>
<tr><td align="left"> </td>
<td align="center"> </td>
<td align="center"> </td>
<td align="center"> </td>
</tr>
<tr><td align="left"><italic>Contact History</italic>
</td>
<td align="center"> </td>
<td align="center"> </td>
<td align="center"> </td>
</tr>
<tr><td align="left"> Camel-linked</td>
<td align="center">59/1186 (5.0%)</td>
<td align="center">0/186 (0%)</td>
<td align="center">NA</td>
</tr>
<tr><td align="left"> Sheep-linked</td>
<td align="center">5/1186 (0.4%)</td>
<td align="center">0/186 (0%)</td>
<td align="center">NA</td>
</tr>
<tr><td align="left"> Hospital-linked</td>
<td align="center">158/1186 (13.3%)</td>
<td align="center">175/186 (94.1%)</td>
<td align="center"><0.001</td>
</tr>
<tr><td align="left"> Community-linked</td>
<td align="center">245/1186 (20.7%)</td>
<td align="center">6/186 (3.2%)</td>
<td align="center"><0.001</td>
</tr>
<tr><td align="left"> Camel and sheep-linked</td>
<td align="center">6/1186 (0.5%)</td>
<td align="center">0/186 (0%)</td>
<td align="center">NA</td>
</tr>
<tr><td align="left"> Camel and community-linked</td>
<td align="center">2/1186 (0.2%)</td>
<td align="center">0/186 (0%)</td>
<td align="center">NA</td>
</tr>
<tr><td align="left"> Camel, sheep and hospital-linked</td>
<td align="center">1/1186 (0.1%)</td>
<td align="center">0/186 (0%)</td>
<td align="center">NA</td>
</tr>
<tr><td align="left"> Unknown</td>
<td align="center">710/1186 (59.9%)</td>
<td align="center">5/186 (2.7%)</td>
<td align="center"><0.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot><fn id="t1-fn1"><p>Abbreviation: not applicable, NA.</p>
</fn>
<fn id="t1-fn2"><p><sup>a</sup>
Missing data for 113 cases in KSA.</p>
</fn>
<fn id="t1-fn3"><p><sup>b</sup>
Age: 36 cases in KSA were unavailable.</p>
</fn>
<fn id="t1-fn4"><p><sup>c</sup>
Sex: 49 cases in KSA were unavailable.</p>
</fn>
<fn id="t1-fn5"><p><sup>d</sup>
Overall fatalities: 19 cases in KSA were
unavailable.</p>
</fn>
<fn id="t1-fn6"><p><sup>e</sup>
Healthcare Worker: 20 cases in KSA and 16 cases in SK
were unavailable.</p>
</fn>
<fn id="t1-fn7"><p><sup>f</sup>
Comorbidity: 121 cases in KSA and 145 cases in SK were
unavailable.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</p>
<p>MERS cases in KSA had various contact risk factors before symptom onset (<xref rid="F0001" ref-type="fig">Figure 1D</xref>
), including contact with camels and camel products (e.g.,
camel raw milk) (<italic>n</italic>
=59, 5.0%), sheep and sheep products
(<italic>n</italic>
=5, 0.4%), nosocomial MERS cases or hospitals having MERS outbreak
(<italic>n</italic>
=158, 13.3%), and diseased family members or friends
(<italic>n</italic>
=245, 20.7%), and a majority had unknown risk factors
(<italic>n</italic>
=710, 59.9%). In addition, several cases had more than one risk factor,
including contact with both camels and sheep (<italic>n</italic>
=6, 0.5%), camels and
confirmed human cases (<italic>n</italic>
=2, 0.2%), and the mixture of several contact types
(<italic>n</italic>
=1, 0.1%). In SK, most cases had a contact history in hospital
facilities (<italic>n</italic>
=175, 94.1%), whereas only six cases (3.2%) were infected with
MERS-CoV through community contacts. The differences in the rates of community-linked,
hospital-linked or unknown risk factors between KSA and SK were statistically significant
(<italic>P</italic>
<0.001).</p>
<p>On the basis of the reported date of onset of symptoms, the epidemic curve in KSA from 2012
to 2015 by week is shown in <xref rid="F0002" ref-type="fig">Figure 2A</xref>
. During the
four years after the emergence of MERS-CoV, several peaks were observed in week 18 in 2014
and weeks 7, 10, 34 in 2015. In addition, numerous clusters and sporadic cases were noted
with no clear seasonality, comprising a very protracted and mixed pattern. The epidemic
curve of MERS-CoV in SK in 2015 (<xref rid="F0002" ref-type="fig">Figure 2B</xref>
)
exhibited a classic epidemic curve over a short time (8 weeks) with a small number of early
cases beginning in week 20 followed by a rapid rise in new cases over two weeks, an epidemic
peak at week 23 and then a decline in cases ending at week 28.<fig id="F0002" orientation="portrait" position="float"><label>Figure
2</label>
<caption><p>Number of MERS cases by the week of onset of symptoms.
(<bold>A</bold>
) Kingdom of Saudi Arabia, 2012–2015 (<italic>n</italic>
=805);
(<bold>B</bold>
) South Korea, 2015 (<italic>n</italic>
=172).</p>
</caption>
<graphic xlink:href="TEMI_A_12040147_F0002_OC"></graphic>
</fig>
</p>
</sec>
<sec id="sec4"><title>Discussion</title>
<p>We compared the characteristics of 1299 laboratory-confirmed MERS cases in KSA with 186
cases in SK from 2012 to 2015, by using publicly available data. The main differences
between the two countries included the slightly older age of cases in SK, the epidemic
pattern and the risk factors for infection. SK had a predominantly nosocomial transmission
pattern, with >90% of cases acquiring infection in the health system. In contrast, in
KSA, the occurrence of MERS-CoV was characterized by a very mixed epidemic pattern<sup><xref rid="bib15" ref-type="bibr">15</xref>
</sup>
with a diversity of risk factors for
disease, and >60% of cases had no known risk factor for infection. This finding is
consistent with other published studies indicating a high proportion of cases without animal
or nosocomial contact.<sup><xref rid="bib8" ref-type="bibr">8</xref>
, <xref rid="bib15" ref-type="bibr">15</xref>
, <xref rid="bib25" ref-type="bibr">25</xref>
, <xref rid="bib26" ref-type="bibr">26</xref>
</sup>
Interestingly, despite the large difference
in nosocomial cases between SK and KSA, the rate of healthcare worker cases was not
significantly different between countries, thus highlighting the high-occupational health
risk that MERS-CoV poses to health workers. The epidemic pattern in KSA has been
substantially different each year from 2012 to 2015 and exhibited an absence of seasonality,
varying timing of annual peaks and a mixture of sporadic and epidemic patterns. The large
epidemic peaks in KSA were not well explained by the estimated
<italic>R</italic>
<sub>0</sub>
of 0.6–1.3.<sup><xref rid="bib27" ref-type="bibr">27</xref>
, <xref rid="bib28" ref-type="bibr">28</xref>
, <xref rid="bib29" ref-type="bibr">29</xref>
</sup>
In contrast, the epidemic curve in SK was clearly epidemic in
pattern.</p>
<p>The demographic features of cases (such as age and sex) were similar and have been
described previously in KSA<sup><xref rid="bib28" ref-type="bibr">28</xref>
, <xref rid="bib29" ref-type="bibr">29</xref>
, <xref rid="bib30" ref-type="bibr">30</xref>
</sup>
and SK;<sup><xref rid="bib20" ref-type="bibr">20</xref>
, <xref rid="bib21" ref-type="bibr">21</xref>
, <xref rid="bib31" ref-type="bibr">31</xref>
, <xref rid="bib32" ref-type="bibr">32</xref>
</sup>
however, cases were slightly older in SK. In addition to the
higher proportion of male cases with underlying comorbidities, the reasons for males being
more at risk may be related to various socio-cultural behaviors. One possible explanation
for the excess of male cases is that females are more likely to adopt hygienic measures and
health-seeking behaviors, as observed during past influenza pandemics, such as that of
H1N1.<sup><xref rid="bib33" ref-type="bibr">33</xref>
</sup>
Women are also more likely
in KSA to wear face veils, thus potentially decreasing exposure. However, this cultural
practice is not the case in SK, which also had a male predominance.<sup><xref rid="bib34" ref-type="bibr">34</xref>
</sup>
The predominance of male cases may also be
associated with the high prevalence of smoking, particularly in middle-aged males in the
Republic of Korea,<sup><xref rid="bib32" ref-type="bibr">32</xref>
</sup>
which has been
considered an independent risk factor of respiratory infections, such as
pneumonia.<sup><xref rid="bib35" ref-type="bibr">35</xref>
</sup>
However, the
association between smoking and MERS-CoV infection requires more supportive evidence.</p>
<p>Sporadic and clusters of MERS cases had been reported from KSA, which were probably due to
contact with camels and other animals. However, >60% of cases in KSA had no clear history
of exposure to camels or other animals, and a study of camel handlers revealed no evidence
of MERS-CoV infection despite handling of camels with MERS-CoV infection.<sup><xref rid="bib36" ref-type="bibr">36</xref>
</sup>
Despite the epidemic arising in SK from a
single traveler,<sup><xref rid="bib18" ref-type="bibr">18</xref>
</sup>
there have been no
epidemics to date in KSA or other countries arising from mass gatherings, such as the annual
Hajj pilgrimage (week 43 in 2012, week 41 in 2013, week 40 in 2014 and week 39 in 2015), a
phenomenon that is difficult to explain.<sup><xref rid="bib37" ref-type="bibr">37</xref>
</sup>
In addition, previous evidence has demonstrated multiple genetic strains
in a single nosocomial outbreak at Al Ahsa Hospital in KSA,<sup><xref rid="bib38" ref-type="bibr">38</xref>
</sup>
a finding inconsistent with a short-time frame single
epidemic. The identification of multiple genetic variants over a longer period of time may
be expected but is unexpected in a single outbreak occurring within a matter of days or
weeks.</p>
<p>All cases in SK (excluding the index case) were associated with a single chain of
transmission and healthcare facilities.<sup><xref rid="bib18" ref-type="bibr">18</xref>
,
<xref rid="bib39" ref-type="bibr">39</xref>
</sup>
The index case was a 68-year-old man
who traveled to the United Arab Emirates (29–30 April), Saudi Arabia (1–2 May), Qatar (2–3
May), and Bahrain (4 May) and returned to Seoul on the same day without a history of
exposure to camels or contact with MERS cases.<sup><xref rid="bib39" ref-type="bibr">39</xref>
</sup>
His respiratory illness developed on 11 May. He received treatment in
Hospital A (local clinic) on 12, 14 and 15 May; Hospital B (hospitalized) from 15 to 17 May;
and Hospital C and Hospital D (hospitalized) from 17 May. He was diagnosed with MERS-CoV on
20 May.<sup><xref rid="bib20" ref-type="bibr">20</xref>
</sup>
A range of hospitalized
patients, healthcare workers and family members were exposed during this period, thus
leading to the nosocomial disease outbreak in SK. The MERS-CoV isolated from inpatients
formed a single monophyletic clade with high similarity to strains from Riyadh.<sup><xref rid="bib40" ref-type="bibr">40</xref>
</sup>
An unreported sequence was detected in the
MERS-CoV circulating in SK,<sup><xref rid="bib41" ref-type="bibr">41</xref>
</sup>
thus
suggesting increased genetic variability and mutation rates during the outbreak in 2015.</p>
<p>This study is limited by missing data and the use of only publicly available data. Of the
1299 cases in KSA, ~10% (113 of 1299) of cases were excluded, owing to missing data for all
variables; thus, our database represented 90% of reported laboratory-confirmed cases in KSA.
Despite our best efforts to obtain information through various public resources, the data
still contained gaps for some variables in KSA (age: 36 of 1186, 3%; sex: 49 of 1186, 4%;
fatality: 19 of 1186, 2%; healthcare worker: 20 of 1186, 2%; comorbidity: 121 of 1186, 10%)
and SK (healthcare worker: 16 of 186, 9%; comorbidity: 145 of 186, 78%). In this study, we
classified the contact history into five types, including camel-linked, sheep-linked,
hospital-linked, community-linked and unknown. The unknown was defined as the MERS cases
with no contact history or cases in which the investigation was ongoing. The corresponding
data were taken from official websites, including those of the WHO and the Saudi MOH, as
well as local news and medical reports. However, we were unable to identify specified risk
factors or contact history for these 710 cases of unknown type. In addition, there appeared
to be some inconsistencies in reporting that affected this study, because publicly available
data were used. The fatalities calculated in this analysis totaled 228 cases with a CFR of
19%, a value different from that reported by the Saudi MOH (43%).<sup><xref rid="bib42" ref-type="bibr">42</xref>
</sup>
This difference may be due to the incomplete information of
deaths provided by Saudi MOH at the time. If more complete data become available, further
analysis on mortality-related risk factors is recommended. This study highlights the value
of publicly available data to improve understanding of complex emerging infections. We were
able to consolidate and enhance available data through searching multiple data sources, but
there is a global need for making high-quality disease surveillance data publicly available
on a routine basis for transparency and enhanced disease control efforts.</p>
<p>The transmission in SK was found to be predominantly hospital linked and a classic epidemic
over a short period of time, thus highlighting the importance of hospital triage protocols
for treating emerging infectious diseases. In the case of SK, low awareness of MERS-CoV at
the index hospital and poor triage and infection control resulted in a large outbreak, which
might have otherwise been prevented. We ranked the risk of travel-acquired MERS-CoV
infections by country, on the basis of airline travel patterns and frequencies, which can be
used to prioritize hospital screening protocols for countries at highest risk.<sup><xref rid="bib26" ref-type="bibr">26</xref>
</sup>
SK was in the top 50 countries at risk in
this analysis and ranked at number 38. In contrast, India ranked first but has not
experienced an imported case of MERS-CoV. This information again highlights the need for a
global approach to mitigate the spread of emerging infectious diseases and the risk that all
countries face in an interconnected world of travel-related epidemics.</p>
<p>In conclusion, on the basis of the comparative epidemiology of MERS-CoV in KSA and SK, both
countries had differing risk factors and epidemic patterns, thus adding to the complexity of
this disease. The varying and complex epidemiology in KSA is consistent with multiple
introductions, which may comprise a mix of animal-to-human, human-to-human
(healthcare-acquired, community-acquired), and other modes of transmission. A large
proportion of KSA cases have unknown exposure, thus warranting further study. MERS-CoV has
an epidemic pattern that has varied from country to country and has disproportionately
affected KSA for reasons that are not yet fully understood. Further research, enabled by
high quality surveillance data, is required to understand and mitigate the risk factors for
MERS-CoV.</p>
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