Epidemic Models of Contact Tracing: Systematic Review of Transmission Studies of Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome
Identifieur interne : 001721 ( Pmc/Corpus ); précédent : 001720; suivant : 001722Epidemic Models of Contact Tracing: Systematic Review of Transmission Studies of Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome
Auteurs : Kin On Kwok ; Arthur Tang ; Vivian W. I. Wei ; Woo Hyun Park ; Eng Kiong Yeoh ; Steven RileySource :
- Computational and Structural Biotechnology Journal [ 2001-0370 ] ; 2019.
Abstract
The emergence and reemergence of coronavirus epidemics sparked renewed concerns from global epidemiology researchers and public health administrators. Mathematical models that represented how contact tracing and follow-up may control Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) transmissions were developed for evaluating different infection control interventions, estimating likely number of infections as well as facilitating understanding of their likely epidemiology. We reviewed mathematical models for contact tracing and follow-up control measures of SARS and MERS transmission. Model characteristics, epidemiological parameters and intervention parameters used in the mathematical models from seven studies were summarized. A major concern identified in future epidemics is whether public health administrators can collect all the required data for building epidemiological models in a short period of time during the early phase of an outbreak. Also, currently available models do not explicitly model constrained resources. We urge for closed-loop communication between public health administrators and modelling researchers to come up with guidelines to delineate the collection of the required data in the midst of an outbreak and the inclusion of additional logistical details in future similar models.
Url:
DOI: 10.1016/j.csbj.2019.01.003
PubMed: 30809323
PubMed Central: 6376160
Links to Exploration step
PMC:6376160Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Epidemic Models of Contact Tracing: Systematic Review of Transmission Studies of Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome</title><author><name sortKey="Kwok, Kin On" sort="Kwok, Kin On" uniqKey="Kwok K" first="Kin On" last="Kwok">Kin On Kwok</name><affiliation><nlm:aff id="af0005">The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China</nlm:aff></affiliation><affiliation><nlm:aff id="af0010">Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region, China</nlm:aff></affiliation><affiliation><nlm:aff id="af0015">Shenzhen Research Institute of The Chinese University of Hong Kong, Shenzhen, China</nlm:aff></affiliation></author><author><name sortKey="Tang, Arthur" sort="Tang, Arthur" uniqKey="Tang A" first="Arthur" last="Tang">Arthur Tang</name><affiliation><nlm:aff id="af0020">Department of Software, Sungkyunkwan University, South Korea</nlm:aff></affiliation></author><author><name sortKey="Wei, Vivian W I" sort="Wei, Vivian W I" uniqKey="Wei V" first="Vivian W. I." last="Wei">Vivian W. I. Wei</name><affiliation><nlm:aff id="af0005">The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China</nlm:aff></affiliation></author><author><name sortKey="Park, Woo Hyun" sort="Park, Woo Hyun" uniqKey="Park W" first="Woo Hyun" last="Park">Woo Hyun Park</name><affiliation><nlm:aff id="af0025">Department of Electrical and Computer Engineering, Sungkyunkwan University, South Korea</nlm:aff></affiliation></author><author><name sortKey="Yeoh, Eng Kiong" sort="Yeoh, Eng Kiong" uniqKey="Yeoh E" first="Eng Kiong" last="Yeoh">Eng Kiong Yeoh</name><affiliation><nlm:aff id="af0005">The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China</nlm:aff></affiliation></author><author><name sortKey="Riley, Steven" sort="Riley, Steven" uniqKey="Riley S" first="Steven" last="Riley">Steven Riley</name><affiliation><nlm:aff id="af0030">MRC Centre for Outbreak Analysis and Modelling, Department for Infectious Disease Epidemiology, Imperial College London, United Kingdom</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">30809323</idno><idno type="pmc">6376160</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6376160</idno><idno type="RBID">PMC:6376160</idno><idno type="doi">10.1016/j.csbj.2019.01.003</idno><date when="2019">2019</date><idno type="wicri:Area/Pmc/Corpus">001721</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001721</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Epidemic Models of Contact Tracing: Systematic Review of Transmission Studies of Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome</title><author><name sortKey="Kwok, Kin On" sort="Kwok, Kin On" uniqKey="Kwok K" first="Kin On" last="Kwok">Kin On Kwok</name><affiliation><nlm:aff id="af0005">The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China</nlm:aff></affiliation><affiliation><nlm:aff id="af0010">Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region, China</nlm:aff></affiliation><affiliation><nlm:aff id="af0015">Shenzhen Research Institute of The Chinese University of Hong Kong, Shenzhen, China</nlm:aff></affiliation></author><author><name sortKey="Tang, Arthur" sort="Tang, Arthur" uniqKey="Tang A" first="Arthur" last="Tang">Arthur Tang</name><affiliation><nlm:aff id="af0020">Department of Software, Sungkyunkwan University, South Korea</nlm:aff></affiliation></author><author><name sortKey="Wei, Vivian W I" sort="Wei, Vivian W I" uniqKey="Wei V" first="Vivian W. I." last="Wei">Vivian W. I. Wei</name><affiliation><nlm:aff id="af0005">The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China</nlm:aff></affiliation></author><author><name sortKey="Park, Woo Hyun" sort="Park, Woo Hyun" uniqKey="Park W" first="Woo Hyun" last="Park">Woo Hyun Park</name><affiliation><nlm:aff id="af0025">Department of Electrical and Computer Engineering, Sungkyunkwan University, South Korea</nlm:aff></affiliation></author><author><name sortKey="Yeoh, Eng Kiong" sort="Yeoh, Eng Kiong" uniqKey="Yeoh E" first="Eng Kiong" last="Yeoh">Eng Kiong Yeoh</name><affiliation><nlm:aff id="af0005">The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China</nlm:aff></affiliation></author><author><name sortKey="Riley, Steven" sort="Riley, Steven" uniqKey="Riley S" first="Steven" last="Riley">Steven Riley</name><affiliation><nlm:aff id="af0030">MRC Centre for Outbreak Analysis and Modelling, Department for Infectious Disease Epidemiology, Imperial College London, United Kingdom</nlm:aff></affiliation></author></analytic><series><title level="j">Computational and Structural Biotechnology Journal</title><idno type="eISSN">2001-0370</idno><imprint><date when="2019">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>The emergence and reemergence of coronavirus epidemics sparked renewed concerns from global epidemiology researchers and public health administrators. Mathematical models that represented how contact tracing and follow-up may control Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) transmissions were developed for evaluating different infection control interventions, estimating likely number of infections as well as facilitating understanding of their likely epidemiology. We reviewed mathematical models for contact tracing and follow-up control measures of SARS and MERS transmission. Model characteristics, epidemiological parameters and intervention parameters used in the mathematical models from seven studies were summarized. A major concern identified in future epidemics is whether public health administrators can collect all the required data for building epidemiological models in a short period of time during the early phase of an outbreak. Also, currently available models do not explicitly model constrained resources. We urge for closed-loop communication between public health administrators and modelling researchers to come up with guidelines to delineate the collection of the required data in the midst of an outbreak and the inclusion of additional logistical details in future similar models.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Who" uniqKey="Who">WHO</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Who" uniqKey="Who">WHO</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chowell, G" uniqKey="Chowell G">G. Chowell</name></author><author><name sortKey="Abdirizak, F" uniqKey="Abdirizak F">F. Abdirizak</name></author><author><name sortKey="Lee, S" uniqKey="Lee S">S. Lee</name></author><author><name sortKey="Lee, J" uniqKey="Lee J">J. Lee</name></author><author><name sortKey="Jung, E" uniqKey="Jung E">E. Jung</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Uscdc" uniqKey="Uscdc">USCDC</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Who" uniqKey="Who">WHO</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chan, J F" uniqKey="Chan J">J.F. Chan</name></author><author><name sortKey="Lau, S K" uniqKey="Lau S">S.K. Lau</name></author><author><name sortKey="To Kk" uniqKey="To Kk">To KK</name></author><author><name sortKey="Cheng, V C" uniqKey="Cheng V">V.C. Cheng</name></author><author><name sortKey="Woo, P C" uniqKey="Woo P">P.C. Woo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ahmed, A E" uniqKey="Ahmed A">A.E. Ahmed</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hui, D S" uniqKey="Hui D">D.S. Hui</name></author><author><name sortKey="Chan, M C" uniqKey="Chan M">M.C. Chan</name></author><author><name sortKey="Wu, A K" uniqKey="Wu A">A.K. Wu</name></author><author><name sortKey="Ng, P C" uniqKey="Ng P">P.C. Ng</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Muller, J" uniqKey="Muller J">J. Muller</name></author><author><name sortKey="Kretzschmar, M" uniqKey="Kretzschmar M">M. Kretzschmar</name></author><author><name sortKey="Dietz, K" uniqKey="Dietz K">K. Dietz</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Riley, S" uniqKey="Riley S">S. Riley</name></author><author><name sortKey="Fraser, C" uniqKey="Fraser C">C. Fraser</name></author><author><name sortKey="Donnelly, C A" uniqKey="Donnelly C">C.A. Donnelly</name></author><author><name sortKey="Ghani, A C" uniqKey="Ghani A">A.C. Ghani</name></author><author><name sortKey="Abu Raddad, L J" uniqKey="Abu Raddad L">L.J. Abu-Raddad</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Saurabh, S" uniqKey="Saurabh S">S. Saurabh</name></author><author><name sortKey="Prateek, S" uniqKey="Prateek S">S. Prateek</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Eames, K T" uniqKey="Eames K">K.T. Eames</name></author><author><name sortKey="Keeling, M J" uniqKey="Keeling M">M.J. Keeling</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lee, M S" uniqKey="Lee M">M.S. Lee</name></author><author><name sortKey="Hu, A Y" uniqKey="Hu A">A.Y. Hu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Moher, D" uniqKey="Moher D">D. Moher</name></author><author><name sortKey="Shamseer, L" uniqKey="Shamseer L">L. Shamseer</name></author><author><name sortKey="Clarke, M" uniqKey="Clarke M">M. Clarke</name></author><author><name sortKey="Ghersi, D" uniqKey="Ghersi D">D. Ghersi</name></author><author><name sortKey="Liberati, A" uniqKey="Liberati A">A. Liberati</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Peak, C M" uniqKey="Peak C">C.M. Peak</name></author><author><name sortKey="Childs, L M" uniqKey="Childs L">L.M. Childs</name></author><author><name sortKey="Grad, Y H" uniqKey="Grad Y">Y.H. Grad</name></author><author><name sortKey="Buckee, C O" uniqKey="Buckee C">C.O. Buckee</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Feng, Z" uniqKey="Feng Z">Z. Feng</name></author><author><name sortKey="Yang, Y" uniqKey="Yang Y">Y. Yang</name></author><author><name sortKey="Xu, D" uniqKey="Xu D">D. Xu</name></author><author><name sortKey="Zhang, P" uniqKey="Zhang P">P. Zhang</name></author><author><name sortKey="Mccauley, M M" uniqKey="Mccauley M">M.M. McCauley</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Klinkenberg, D" uniqKey="Klinkenberg D">D. Klinkenberg</name></author><author><name sortKey="Fraser, C" uniqKey="Fraser C">C. Fraser</name></author><author><name sortKey="Heesterbeek, H" uniqKey="Heesterbeek H">H. Heesterbeek</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chen, S C" uniqKey="Chen S">S.C. Chen</name></author><author><name sortKey="Chang, C F" uniqKey="Chang C">C.F. Chang</name></author><author><name sortKey="Liao, C M" uniqKey="Liao C">C.M. Liao</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fraser, C" uniqKey="Fraser C">C. Fraser</name></author><author><name sortKey="Riley, S" uniqKey="Riley S">S. Riley</name></author><author><name sortKey="Anderson, R M" uniqKey="Anderson R">R.M. Anderson</name></author><author><name sortKey="Ferguson, N M" uniqKey="Ferguson N">N.M. Ferguson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lloyd Smith, J O" uniqKey="Lloyd Smith J">J.O. Lloyd-Smith</name></author><author><name sortKey="Galvani, A P" uniqKey="Galvani A">A.P. Galvani</name></author><author><name sortKey="Getz, W M" uniqKey="Getz W">W.M. Getz</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Becker, N G" uniqKey="Becker N">N.G. Becker</name></author><author><name sortKey="Glass, K" uniqKey="Glass K">K. Glass</name></author><author><name sortKey="Li, Z" uniqKey="Li Z">Z. Li</name></author><author><name sortKey="Aldis, G K" uniqKey="Aldis G">G.K. Aldis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Anderson, R M M R M" uniqKey="Anderson R">R.M.M.R.M. Anderson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kirch, W" uniqKey="Kirch W">W. Kirch</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kirch, W" uniqKey="Kirch W">W. Kirch</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wilder Smith, A" uniqKey="Wilder Smith A">A. Wilder-Smith</name></author><author><name sortKey="Teleman, M D" uniqKey="Teleman M">M.D. Teleman</name></author><author><name sortKey="Heng, B H" uniqKey="Heng B">B.H. Heng</name></author><author><name sortKey="Earnest, A" uniqKey="Earnest A">A. Earnest</name></author><author><name sortKey="Ling, A E" uniqKey="Ling A">A.E. Ling</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Omrani, A S" uniqKey="Omrani A">A.S. Omrani</name></author><author><name sortKey="Matin, M A" uniqKey="Matin M">M.A. Matin</name></author><author><name sortKey="Haddad, Q" uniqKey="Haddad Q">Q. Haddad</name></author><author><name sortKey="Al Nakhli, D" uniqKey="Al Nakhli D">D. Al-Nakhli</name></author><author><name sortKey="Memish, Z A" uniqKey="Memish Z">Z.A. Memish</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Donnelly, C A" uniqKey="Donnelly C">C.A. Donnelly</name></author><author><name sortKey="Ghani, A C" uniqKey="Ghani A">A.C. Ghani</name></author><author><name sortKey="Leung, G M" uniqKey="Leung G">G.M. Leung</name></author><author><name sortKey="Hedley, A J" uniqKey="Hedley A">A.J. Hedley</name></author><author><name sortKey="Fraser, C" uniqKey="Fraser C">C. Fraser</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kwok, K O" uniqKey="Kwok K">K.O. Kwok</name></author><author><name sortKey="Leung, G M" uniqKey="Leung G">G.M. Leung</name></author><author><name sortKey="Lam, W Y" uniqKey="Lam W">W.Y. Lam</name></author><author><name sortKey="Riley, S" uniqKey="Riley S">S. Riley</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Majumder, M S" uniqKey="Majumder M">M.S. Majumder</name></author><author><name sortKey="Brownstein, J S" uniqKey="Brownstein J">J.S. Brownstein</name></author><author><name sortKey="Finkelstein, S N" uniqKey="Finkelstein S">S.N. Finkelstein</name></author><author><name sortKey="Larson, R C" uniqKey="Larson R">R.C. Larson</name></author><author><name sortKey="Bourouiba, L" uniqKey="Bourouiba L">L. Bourouiba</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Day, T" uniqKey="Day T">T. Day</name></author><author><name sortKey="Park, A" uniqKey="Park A">A. Park</name></author><author><name sortKey="Madras, N" uniqKey="Madras N">N. Madras</name></author><author><name sortKey="Gumel, A" uniqKey="Gumel A">A. Gumel</name></author><author><name sortKey="Wu, J" uniqKey="Wu J">J. Wu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rivers, C M" uniqKey="Rivers C">C.M. Rivers</name></author><author><name sortKey="Lofgren, E T" uniqKey="Lofgren E">E.T. Lofgren</name></author><author><name sortKey="Marathe, M" uniqKey="Marathe M">M. Marathe</name></author><author><name sortKey="Eubank, S" uniqKey="Eubank S">S. Eubank</name></author><author><name sortKey="Lewis, B L" uniqKey="Lewis B">B.L. Lewis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Webb, G" uniqKey="Webb G">G. Webb</name></author><author><name sortKey="Browne, C" uniqKey="Browne C">C. Browne</name></author><author><name sortKey="Huo, X" uniqKey="Huo X">X. Huo</name></author><author><name sortKey="Seydi, O" uniqKey="Seydi O">O. Seydi</name></author><author><name sortKey="Seydi, M" uniqKey="Seydi M">M. Seydi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kuk, A Y" uniqKey="Kuk A">A.Y. Kuk</name></author><author><name sortKey="Ma, S" uniqKey="Ma S">S. Ma</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pitzer, V E" uniqKey="Pitzer V">V.E. Pitzer</name></author><author><name sortKey="Leung, G M" uniqKey="Leung G">G.M. Leung</name></author><author><name sortKey="Lipsitch, M" uniqKey="Lipsitch M">M. Lipsitch</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wearing, H J" uniqKey="Wearing H">H.J. Wearing</name></author><author><name sortKey="Rohani, P" uniqKey="Rohani P">P. Rohani</name></author><author><name sortKey="Keeling, M J" uniqKey="Keeling M">M.J. Keeling</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chowell, G" uniqKey="Chowell G">G. Chowell</name></author><author><name sortKey="Nishiura, H" uniqKey="Nishiura H">H. Nishiura</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lloyd, A L" uniqKey="Lloyd A">A.L. Lloyd</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wilkinson, R R" uniqKey="Wilkinson R">R.R. Wilkinson</name></author><author><name sortKey="Sharkey, K J" uniqKey="Sharkey K">K.J. Sharkey</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kaplan, E H" uniqKey="Kaplan E">E.H. Kaplan</name></author><author><name sortKey="Craft, D L" uniqKey="Craft D">D.L. Craft</name></author><author><name sortKey="Wein, L M" uniqKey="Wein L">L.M. Wein</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Halloran, M E" uniqKey="Halloran M">M.E. Halloran</name></author><author><name sortKey="Longini, I M" uniqKey="Longini I">I.M. Longini</name></author><author><name sortKey="Nizam, A" uniqKey="Nizam A">A. Nizam</name></author><author><name sortKey="Yang, Y" uniqKey="Yang Y">Y. Yang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kwok, K O" uniqKey="Kwok K">K.O. Kwok</name></author><author><name sortKey="Cowling, B" uniqKey="Cowling B">B. Cowling</name></author><author><name sortKey="Wei, V" uniqKey="Wei V">V. Wei</name></author><author><name sortKey="Riley, S" uniqKey="Riley S">S. Riley</name></author><author><name sortKey="Read, J M" uniqKey="Read J">J.M. Read</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kwok, K O" uniqKey="Kwok K">K.O. Kwok</name></author><author><name sortKey="Cowling, B J" uniqKey="Cowling B">B.J. Cowling</name></author><author><name sortKey="Wei, V W" uniqKey="Wei V">V.W. Wei</name></author><author><name sortKey="Wu, K M" uniqKey="Wu K">K.M. Wu</name></author><author><name sortKey="Read, J M" uniqKey="Read J">J.M. Read</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Huerta, R" uniqKey="Huerta R">R. Huerta</name></author><author><name sortKey="Tsimring, L S" uniqKey="Tsimring L">L.S. Tsimring</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Galvani, A P" uniqKey="Galvani A">A.P. Galvani</name></author><author><name sortKey="Lei, X" uniqKey="Lei X">X. Lei</name></author><author><name sortKey="Jewell, N P" uniqKey="Jewell N">N.P. Jewell</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mckinney, K R" uniqKey="Mckinney K">K.R. McKinney</name></author><author><name sortKey="Gong, Y Y" uniqKey="Gong Y">Y.Y. Gong</name></author><author><name sortKey="Lewis, T G" uniqKey="Lewis T">T.G. Lewis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Yu, I T" uniqKey="Yu I">I.T. Yu</name></author><author><name sortKey="Li, Y" uniqKey="Li Y">Y. Li</name></author><author><name sortKey="Wong, T W" uniqKey="Wong T">T.W. Wong</name></author><author><name sortKey="Tam, W" uniqKey="Tam W">W. Tam</name></author><author><name sortKey="Chan, A T" uniqKey="Chan A">A.T. Chan</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="review-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Comput Struct Biotechnol J</journal-id><journal-id journal-id-type="iso-abbrev">Comput Struct Biotechnol J</journal-id><journal-title-group><journal-title>Computational and Structural Biotechnology Journal</journal-title></journal-title-group><issn pub-type="epub">2001-0370</issn><publisher><publisher-name>The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">30809323</article-id><article-id pub-id-type="pmc">6376160</article-id><article-id pub-id-type="publisher-id">S2001-0370(18)30170-3</article-id><article-id pub-id-type="doi">10.1016/j.csbj.2019.01.003</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Epidemic Models of Contact Tracing: Systematic Review of Transmission Studies of Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome</article-title></title-group><contrib-group><contrib contrib-type="author" id="au0005"><name><surname>Kwok</surname><given-names>Kin On</given-names></name><email>kkokwok@cuhk.edu.hk</email><xref rid="af0005" ref-type="aff">a</xref><xref rid="af0010" ref-type="aff">b</xref><xref rid="af0015" ref-type="aff">c</xref><xref rid="cr0005" ref-type="corresp">⁎</xref><xref rid="fn0005" ref-type="fn">1</xref></contrib><contrib contrib-type="author" id="au0010"><name><surname>Tang</surname><given-names>Arthur</given-names></name><email>atang@skku.edu</email><xref rid="af0020" ref-type="aff">d</xref><xref rid="cr0010" ref-type="corresp">⁎⁎</xref><xref rid="fn0005" ref-type="fn">1</xref></contrib><contrib contrib-type="author" id="au0015"><name><surname>Wei</surname><given-names>Vivian W.I.</given-names></name><xref rid="af0005" ref-type="aff">a</xref></contrib><contrib contrib-type="author" id="au0020"><name><surname>Park</surname><given-names>Woo Hyun</given-names></name><xref rid="af0025" ref-type="aff">e</xref></contrib><contrib contrib-type="author" id="au0025"><name><surname>Yeoh</surname><given-names>Eng Kiong</given-names></name><xref rid="af0005" ref-type="aff">a</xref></contrib><contrib contrib-type="author" id="au0030"><name><surname>Riley</surname><given-names>Steven</given-names></name><xref rid="af0030" ref-type="aff">f</xref></contrib></contrib-group><aff id="af0005"><label>a</label>The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China</aff><aff id="af0010"><label>b</label>Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region, China</aff><aff id="af0015"><label>c</label>Shenzhen Research Institute of The Chinese University of Hong Kong, Shenzhen, China</aff><aff id="af0020"><label>d</label>Department of Software, Sungkyunkwan University, South Korea</aff><aff id="af0025"><label>e</label>Department of Electrical and Computer Engineering, Sungkyunkwan University, South Korea</aff><aff id="af0030"><label>f</label>MRC Centre for Outbreak Analysis and Modelling, Department for Infectious Disease Epidemiology, Imperial College London, United Kingdom</aff><author-notes><corresp id="cr0005"><label>⁎</label>Correspondence to: Kwok Kin On, Room 416, 4/F, JC School of Public Health and Primary Care Building, Prince of Wales Hospital, Shatin, New Territories, Hong Kong Special Administrative Region, China <email>kkokwok@cuhk.edu.hk</email></corresp><corresp id="cr0010"><label>⁎⁎</label>Correspondence to: Arthur Tang, Natural Science Campus, 2066 Seobu-ro, Jangan-gu Suwon, Gyeonggi-do 16419, South Korea <email>atang@skku.edu</email></corresp><fn id="fn0005"><label>1</label><p id="np0005">Joint first author</p></fn></author-notes><pub-date pub-type="pmc-release"><day>26</day><month>1</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"><year>2019</year></pub-date><pub-date pub-type="epub"><day>26</day><month>1</month><year>2019</year></pub-date><volume>17</volume><fpage>186</fpage><lpage>194</lpage><history><date date-type="received"><day>22</day><month>9</month><year>2018</year></date><date date-type="rev-recd"><day>14</day><month>1</month><year>2019</year></date><date date-type="accepted"><day>19</day><month>1</month><year>2019</year></date></history><permissions><copyright-statement>© 2019 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.</copyright-statement><copyright-year>2019</copyright-year><copyright-holder></copyright-holder><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="ab0005"><p>The emergence and reemergence of coronavirus epidemics sparked renewed concerns from global epidemiology researchers and public health administrators. Mathematical models that represented how contact tracing and follow-up may control Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) transmissions were developed for evaluating different infection control interventions, estimating likely number of infections as well as facilitating understanding of their likely epidemiology. We reviewed mathematical models for contact tracing and follow-up control measures of SARS and MERS transmission. Model characteristics, epidemiological parameters and intervention parameters used in the mathematical models from seven studies were summarized. A major concern identified in future epidemics is whether public health administrators can collect all the required data for building epidemiological models in a short period of time during the early phase of an outbreak. Also, currently available models do not explicitly model constrained resources. We urge for closed-loop communication between public health administrators and modelling researchers to come up with guidelines to delineate the collection of the required data in the midst of an outbreak and the inclusion of additional logistical details in future similar models.</p></abstract><kwd-group id="ks0005"><title>Keywords</title><kwd>Contact Tracing</kwd><kwd>Coronavirus Epidemics</kwd><kwd>Transmission Modelling</kwd><kwd>SARS</kwd><kwd>MERS</kwd></kwd-group><kwd-group id="ks0010"><title>Abbreviations</title><kwd>Co-V, Coronavirus</kwd><kwd>MERS, Middle East Respiratory Syndrome</kwd><kwd>R<sub>0</sub>, Basic reproduction number</kwd><kwd>SARS, Severe Acute Respiratory Syndrome</kwd><kwd>SEIR, Susceptible Exposed Infectious Recovered</kwd><kwd>WHO, World Health Organization</kwd></kwd-group></article-meta></front><body><sec id="s0005"><label>1</label><title>Introduction</title><p id="p0005">In the 21st century, there were three large-scale outbreaks in human populations caused by emerging coronaviruses (Co-Vs): (i) Severe Acute Respiratory Syndrome (SARS) outbreak in 2003; (ii) Middle East Respiratory Syndrome (MERS) outbreak in 2012 primarily in the Middle East Saudi Arabian Peninsula region; and (iii) MERS outbreak in 2015 primarily in South Korea. Since their emergence, World Health Organization (WHO) had been notified of more than 8000 confirmed cases of SARS in 26 countries [<xref rid="bb0005" ref-type="bibr">1</xref>] and more than 2200 confirmed cases of MERS in 27 countries [<xref rid="bb0010" ref-type="bibr">2</xref>].</p><p id="p0010">SARS and MERS were both considered as “fast-course” infectious diseases given their relatively short infectious period. However, overall transmission potential for MERS is lower compared to SARS [<xref rid="bb0015" ref-type="bibr">3</xref>] and its outbreaks have been contained with much lower cumulative numbers of infected individuals than was the case for SARS. Prior studies showed that human-to-human transmission of SARS occurred via close contact and respiratory droplets [<xref rid="bb0020" ref-type="bibr">4</xref>], while that of MERS occurred via close contact only [<xref rid="bb0025" ref-type="bibr">5</xref>]. A large proportion of MERS cases were clustered in healthcare settings, most of which were contributed by unprotected close contact between healthcare workers and infected MERS patients. Previous studies have also highlighted the transmission heterogeneity between SARS and MERS. Higher transmission heterogeneity for MERS in the distribution of secondary cases than SARS highlighted the largest outbreak of MERS with sharper incidence peaks [<xref rid="bb0015" ref-type="bibr">3</xref>]. The majority of SARS cases occurred among healthcare workers; while substantial number of MERS cases were patients [<xref rid="bb0015" ref-type="bibr">3</xref>], with most MERS severe cases and mortality being individuals with comorbidities [<xref rid="bb0030" ref-type="bibr">6</xref>]. On the other hand, there were similarities between the two Co-Vs. Diseases caused by them were deadly, with a mortality rate of 29.8% for MERS [<xref rid="bb0035" ref-type="bibr">7</xref>] and 7% for SARS [<xref rid="bb0040" ref-type="bibr">8</xref>].</p><p id="p0015">Contact tracing, the identification and follow-up of individuals who have had contacts with infectious individuals, is a critical process to ensure the best possible chance of control and the longest possible time to local take-off [<xref rid="bb0045" ref-type="bibr">9</xref>]. Contact tracing and follow-up control measures such as quarantine and isolation were crucially important during the SARS outbreak in 2003 [<xref rid="bb0050" ref-type="bibr">10</xref>], the Ebola outbreak in Africa in 2014 [<xref rid="bb0055" ref-type="bibr">11</xref>], as well as its part in the eradication of smallpox [<xref rid="bb0060" ref-type="bibr">12</xref>]. With current advances in vaccine development technologies, the role of contact tracing and follow-up control measures in the initial stage of an epidemic becomes especially important. For some novel pathogens such as pandemic influenza, cutting-edge technology may shorten the time needed for vaccine development after initial isolation, bridging shorter gaps between epidemic emergence and vaccine availability [<xref rid="bb0065" ref-type="bibr">13</xref>]. Should such improvements in vaccine development occur, the potential marginal benefits of improving contact tracing processes will be substantial.</p><p id="p0020">Mathematical models were developed to study the dynamics of SARS and MERS transmissions. Models that explicitly represented how contact tracing and follow-up control measures affected the epidemic dynamics were useful for evaluating different infection control interventions, evaluating burden of infection as well as facilitating further understanding of their epidemiology. Such models have direct utility in planning for future outbreaks of coronaviruses: they can be used to estimate the scale of resources required to conduct effective contact tracing.</p><p id="p0025">In light of this, we conducted a systematic review of mathematical models for contact tracing and follow-up control measures of SARS and MERS transmission. The aims of this review are to (i) provide an overview of contact tracing and follow-up control measures of SARS and MERS transmission gained through mathematical modelling, (ii) to identify future research direction in this area and (iii) to improve future models by addressing current models' deficiencies.</p></sec><sec id="s0010"><label>2</label><title>Methods</title><p id="p0030">To identify articles for the current study, an initial search using the PUBMED/Medline and SCOPUS databases was conducted on 27<sup>th</sup> October 2018 using the following search terms:<list list-type="simple" id="l0005"><list-item id="li0005"><label>a.</label><p id="p0035">(“Contact Tracing” OR “Contact Investigation” OR “Contact Screening”) AND</p></list-item><list-item id="li0010"><label>b.</label><p id="p0040">(“model” OR “modelling” OR “modelling”) AND</p></list-item><list-item id="li0015"><label>c.</label><p id="p0045">(“SARS” OR “MERS” OR “Middle East Respiratory Syndrome” OR “Severe Acute Respiratory Syndrome”)</p></list-item></list></p><sec id="s0015"><label>2.1</label><title>Article Selection Criteria</title><p id="p0050">Reviewers used the following selection criteria to include eligible articles:<list list-type="simple" id="l0010"><list-item id="li0020"><label>a.</label><p id="p0055">Transmission dynamics modelling studies of SARS or MERS in human populations;</p></list-item><list-item id="li0025"><label>b.</label><p id="p0060">Model(s) incorporating contact tracing interventions, case contacts finding, quarantine, contact tracing data, population stratification or that used a heterogeneous contact structure;</p></list-item><list-item id="li0030"><label>c.</label><p id="p0065">Model(s) not explicitly discussing contact tracing and follow-up control measures were excluded.</p></list-item><list-item id="li0035"><label>d.</label><p id="p0070">Articles not in English were excluded.</p></list-item></list></p></sec><sec id="s0020"><label>2.2</label><title>Article Selection</title><p id="p0075">Two independent reviewers (KOK, AT) screened the titles and abstracts of articles obtained from the initial search and excluded articles that did not fit the selection criteria. The two reviewers then read the full text of the remaining articles, and further excluded articles that did not fit the selection criteria. Finally, reference lists of the included articles were extracted, and the titles and abstracts of these articles from the reference lists were reviewed by the two reviewers based on the article selection criteria. Articles from the extracted reference lists that fit the selection criteria were also included in the current study. The flow diagram of the search process and the result are shown in <xref rid="f0005" ref-type="fig">Fig. 1</xref>.<fig id="f0005"><label>Fig. 1</label><caption><p>Flow diagram of the selection process.</p></caption><alt-text id="al0005">Fig. 1</alt-text><graphic xlink:href="gr1_lrg"></graphic></fig></p><p id="p0080">The current systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis: The PRISMA statement [<xref rid="bb0070" ref-type="bibr">14</xref>].</p></sec><sec id="s0025"><label>2.3</label><title>Data Extraction</title><p id="p0085">A standard data extraction form was adopted to extract information from each article, including basic information of the article, characteristics of the study and parameters incorporated in the model.</p></sec></sec><sec id="s0030"><label>3</label><title>Results</title><p id="p0090">Twenty-five articles were identified from the PUBMED/Medline database, and twenty-seven articles were identified from the SCOPUS database with the initial search. Two articles from the PUBMED/Medline database and three articles from the SCOPUS database were excluded because they were not written in English. Sixteen articles were duplicated from the two databases, and thirty-one unique articles were identified from the two databases for assessment of their abstracts using the selection criteria. Eighteen articles were excluded based on their titles and abstracts. Seven articles were further excluded after reading the full text, leaving six articles to be included in the current study. Titles and abstracts of the references of these six articles were further reviewed, and one additional article was included. Therefore, the current study included seven articles for review [<xref rid="bb0075" ref-type="bibr">[15]</xref>, <xref rid="bb0080" ref-type="bibr">[16]</xref>, <xref rid="bb0085" ref-type="bibr">[17]</xref>, <xref rid="bb0090" ref-type="bibr">[18]</xref>, <xref rid="bb0095" ref-type="bibr">[19]</xref>, <xref rid="bb0100" ref-type="bibr">[20]</xref>, <xref rid="bb0105" ref-type="bibr">[21]</xref>].</p><sec id="s0035"><label>3.1</label><title>Characteristics of the Included Studies</title><p id="p0095">Among the seven included studies, four [<xref rid="bb0075" ref-type="bibr">15</xref>,<xref rid="bb0085" ref-type="bibr">17</xref>,<xref rid="bb0095" ref-type="bibr">19</xref>,<xref rid="bb0105" ref-type="bibr">21</xref>] presented the transmission dynamics per individual in an agent-based manner while the other three [<xref rid="bb0080" ref-type="bibr">16</xref>,<xref rid="bb0090" ref-type="bibr">18</xref>,<xref rid="bb0100" ref-type="bibr">20</xref>] modelled from the population perspective and were presented as population-based models. The models by Fraser et al. [<xref rid="bb0095" ref-type="bibr">19</xref>] and Klinkenberg et al. [<xref rid="bb0085" ref-type="bibr">17</xref>] were implemented using agent-based modelling with discrete time simulation. The model by Peak et al. [<xref rid="bb0075" ref-type="bibr">15</xref>] was an agent-based model implemented using Susceptible Exposed Infectious Recovered (SEIR) compartmental branching model. The agent-based model by Becker et al. [<xref rid="bb0105" ref-type="bibr">21</xref>] was a study with household structure. The population-based model by Lloyd-Smith et al. [<xref rid="bb0100" ref-type="bibr">20</xref>] and Feng et al. [<xref rid="bb0080" ref-type="bibr">16</xref>] were implemented using SEIR compartmental model. Chen et al. [<xref rid="bb0090" ref-type="bibr">18</xref>] used Foerster equation-based to describe the population dynamics.</p><p id="p0100">Seven included studies used either (i) population-based modelling, and (ii) agent-based modelling. Population-based modelling is a top-down approach depicting disease dynamics on a system level. Agent-based modelling is a bottom-up approach regarding each individual in the environment as an agent with their own movements and infection states. Population-based models are typically used for analyzing research questions from macroscopic perspectives, whereas agent-based models are good for analyzing microscopic behaviors. Agent-based approach is commonly used to implement heterogeneous and adaptive behaviors. Population-based models are usually less computationally demanding comparing with agent-based models, especially when the number of parameters incorporated in the model is large.</p><p id="p0105">All reviewed studies presented models using SARS as the study example of Co-V; one also presented model studying MERS [<xref rid="bb0075" ref-type="bibr">15</xref>]. Six studies simulated community setting in the models [<xref rid="bb0075" ref-type="bibr">[15]</xref>, <xref rid="bb0080" ref-type="bibr">[16]</xref>, <xref rid="bb0085" ref-type="bibr">[17]</xref>,<xref rid="bb0095" ref-type="bibr">[19]</xref>, <xref rid="bb0100" ref-type="bibr">[20]</xref>, <xref rid="bb0105" ref-type="bibr">[21]</xref>]; three studies simulated hospital setting in their models [<xref rid="bb0080" ref-type="bibr">16</xref>,<xref rid="bb0090" ref-type="bibr">18</xref>,<xref rid="bb0100" ref-type="bibr">20</xref>]. Six studies assumed homogeneous mixing in the social contact structure [<xref rid="bb0075" ref-type="bibr">[15]</xref>, <xref rid="bb0080" ref-type="bibr">[16]</xref>, <xref rid="bb0085" ref-type="bibr">[17]</xref>, <xref rid="bb0090" ref-type="bibr">[18]</xref>, <xref rid="bb0095" ref-type="bibr">[19]</xref>, <xref rid="bb0100" ref-type="bibr">[20]</xref>]: the assumption that everyone in the population had the same probability of making social contact with others. One study assumed heterogeneous social contact mixing between school attendees and non-school attendees [<xref rid="bb0105" ref-type="bibr">21</xref>]. All studies considered single-step tracing of contacts directly exposed to the infectious individuals. One study also considered interactive tracing of contacts directly and indirectly exposed to the infectious individuals [<xref rid="bb0085" ref-type="bibr">17</xref>].Characteristics of the seven included studies are summarized in <xref rid="t0005" ref-type="table">Table 1</xref>.<table-wrap position="float" id="t0005"><label>Table 1</label><caption><p>Summary of characteristics of the reviewed studies.</p></caption><alt-text id="al0015">Table 1</alt-text><table frame="hsides" rules="groups"><thead><tr><th>Author</th><th>Lloyd Smith et al.</th><th>Fraser et al.</th><th>Becker et al.</th><th>Chen et al.</th><th>Klinkenberg et al.</th><th>Feng et al.</th><th>Peak et al.</th></tr></thead><tbody><tr><td>Year</td><td>2003</td><td>2004</td><td>2005</td><td>2006</td><td>2006</td><td>2009</td><td>2017</td></tr><tr><td>Model type</td><td>Population based: Stochastic SEIR compartmental model</td><td>Agent based: Discrete time simulation model</td><td>Agent based: Branching process with household level transmission model</td><td>Population based: Von Foerster equation-based control model</td><td>Agent based: Discrete time simulation model</td><td>Population based: SEIR compartmental model</td><td>Agent based: SEIR compartmental branching model</td></tr><tr><td>Co-V to be studied</td><td>SARS</td><td>SARS</td><td>SARS</td><td>SARS</td><td>SARS</td><td>SARS</td><td>MERS and SARS</td></tr><tr><td>Setting</td><td>Community and its hospital</td><td>Community</td><td>Community of households</td><td>Hospital</td><td>Community</td><td>Community and<break></break>hospital</td><td>Community</td></tr><tr><td>Social contact structure</td><td>Homogeneous mixing</td><td>Homogeneous mixing</td><td>Heterogeneous mixing between two groups: school attendees and non-school attendees</td><td>Homogeneous mixing</td><td>Homogeneous mixing</td><td>Homogeneous mixing</td><td>Homogeneous mixing</td></tr><tr><td>Types of tracing</td><td>Single-step</td><td>Single-step</td><td>Single-step</td><td>Single-step</td><td>Single-step or Interactive</td><td>Single-step</td><td>Single-step</td></tr><tr><td>Self-reported limitations</td><td>1. Superspreading event was not considered in the model.<break></break>2. Non-infected patients in hospitals was not considered in the model.</td><td>Overestimation in contact tracing efficacy due to failure of identifying correlation structure between diseases generation by contact tracing.</td><td>The effect of different interventions on infection dynamics was not considered.</td><td>Transmission heterogeneity such as different social contact mixing was not considered in the model.</td><td>1. The model only considers transmission before tracing or isolation.<break></break>2. The model does not incorporate both hospital and community settings.</td><td>1. Medical consultation seeking rate and diagnosis probability were combined.<break></break>2. Quarantined individuals were assumed to spend half of their incubation period at large.</td><td>The study focused on early stage of the outbreak.</td></tr></tbody></table></table-wrap></p></sec><sec id="s0040"><label>3.2</label><title>Key Input Epidemiological Parameters</title><p id="p0110">There was considerable variation in the assumptions made about key epidemiological parameters: the basic reproduction number (R<sub>0</sub>), incubation period, latent period and infectious period. Key epidemiological parameters used in the seven included studies are summarized in <xref rid="t0010" ref-type="table">Table 2</xref>.<table-wrap position="float" id="t0010"><label>Table 2</label><caption><p>Summary of key epidemiological parameters used in the reviewed studies.</p></caption><alt-text id="al0020">Table 2</alt-text><table frame="hsides" rules="groups"><thead><tr><th>Author</th><th>Lloyd Smith et al.</th><th>Fraser et al.</th><th>Becker et al.</th><th>Chen et al.</th><th>Klinkenberg et al.</th><th>Feng et al.</th><th>Peak et al.</th></tr></thead><tbody><tr><td>Year</td><td>2003</td><td>2004</td><td>2005</td><td>2006</td><td>2006</td><td>2009</td><td>2017</td></tr><tr><td>a) Basic reproduction number (Ro)</td><td>1.5–5<break></break>Ro of 3 was focused</td><td>2–4</td><td>6</td><td>0.25–5.31</td><td>1.5, 2 and 3</td><td>Not an input parameter</td><td>2.9 for SARS<break></break>0.95 for MERS</td></tr><tr><td>b) Incubation period</td><td>Gamma distribution</td><td>Gamma distribution with mean of 4.25 days and variance of 14.25 days</td><td>Assumed to be equal to latent period</td><td>Exponential distribution</td><td>Gamma distribution with 3.81 days</td><td>Gamma distribution of incubation period was used for Latin Hypercube sampling</td><td>4.01 days for SARS<break></break>5.20 days for MERS</td></tr><tr><td>c) Latent period</td><td>Not included in the model</td><td>Not included in the model</td><td>6.5 days</td><td>Not included in the model</td><td>6.81 days</td><td>Not included in the model</td><td>Represented by the latent offset term which refers to the timing of the latent period relative to the incubation period</td></tr><tr><td>d) Infectious period</td><td>Gamma distribution</td><td>Gamma distribution:<break></break>low variance gamma distribution<break></break>with a peak at 9.25 days after infection</td><td>Effective infectious period of 9 days<break></break></td><td>Exponential distribution</td><td>Effective infectious period of 3.87 days</td><td>Gamma distribution of infectious period was used for Latin Hypercube sampling</td><td>Represented by time varying relative infectiousness following triangular distribution</td></tr></tbody></table></table-wrap></p><sec id="s0045"><label>3.2.1</label><title>Basic Reproduction Number (R<sub>0</sub>)</title><p id="p0115">R<sub>0</sub> is the average number of secondary cases caused by one typically infectious individual of an epidemic in a wholly susceptible population [<xref rid="bb0110" ref-type="bibr">22</xref>]. R<sub>0</sub> used by transmission models were usually estimated based on clinical data, and they varied by the dataset used. The values of R<sub>0</sub> for SARS used by the seven included studies ranged from 0.25 to 6; and the value of R<sub>0</sub> for MERS used by Peak et al. was 0.95 [<xref rid="bb0075" ref-type="bibr">15</xref>].</p></sec><sec id="s0050"><label>3.2.2</label><title>Incubation Period and Latent Period</title><p id="p0120">Incubation period refers to the time elapsed between pathogenic exposures to symptom onset [<xref rid="bb0115" ref-type="bibr">23</xref>] whereas latent period is the time elapsed between pathogenic exposures to being infectious [<xref rid="bb0120" ref-type="bibr">24</xref>]. <xref rid="f0010" ref-type="fig">Fig. 2</xref>illustrates these two periods in the natural history of a disease course, where symptom onset can occur before or after being infectious. Six studies incorporated incubation period in the models. They characterized the incubation period with three different parametric distributions. Lloyd-Smith et al., Fraser et al., Feng et al. and Klinkenberg et al. assumed gamma distribution [<xref rid="bb0080" ref-type="bibr">16</xref>,<xref rid="bb0085" ref-type="bibr">17</xref>,<xref rid="bb0095" ref-type="bibr">19</xref>,<xref rid="bb0100" ref-type="bibr">20</xref>]. Chen et al. assumed exponential distribution [<xref rid="bb0090" ref-type="bibr">18</xref>]. Peak et al. did not assume any distribution [<xref rid="bb0075" ref-type="bibr">15</xref>].<fig id="f0010"><label>Fig. 2</label><caption><p>Disease progression periods in the natural history of a disease course. Note that symptom onset can occur before or after being infectious.</p></caption><alt-text id="al0010">Fig. 2</alt-text><graphic xlink:href="gr2_lrg"></graphic></fig></p><p id="p0125">Three studies incorporated latent period in their models [<xref rid="bb0075" ref-type="bibr">15</xref>,<xref rid="bb0085" ref-type="bibr">17</xref>,<xref rid="bb0105" ref-type="bibr">21</xref>]. Klinkenberg et al. defined latent period relative to detection time by subtracting latent period from the sum of the incubation period and the time lapse from symptom onset to isolation [<xref rid="bb0085" ref-type="bibr">17</xref>]. Becker et al. assumed that symptom onsets occurred prior to infection, and used incubation period from previous literatures as latent period [<xref rid="bb0105" ref-type="bibr">21</xref>]. Peak et al. did not incorporate latent period as a parameter; the latent period offset was introduced as a parameter to measure the timing of the latent period relative to the incubation period [<xref rid="bb0075" ref-type="bibr">15</xref>].</p><p id="p0130">In our two study Co-Vs, SARS had a slightly shorter incubation period than latent period [<xref rid="bb0075" ref-type="bibr">15</xref>,<xref rid="bb0085" ref-type="bibr">17</xref>]. However, a longer incubation period than latent period was observed in MERS with occurrence of symptom onset after being infectious as presented as positive latent period offset in the model by Peak et al. [<xref rid="bb0075" ref-type="bibr">15</xref>].</p></sec><sec id="s0055"><label>3.2.3</label><title>Infectious Period</title><p id="p0135">The infectious period is the time interval during which the infected individuals could transmit the disease to any susceptible contacts (<xref rid="f0010" ref-type="fig">Fig. 2</xref>) Lloyd-Smith et al., Fraser et al. and Feng et al. [<xref rid="bb0080" ref-type="bibr">16</xref>,<xref rid="bb0095" ref-type="bibr">19</xref>,<xref rid="bb0100" ref-type="bibr">20</xref>] assumed gamma-distributed infectious period, while Chen et al. [<xref rid="bb0090" ref-type="bibr">18</xref>] assumed exponentially-distributed infectious period. Peak et al. [<xref rid="bb0075" ref-type="bibr">15</xref>] assumed a triangular distribution using peak infectiousness as a parameter of the distribution. Klinkenberg et al. and Becker et al. [<xref rid="bb0085" ref-type="bibr">17</xref>,<xref rid="bb0105" ref-type="bibr">21</xref>] used a similar term called “effective infectious period”, defined as the time lapse from being infectious to isolation Klinkenberg et al. [<xref rid="bb0085" ref-type="bibr">17</xref>] and Becker et al. [<xref rid="bb0105" ref-type="bibr">21</xref>] both assumed a constant effective infectious period adopted from previous literature and WHO data respectively.</p></sec></sec><sec id="s0060"><label>3.3</label><title>Intervention Parameters</title><p id="p0140">The process of contact tracing sits on top of the transmission model and is governed by its own parameters within these models. Intervention parameters used in the seven reviewed studies are summarized in <xref rid="t0015" ref-type="table">Table 3</xref>.<table-wrap position="float" id="t0015"><label>Table 3</label><caption><p>Summary of intervention parameters of the reviewed studies.</p></caption><alt-text id="al0025">Table 3</alt-text><table frame="hsides" rules="groups"><thead><tr><th>Author</th><th>Lloyd Smith et al.</th><th>Fraser et al.</th><th>Becker et al.</th><th>Chen et al.</th><th>Klinkenberg et al.</th><th>Feng et al.</th><th>Peak et al.</th></tr></thead><tbody><tr><td>Year</td><td>2003</td><td>2004</td><td>2005</td><td>2006</td><td>2006</td><td>2009</td><td>2017</td></tr><tr><td>a) Successful tracing ratio</td><td>Not included in the model</td><td>Not included in the model</td><td>Assuming 100% traced on all household members of the primary household infective and a proportion of 50% of between household contact of an infective</td><td>Yes; Not explicitly included in the model but reflected in contact tracing efficacy</td><td>Yes; named as probability of contact being traced</td><td>Not included in the model</td><td>Yes; named as proportion of contact traced;</td></tr><tr><td>b) Asymptomatic infection ratio</td><td>0–10%</td><td>0–11%</td><td>Not included in the model</td><td>0.01–11%</td><td>Not included in the model</td><td>Yes; reflected in infection rates contributed by exposed individuals</td><td>Yes</td></tr><tr><td>c) Quarantine delay</td><td>Yes; probability of quarantining incubating individuals in community to reflect delay of quarantine of incubating individuals in the community</td><td>yes;<break></break>quarantine<break></break>efficacy</td><td>Not included in the model</td><td>Yes; in term of contact tracing efficacy</td><td>Not included in the model</td><td>Yes; a progression rate from exposed state to prodrome in the disease dynamics</td><td>Yes; by a term of delay in tracing a named contact</td></tr><tr><td>d) Isolation delay<break></break><break></break></td><td>Yes; in terms of “probability of isolation of symptomatic individuals in the community” and “probability of isolation of symptomatic health care workers”</td><td>Yes; in terms of a distribution characterizing an individual person who has not been isolated by time since infection to reflect all individuals' infection due to this delay</td><td>Yes; proportion of the infectious period that has passed at the time the infected is isolated.</td><td>Not included in the model</td><td>Yes;<break></break>3.67 days</td><td>Yes;<break></break>a progression rate from prodrome state to acute illness state in the disease dynamics<break></break></td><td>Yes;<break></break>delay from symptom onset to isolation, delay from symptom onset to health seeking behaviour.</td></tr><tr><td>e) Quarantine efficiency</td><td>Yes; in the term of “probability of quarantining of incubating individuals in the community”</td><td>Yes; defined as quarantine efficacy</td><td>Not included in the model; assuming 100% efficiency</td><td>Yes; in term of contact tracing efficacy</td><td>Not included in the model;<break></break>assuming 100% efficiency</td><td>Yes; in terms of<break></break>a smaller hospitalization rate per capita of the exposed individuals</td><td>Not included in the model;<break></break>assuming 100% efficiency</td></tr><tr><td>f) Isolation efficiency<break></break></td><td>Yes; reflected in two terms: “probability of isolation of symptomatic individuals in the community” and “probability of isolation of symptomatic health care workers”</td><td>Yes; defined as isolation efficacy and contact tracing efficiency</td><td>Not included in the model;<break></break></td><td>Not included in the model;<break></break>assuming 100% efficiency</td><td>Not included in the model;<break></break></td><td>Yes; in terms of a smaller hospitalization rate per capita of the symptomatic individuals</td><td>Yes; in terms of a term of isolation effectiveness</td></tr><tr><td>h) Correlation structure between diseases generation due to contact tracing</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr></tbody></table></table-wrap></p><sec id="s0065"><label>3.3.1</label><title>Successful Tracing Ratio of Contacts</title><p id="p0145">Contacts who are successfully traced will be handled through subsequent follow-up actions such as symptom monitoring or isolation. In practical epidemiological cases, not all contacts can be successfully traced. Four studies included the factor of successful tracing ratio of contacts in their dynamics models. Peak et al. [<xref rid="bb0075" ref-type="bibr">15</xref>] and Klinkenberg et al. [<xref rid="bb0085" ref-type="bibr">17</xref>] explicitly incorporated a component for proportion of identified contacts in their models. Identified contacts were quarantined before development of symptoms. Slightly different populations targeted for quarantine were identified in our review studies. Becker et al. [<xref rid="bb0105" ref-type="bibr">21</xref>] targeted all household members and a proportion of 50% of between household contact of an infected individual. Contact tracing efficacy was defined by Chen et al. [<xref rid="bb0090" ref-type="bibr">18</xref>] to reflect this ratio. In general, quarantine of all identified contacts of diagnosed infected individuals was featured in all individual-based infection dynamics models. Ideally, identified contacts are subsequently isolated for management immediately after they have been identified through regular symptom monitor assessment as symptomatic individuals. Peak et al. further considered the fractions of traced contacts who were truly infected and defined uninfected contacts to be traced for a duration up to 95th percentile of incubation period.</p></sec><sec id="s0070"><label>3.3.2</label><title>Asymptomatic Transmission Ratio</title><p id="p0150">Asymptomatic transmission ratio refers to the proportion of infections that have occurred before symptom onset. Prior research showed that a significant amount of SARS Co-V transmissions were either pre-symptomatic or asymptomatic [<xref rid="bb0125" ref-type="bibr">25</xref>]. As of 18 August 2018, there has been no confirmed case caused by asymptomatic transmission for MERS, with one possible case of asymptomatic transmission occurred during the 2012 outbreak [<xref rid="bb0130" ref-type="bibr">26</xref>]. Four studies [<xref rid="bb0075" ref-type="bibr">15</xref>,<xref rid="bb0090" ref-type="bibr">[18]</xref>, <xref rid="bb0095" ref-type="bibr">[19]</xref>, <xref rid="bb0100" ref-type="bibr">[20]</xref>] considered this factor in their transmission models and a range of estimates from 0 to 11% were used. Feng et al. did not explicitly include this parameter in the model, but reflected the occurrence of asymptomatic transmission in infection rates contributed by exposed individuals. The study for MERS by Peak et al. assumed no asymptomatic infection [<xref rid="bb0075" ref-type="bibr">15</xref>].</p></sec><sec id="s0075"><label>3.3.3</label><title>Quarantine and Isolation Delay</title><p id="p0155">In practical epidemiological cases, follow-up control measures might not be implemented to contacts and infected individuals immediately after the contact event. In the 2003 SARS outbreak, it was observed that the time between symptoms onset and hospitalization was 3–5 days, with longer times earlier in the epidemic [<xref rid="bb0135" ref-type="bibr">27</xref>]. Two kinds of implementation delay were considered in the reviewed studies: quarantine delay and isolation delay. Quarantine delay refers to the time lapse between the identification of infected individuals and the implementation of quarantine to contacts. Isolation delay refers to the time lapse between symptoms onset and formal hospital diagnosis/isolation of the infected individuals.</p><p id="p0160">Across the seven studies, five considered the effect of quarantine delay in the transmission dynamics using similar quantities. Peak et al. [<xref rid="bb0075" ref-type="bibr">15</xref>] specified a term called “delay in tracing a named contact”. Fraser et al. [<xref rid="bb0095" ref-type="bibr">19</xref>] used a term called “quarantine efficacy”. Lloyd Smith et al. [<xref rid="bb0100" ref-type="bibr">20</xref>] used a term called “probability of quarantining incubating individuals in community”. Chen et al. [<xref rid="bb0090" ref-type="bibr">18</xref>] used a term called “contact tracing efficacy”. Feng et al. [<xref rid="bb0080" ref-type="bibr">16</xref>] featured this delay with a progression rate from exposed state (either identified or unidentified contacts) to prodrome in the compartmental model.</p><p id="p0165">Three studies characterized isolation delay in the model. Klinkenberg et al. defined a fixed value of 3.67 days between symptom onset and isolation [<xref rid="bb0085" ref-type="bibr">17</xref>]. Peak et al. [<xref rid="bb0075" ref-type="bibr">15</xref>] defined a variable value of 0–0.5 days for isolation delay, and also supplemented a uniform distributed delay from onset to health seeking behavior. Fraser et al. [<xref rid="bb0095" ref-type="bibr">19</xref>] defined a distribution characterizing an individual person who had not been isolated by the time since infection to reflect isolation delay. Other three studies did not include an explicit term for isolation delay, but reflected it in terms of other embedded quantities. Lloyd-Smith et al. [<xref rid="bb0100" ref-type="bibr">20</xref>] embedded a probability of isolating symptomatic individuals in the community and healthcare workers in their model. Feng et al. [<xref rid="bb0080" ref-type="bibr">16</xref>] reflected isolation delay with a progression rate of prodromal individuals becoming acute illness in the disease dynamics. Becker et al. [<xref rid="bb0105" ref-type="bibr">21</xref>] accounted for isolation delay with a revised proportion of the infectious period that had passed at the time the infected individuals were isolated. Chen et al. [<xref rid="bb0090" ref-type="bibr">18</xref>] did not consider the factor of isolation delay in their model.</p></sec><sec id="s0080"><label>3.3.4</label><title>Quarantine and Isolation Efficiency</title><p id="p0170">Contact tracing is shown to be an essential and successful public health tool in reducing the transmission risk of new emerging infectious diseases such as SARS among Hong Kong population [<xref rid="bb0050" ref-type="bibr">10</xref>]. However, the follow-up quarantine and isolation did not necessarily stop all transmission, as illustrated in nosocomial outbreaks of SARS [<xref rid="bb0140" ref-type="bibr">28</xref>] and MERS [<xref rid="bb0145" ref-type="bibr">29</xref>]. An efficiency factor is therefore sometimes incorporated in the models in order to represent the overall quarantine and isolation efficiency in transmission control. Four studies incorporated quarantine efficiency in their models. Quarantine efficacy [<xref rid="bb0095" ref-type="bibr">19</xref>], probability of quarantining individuals in the community [<xref rid="bb0100" ref-type="bibr">20</xref>], a smaller hospitalization rate per capita of the exposed individuals [<xref rid="bb0080" ref-type="bibr">16</xref>] and contact tracing efficacy [<xref rid="bb0090" ref-type="bibr">18</xref>] were used to account for quarantine efficiency. Becker et al. [<xref rid="bb0105" ref-type="bibr">21</xref>], Klinkenberg et al. [<xref rid="bb0085" ref-type="bibr">17</xref>], and Peak et al. [<xref rid="bb0075" ref-type="bibr">15</xref>] did not incorporate quarantine efficiency as a factor of their model, and assuming 100% in quarantine efficiency.</p><p id="p0175">Four studies incorporated isolation efficiency as a factor in the models. Peak et al. [<xref rid="bb0075" ref-type="bibr">15</xref>] used a term named “isolation effectiveness” to represent isolation efficiency in their model. Lloyd-Smith et al. [<xref rid="bb0100" ref-type="bibr">20</xref>] reflected isolation efficiency with two terms, “probability of isolation of symptomatic individuals in the community” and “probability of isolation of symptomatic healthcare workers”. Feng et al. [<xref rid="bb0080" ref-type="bibr">16</xref>] defined a smaller hospitalization rate per capita of the symptomatic individuals (prodromal individuals or acute illness individuals) to represent isolation efficiency in their model. Fraser et al. [<xref rid="bb0095" ref-type="bibr">19</xref>] used two terms, “isolation efficacy” and “contact tracing efficiency”, to measure effectiveness of isolation for infected individuals and effectiveness of isolation for contacts respectively. Becker et al. [<xref rid="bb0105" ref-type="bibr">21</xref>], Chen et al. [<xref rid="bb0090" ref-type="bibr">18</xref>] and Klinkenberg et al. [<xref rid="bb0085" ref-type="bibr">17</xref>] did not model possible transmission that might occur while being isolated, assuming isolation to be 100% effective to stop disease transmission.</p></sec><sec id="s0085"><label>3.3.5</label><title>Correlation Structure between Disease Generation Induced by Contact Tracing</title><p id="p0180">Social contact structure between disease generations is affected by contact tracing and follow-up measures. Social contact is expected to be reduced when an individual is successfully traced. Out of the seven studies, only Peak et al. [<xref rid="bb0075" ref-type="bibr">15</xref>] considered this correlation structure between disease generation due to contact tracing in the model.</p></sec></sec><sec id="s0090"><label>3.4</label><title>Implications of Quarantine for Controlling SARS and MERS</title><p id="p0185">Quarantine is usually considered as one of the live options during disease outbreaks. Day et al. proposed a criteria for quarantine to be effective if a large number of infections were contributed by asymptomatic infection [<xref rid="bb0150" ref-type="bibr">30</xref>]. However, given the social disruption and high costs of this control measure, its poor scalability and resource constraint resulting implementation delay reduce immensely its effectiveness for epidemic containment. For SARS, Feng et al. noted that <italic>“final outbreak size would have been smaller if greater proportion of exposed individuals being quarantined in theory but quarantine is exceedingly inefficient”</italic> [<xref rid="bb0080" ref-type="bibr">16</xref>]. Fraser et al. concluded that “<italic>effective isolation of symptomatic individuals is sufficient to control an outbreak for SARS</italic>” [<xref rid="bb0095" ref-type="bibr">19</xref>]. Chen et al. concluded that “<italic>effective isolation of symptomatic patients with low efficacy contact tracing is sufficient to control of SARS outbreak</italic>” [<xref rid="bb0090" ref-type="bibr">18</xref>]. Peak et al. [<xref rid="bb0075" ref-type="bibr">15</xref>] found that “<italic>comparative effectiveness of quarantine and symptom monitoring is strongly influenced by differences in the infection's natural history</italic>”. With other intervention strategies in force, marginal benefit of quarantine was shown to be usually very low for SARS [<xref rid="bb0075" ref-type="bibr">15</xref>]. Combination of symptom monitoring with other complementary interventions, such as hand-hygiene, social distancing and protective equipment or simply isolating infected individuals only, likely controlled the diseases and ruled out the need to resort to quarantine [<xref rid="bb0075" ref-type="bibr">15</xref>,<xref rid="bb0095" ref-type="bibr">19</xref>]. For MERS, the possible substantial increase in its transmissibility resulted in difficulty in using symptom monitoring to control the disease which guaranteed the reassessment of the role of quarantine [<xref rid="bb0075" ref-type="bibr">15</xref>].</p></sec></sec><sec id="s0095"><label>4</label><title>Discussion</title><p id="p0190">Mathematical modelling has been a powerful tool for understanding and predicting the transmission dynamics of infectious diseases. It was particularly useful during ongoing outbreaks, and was used for evaluating public health policies for disease containment in the SARS outbreak [<xref rid="bb0050" ref-type="bibr">10</xref>], predicting transmission progression, testing efficacy and evaluating effectiveness of contact tracing during the Ebola outbreak in 2013 [<xref rid="bb0155" ref-type="bibr">31</xref>,<xref rid="bb0160" ref-type="bibr">32</xref>]. Improvements for future work and future research directions are summarized from the reviewed models.</p><sec id="s0100"><label>4.1</label><title>Parameterization in Epidemiological Models</title><p id="p0195"><xref rid="t0010" ref-type="table">Tables 2</xref> and <xref rid="t0015" ref-type="table">3</xref> summarize the major parameters incorporated in the seven reviewed models. The two tables list eleven parameters (four key epidemiological parameters and seven intervention parameters). A primary consideration in the initial stage of constructing mathematical epidemiology models is what parameters to be included in a model. The most straightforward approach is to include as many parameters as possible to reflect disease natural history, practical intervention and control measures. A more generic and complex model incorporating all major parameters provides the flexibility of manipulating different variables to simulate outcomes under various hypothetical scenarios. However, from a practical point of view, modelers should consider whether parameters featuring disease courses and interventions can be estimated from empirical or clinical data. The availability of data for parameter estimation is the central challenge for building realistic models.</p><p id="p0200">A robust and rapid estimation of parameters during the early stage of a Co—V outbreak is essential for the construction of useful models of contact tracing. When designing a mathematical epidemiology model, modelers need to ascertain what data can be collected by public health administrators, practically during an outbreak. It was not clear to us from the reports considered in this review that this was yet common practice. We recommend closed-loop communication between epidemiologists and public health administrators for assessing clinical and empirical data collection process during the initial phase of an outbreak. During that period, public health administrators should also prepare a set of operation guidelines for collecting required data.</p><p id="p0205">There was considerable variation in the choice of distributions used for key elements of the natural history. Exponential distributed incubation and infectious period were normally used to ensure greater mathematical tractability. However, some studies proposed to adopt gamma distribution instead to better describe the disease stages of SARS [<xref rid="bb0135" ref-type="bibr">27</xref>,<xref rid="bb0165" ref-type="bibr">33</xref>]. Pitzer et al. [<xref rid="bb0170" ref-type="bibr">34</xref>] suggested that models with gamma distributed infectious period were best fit to the data compared with those assuming constant transmission probability or proportional relation with viral load. The nature of memory-less free characteristics of gamma distribution ensured the biology in the transmission process to be more realistic. Previous work [<xref rid="bb0175" ref-type="bibr">[35]</xref>, <xref rid="bb0180" ref-type="bibr">[36]</xref>, <xref rid="bb0185" ref-type="bibr">[37]</xref>] suggested the importance of realistic distribution of these quantities facilitated more understanding in the epidemic size, the disease progression and how their distributions be adjusted in response to different intervention strategies [<xref rid="bb0190" ref-type="bibr">38</xref>]. These choices are especially relevant to models of contact tracing, as was evident in the early 2000s when two apparently similar models [<xref rid="bb0195" ref-type="bibr">39</xref>,<xref rid="bb0200" ref-type="bibr">40</xref>] came to very different conclusions about likely efficacy of contact tracing because of their different implicit assumptions about the variance of the latent period distribution.</p></sec><sec id="s0105"><label>4.2</label><title>Social Contact Mixing Assumptions</title><p id="p0210">One common assumption among the reviewed studies was homogeneity in social contact mixing. Six reviewed studies assumed homogeneous social contact mixing [<xref rid="bb0075" ref-type="bibr">[15]</xref>, <xref rid="bb0080" ref-type="bibr">[16]</xref>, <xref rid="bb0085" ref-type="bibr">[17]</xref>, <xref rid="bb0090" ref-type="bibr">[18]</xref>, <xref rid="bb0095" ref-type="bibr">[19]</xref>, <xref rid="bb0100" ref-type="bibr">[20]</xref>]. This assumption was not reflecting realistic situations. Previous study found that social contact patterns was heterogeneous [<xref rid="bb0205" ref-type="bibr">41</xref>,<xref rid="bb0210" ref-type="bibr">42</xref>], and social contact pattern within age groups was a significant factor of age-specific infection rates [<xref rid="bb0210" ref-type="bibr">42</xref>]. Future models should consider contact patterns in different social settings such as home, workplace, school, hospital and community to reflect the heterogeneity in disease transmissibility.</p><p id="p0215">Another common assumption among the reviewed studies was assuming social contact mixing to be the same before and after control measures were implemented. Six studies made such assumption [<xref rid="bb0080" ref-type="bibr">[16]</xref>, <xref rid="bb0085" ref-type="bibr">[17]</xref>, <xref rid="bb0090" ref-type="bibr">[18]</xref>, <xref rid="bb0095" ref-type="bibr">[19]</xref>, <xref rid="bb0100" ref-type="bibr">[20]</xref>, <xref rid="bb0105" ref-type="bibr">[21]</xref>]. Fraser et al. [<xref rid="bb0095" ref-type="bibr">19</xref>] addressed this issue by pointing out this misassumption as a limitation of their model, and suggested that models incorporate this feature or alternative mechanisms [<xref rid="bb0045" ref-type="bibr">9</xref>,<xref rid="bb0215" ref-type="bibr">43</xref>] with heterogeneity transmission would give a more unbiased efficacy estimate of contact tracing. Peak et al. addressed this limitation by incorporating this factor into their model [<xref rid="bb0075" ref-type="bibr">15</xref>]. The correlation structure between infectious individuals and infected individuals for subsequent intervention measures following contact tracing should be considered.</p></sec><sec id="s0110"><label>4.3</label><title>Resource Constraints</title><p id="p0220">All studies assumed that contact tracing and follow-up control measures were conducted with unlimited resources. None considered the practical constraints that resources for contact tracing and follow-up control measures might not be available at full throttle. Previous outbreak demonstrated that it was not feasible to have all identified contacts traced as the workforce for contact tracing were depleted in particular for top-stretched resources during the early phase of outbreaks. Public health administrators were faced with decisions and trade-offs in various resources allocation and effort prioritization along with political and social resistance. Future research questions need addressing are how many resources are required and how to prioritize the resources to contain the diseases.</p></sec></sec><sec id="s0115"><label>5</label><title>Summary and Outlook</title><p id="p0225">No one-size guideline is available for containment of both SARS and MERS or other emerging coronavirus. However, mathematical models can help inform policy makers by evaluating the effectiveness of different existing intervention approaches in the early phase of epidemics of new emerging and reemerging Co-V outbreak in the future. A recent confirmed case of MERS in Seoul in September 2018 [<xref rid="bb0220" ref-type="bibr">44</xref>] sparked the imminent call for epidemic counter measures preparedness. To devise appropriate decision tools for the challenges of Co-V, there are a few highlights to refine the models in future research.</p><p id="p0230">First, understanding the epidemiology of the disease will be the top priority in response preparedness. It is key to ensure that proper data can be obtained for model construction during the early stage of an outbreak. To address this concern, public health authority should work closely with other government departments to work out accurate epidemiology data. Modelling researchers should also liaise with public health authority to ensure the required data can be obtained. Second, to reflect the real situation, future modelling studies should evaluate intervention tools under resource-constrained situations and how to allocate resources at different epidemic stages. Third, although SARS and MERS are both classified as coronavirus, the transmission heterogeneity, differences in relative exposure patterns [<xref rid="bb0015" ref-type="bibr">3</xref>] and practicability of control measures determine the optimal solutions for their containment. Geographic disparity of Ro estimate for SARS in 2003 due to different disease and community dependent transmission rate [<xref rid="bb0225" ref-type="bibr">45</xref>] highlighted the need for different levels of contact tracing approach during the epidemics. Forth, both hospital and community setting with the super-spreading events should be included in the transmission dynamics of Co-V outbreak to characterize the role of contact tracing in curbing the escalation of number of infections contributed by super-spreaders. Fifth, modelling approach can provide the condition that contact tracing effectively control the outbreak. Sometimes, politicians or health policy makers may hesitate to implement contact tracing. For example, SARS transmission in high-rise buildings among residents of Amoy Gardens suggested possible airborne transmission [<xref rid="bb0230" ref-type="bibr">46</xref>,<xref rid="bb0235" ref-type="bibr">47</xref>] and government officials were prone to political pressures regarding quarantine. With the help of mathematical models, they can decide when to adopt contact tracing if certain conditions are satisfied. Last but not least, model developers should incorporate factors regarding pre-symptomatic transmission and limited resources allocation against the epidemics at the beginning of the outbreak. Resources optimization and quarantine prioritization by target population will be the next step epidemiologists or modelers should focus on for the challenges of new emerging Co-V epidemics in the future. Two typical examples of the refinement of the current quarantine approach are household quarantine and quarantine based on residential geographic location. In addition to restricting movement in a particular geographic region and recommendation of infectious household members to stay home, other tracing approaches including identifying contacts at workplace or school can also be considered.</p></sec><sec id="s0120"><title>Declarations of Interest</title><p id="p0235">None.</p></sec><sec id="s9120"><title>Acknowledgments</title><p id="p0240">This work has been partially supported by <funding-source id="gts0005">Research Fund for the Control of Infectious Diseases</funding-source>, Hong Kong (Number: CU-17-C18, 11100642); <funding-source id="gts0010">General Research Fund</funding-source> (Number: 14112818); <funding-source id="gts0015">Health and Medical Research Fund</funding-source> (Ref: 17160302); <funding-source id="gts0020">Wellcome Trust</funding-source> (UK, 200861/Z/16/Z); <funding-source id="gts0025">National Institute for General Medical Sciences</funding-source> (US, MIDAS U01 GM110721-01); <funding-source id="gts0030">National Institute for Health Research</funding-source> (UK, for Health Protection Research Unit funding). The authors also thank Li Ka Shing Institute of Health Sciences for technical support.</p></sec></body><back><ref-list id="bi0005"><title>References</title><ref id="bb0005"><label>1</label><element-citation publication-type="book" id="rf0005"><person-group person-group-type="author"><name><surname>WHO</surname></name></person-group><chapter-title>Summary of probable SARS cases with onset of illness from 1 November 2002 to 31 July</chapter-title><year>2018</year><fpage>2003</fpage></element-citation></ref><ref id="bb0010"><label>2</label><element-citation publication-type="book" id="rf0010"><person-group person-group-type="author"><name><surname>WHO</surname></name></person-group><chapter-title>MERS situation update July 2018</chapter-title><year>2018</year></element-citation></ref><ref id="bb0015"><label>3</label><element-citation publication-type="journal" id="rf0015"><person-group person-group-type="author"><name><surname>Chowell</surname><given-names>G.</given-names></name><name><surname>Abdirizak</surname><given-names>F.</given-names></name><name><surname>Lee</surname><given-names>S.</given-names></name><name><surname>Lee</surname><given-names>J.</given-names></name><name><surname>Jung</surname><given-names>E.</given-names></name></person-group><article-title>Transmission characteristics of MERS and SARS in the healthcare setting: a comparative study</article-title><source>BMC Med</source><volume>13</volume><year>2015</year><fpage>210</fpage><pub-id pub-id-type="pmid">26336062</pub-id></element-citation></ref><ref id="bb0020"><label>4</label><element-citation publication-type="book" id="rf0020"><person-group person-group-type="author"><name><surname>USCDC</surname></name></person-group><chapter-title>Frequently asked questions about SARS</chapter-title><year>2012</year></element-citation></ref><ref id="bb0025"><label>5</label><element-citation publication-type="book" id="rf0025"><person-group person-group-type="author"><name><surname>WHO</surname></name></person-group><chapter-title>Middle East respiratory syndrome coronavirus (MERS-CoV)</chapter-title><year>2018</year></element-citation></ref><ref id="bb0030"><label>6</label><element-citation publication-type="journal" id="rf0030"><person-group person-group-type="author"><name><surname>Chan</surname><given-names>J.F.</given-names></name><name><surname>Lau</surname><given-names>S.K.</given-names></name><name><surname>To KK</surname></name><name><surname>Cheng</surname><given-names>V.C.</given-names></name><name><surname>Woo</surname><given-names>P.C.</given-names></name></person-group><article-title>Middle East respiratory syndrome coronavirus: another zoonotic betacoronavirus causing SARS-like disease</article-title><source>Clin Microbiol Rev</source><volume>28</volume><year>2015</year><fpage>465</fpage><lpage>522</lpage><pub-id pub-id-type="pmid">25810418</pub-id></element-citation></ref><ref id="bb0035"><label>7</label><element-citation publication-type="journal" id="rf0035"><person-group person-group-type="author"><name><surname>Ahmed</surname><given-names>A.E.</given-names></name></person-group><article-title>The predictors of 3- and 30-day mortality in 660 MERS-CoV patients</article-title><source>BMC Infect Dis</source><volume>17</volume><year>2017</year><fpage>615</fpage><pub-id pub-id-type="pmid">28893197</pub-id></element-citation></ref><ref id="bb0040"><label>8</label><element-citation publication-type="journal" id="rf0040"><person-group person-group-type="author"><name><surname>Hui</surname><given-names>D.S.</given-names></name><name><surname>Chan</surname><given-names>M.C.</given-names></name><name><surname>Wu</surname><given-names>A.K.</given-names></name><name><surname>Ng</surname><given-names>P.C.</given-names></name></person-group><article-title>Severe acute respiratory syndrome (SARS): epidemiology and clinical features</article-title><source>Postgrad Med J</source><volume>80</volume><year>2004</year><fpage>373</fpage><lpage>381</lpage><pub-id pub-id-type="pmid">15254300</pub-id></element-citation></ref><ref id="bb0045"><label>9</label><element-citation publication-type="journal" id="rf0045"><person-group person-group-type="author"><name><surname>Muller</surname><given-names>J.</given-names></name><name><surname>Kretzschmar</surname><given-names>M.</given-names></name><name><surname>Dietz</surname><given-names>K.</given-names></name></person-group><article-title>Contact tracing in stochastic and deterministic epidemic models</article-title><source>Math Biosci</source><volume>164</volume><year>2000</year><fpage>39</fpage><lpage>64</lpage><pub-id pub-id-type="pmid">10704637</pub-id></element-citation></ref><ref id="bb0050"><label>10</label><element-citation publication-type="journal" id="rf0050"><person-group person-group-type="author"><name><surname>Riley</surname><given-names>S.</given-names></name><name><surname>Fraser</surname><given-names>C.</given-names></name><name><surname>Donnelly</surname><given-names>C.A.</given-names></name><name><surname>Ghani</surname><given-names>A.C.</given-names></name><name><surname>Abu-Raddad</surname><given-names>L.J.</given-names></name></person-group><article-title>Transmission dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions</article-title><source>Science</source><volume>300</volume><year>2003</year><fpage>1961</fpage><lpage>1966</lpage><pub-id pub-id-type="pmid">12766206</pub-id></element-citation></ref><ref id="bb0055"><label>11</label><element-citation publication-type="journal" id="rf0055"><person-group person-group-type="author"><name><surname>Saurabh</surname><given-names>S.</given-names></name><name><surname>Prateek</surname><given-names>S.</given-names></name></person-group><article-title>Role of contact tracing in containing the 2014 Ebola outbreak: a review</article-title><source>Afr Health Sci</source><volume>17</volume><year>2017</year><fpage>225</fpage><lpage>236</lpage><pub-id pub-id-type="pmid">29026397</pub-id></element-citation></ref><ref id="bb0060"><label>12</label><element-citation publication-type="journal" id="rf0060"><person-group person-group-type="author"><name><surname>Eames</surname><given-names>K.T.</given-names></name><name><surname>Keeling</surname><given-names>M.J.</given-names></name></person-group><article-title>Contact tracing and disease control</article-title><source>Proc Biol Sci</source><volume>270</volume><year>2003</year><fpage>2565</fpage><lpage>2571</lpage><pub-id pub-id-type="pmid">14728778</pub-id></element-citation></ref><ref id="bb0065"><label>13</label><element-citation publication-type="journal" id="rf0065"><person-group person-group-type="author"><name><surname>Lee</surname><given-names>M.S.</given-names></name><name><surname>Hu</surname><given-names>A.Y.</given-names></name></person-group><article-title>A cell-based backup to speed up pandemic influenza vaccine production</article-title><source>Trends Microbiol</source><volume>20</volume><year>2012</year><fpage>103</fpage><lpage>105</lpage><pub-id pub-id-type="pmid">22257962</pub-id></element-citation></ref><ref id="bb0070"><label>14</label><element-citation publication-type="journal" id="rf0070"><person-group person-group-type="author"><name><surname>Moher</surname><given-names>D.</given-names></name><name><surname>Shamseer</surname><given-names>L.</given-names></name><name><surname>Clarke</surname><given-names>M.</given-names></name><name><surname>Ghersi</surname><given-names>D.</given-names></name><name><surname>Liberati</surname><given-names>A.</given-names></name></person-group><article-title>Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement</article-title><source>Syst Rev</source><volume>4</volume><issue>1</issue><year>2015</year></element-citation></ref><ref id="bb0075"><label>15</label><element-citation publication-type="journal" id="rf0075"><person-group person-group-type="author"><name><surname>Peak</surname><given-names>C.M.</given-names></name><name><surname>Childs</surname><given-names>L.M.</given-names></name><name><surname>Grad</surname><given-names>Y.H.</given-names></name><name><surname>Buckee</surname><given-names>C.O.</given-names></name></person-group><article-title>Comparing nonpharmaceutical interventions for containing emerging epidemics</article-title><source>Proc Natl Acad Sci U S A</source><volume>114</volume><year>2017</year><fpage>4023</fpage><lpage>4028</lpage><pub-id pub-id-type="pmid">28351976</pub-id></element-citation></ref><ref id="bb0080"><label>16</label><element-citation publication-type="journal" id="rf0080"><person-group person-group-type="author"><name><surname>Feng</surname><given-names>Z.</given-names></name><name><surname>Yang</surname><given-names>Y.</given-names></name><name><surname>Xu</surname><given-names>D.</given-names></name><name><surname>Zhang</surname><given-names>P.</given-names></name><name><surname>McCauley</surname><given-names>M.M.</given-names></name></person-group><article-title>Timely identification of optimal control strategies for emerging infectious diseases</article-title><source>J Theor Biol</source><volume>259</volume><year>2009</year><fpage>165</fpage><lpage>171</lpage><pub-id pub-id-type="pmid">19289133</pub-id></element-citation></ref><ref id="bb0085"><label>17</label><element-citation publication-type="journal" id="rf0085"><person-group person-group-type="author"><name><surname>Klinkenberg</surname><given-names>D.</given-names></name><name><surname>Fraser</surname><given-names>C.</given-names></name><name><surname>Heesterbeek</surname><given-names>H.</given-names></name></person-group><article-title>The effectiveness of contact tracing in emerging epidemics</article-title><source>PLoS One</source><volume>1</volume><year>2006</year><fpage>e12</fpage><pub-id pub-id-type="pmid">17183638</pub-id></element-citation></ref><ref id="bb0090"><label>18</label><element-citation publication-type="journal" id="rf0090"><person-group person-group-type="author"><name><surname>Chen</surname><given-names>S.C.</given-names></name><name><surname>Chang</surname><given-names>C.F.</given-names></name><name><surname>Liao</surname><given-names>C.M.</given-names></name></person-group><article-title>Predictive models of control strategies involved in containing indoor airborne infections</article-title><source>Indoor Air</source><volume>16</volume><year>2006</year><fpage>469</fpage><lpage>481</lpage><pub-id pub-id-type="pmid">17100668</pub-id></element-citation></ref><ref id="bb0095"><label>19</label><element-citation publication-type="journal" id="rf0095"><person-group person-group-type="author"><name><surname>Fraser</surname><given-names>C.</given-names></name><name><surname>Riley</surname><given-names>S.</given-names></name><name><surname>Anderson</surname><given-names>R.M.</given-names></name><name><surname>Ferguson</surname><given-names>N.M.</given-names></name></person-group><article-title>Factors that make an infectious disease outbreak controllable</article-title><source>Proc Natl Acad Sci U S A</source><volume>101</volume><year>2004</year><fpage>6146</fpage><lpage>6151</lpage><pub-id pub-id-type="pmid">15071187</pub-id></element-citation></ref><ref id="bb0100"><label>20</label><element-citation publication-type="journal" id="rf0100"><person-group person-group-type="author"><name><surname>Lloyd-Smith</surname><given-names>J.O.</given-names></name><name><surname>Galvani</surname><given-names>A.P.</given-names></name><name><surname>Getz</surname><given-names>W.M.</given-names></name></person-group><article-title>Curtailing transmission of severe acute respiratory syndrome within a community and its hospital</article-title><source>Proc Biol Sci</source><volume>270</volume><year>2003</year><fpage>1979</fpage><lpage>1989</lpage><pub-id pub-id-type="pmid">14561285</pub-id></element-citation></ref><ref id="bb0105"><label>21</label><element-citation publication-type="journal" id="rf0105"><person-group person-group-type="author"><name><surname>Becker</surname><given-names>N.G.</given-names></name><name><surname>Glass</surname><given-names>K.</given-names></name><name><surname>Li</surname><given-names>Z.</given-names></name><name><surname>Aldis</surname><given-names>G.K.</given-names></name></person-group><article-title>Controlling emerging infectious diseases like SARS</article-title><source>Math Biosci</source><volume>193</volume><year>2005</year><fpage>205</fpage><lpage>221</lpage><pub-id pub-id-type="pmid">15748730</pub-id></element-citation></ref><ref id="bb0110"><label>22</label><element-citation publication-type="book" id="rf0110"><person-group person-group-type="author"><name><surname>Anderson</surname><given-names>R.M.M.R.M.</given-names></name></person-group><chapter-title>Infectious Diseases of Humans Dynamics and Control</chapter-title><year>1992</year><publisher-name>Oxford University Press</publisher-name></element-citation></ref><ref id="bb0115"><label>23</label><element-citation publication-type="book" id="rf0115"><chapter-title>Incubation</chapter-title><person-group person-group-type="editor"><name><surname>Kirch</surname><given-names>W.</given-names></name></person-group><source>Encyclopedia of Public Health</source><year>2008</year><publisher-name>Springer</publisher-name><publisher-loc>Dordrecht</publisher-loc></element-citation></ref><ref id="bb0120"><label>24</label><element-citation publication-type="book" id="rf0120"><chapter-title>Latency</chapter-title><person-group person-group-type="editor"><name><surname>Kirch</surname><given-names>W.</given-names></name></person-group><source>Encyclopedia of Public Health</source><year>2008</year><publisher-name>Springer</publisher-name><publisher-loc>Dordrecht</publisher-loc></element-citation></ref><ref id="bb0125"><label>25</label><element-citation publication-type="journal" id="rf0125"><person-group person-group-type="author"><name><surname>Wilder-Smith</surname><given-names>A.</given-names></name><name><surname>Teleman</surname><given-names>M.D.</given-names></name><name><surname>Heng</surname><given-names>B.H.</given-names></name><name><surname>Earnest</surname><given-names>A.</given-names></name><name><surname>Ling</surname><given-names>A.E.</given-names></name></person-group><article-title>Asymptomatic SARS coronavirus infection among healthcare workers, Singapore</article-title><source>Emerg Infect Dis</source><volume>11</volume><year>2005</year><fpage>1142</fpage><lpage>1145</lpage><pub-id pub-id-type="pmid">16022801</pub-id></element-citation></ref><ref id="bb0130"><label>26</label><element-citation publication-type="journal" id="rf0130"><person-group person-group-type="author"><name><surname>Omrani</surname><given-names>A.S.</given-names></name><name><surname>Matin</surname><given-names>M.A.</given-names></name><name><surname>Haddad</surname><given-names>Q.</given-names></name><name><surname>Al-Nakhli</surname><given-names>D.</given-names></name><name><surname>Memish</surname><given-names>Z.A.</given-names></name></person-group><article-title>A family cluster of Middle East respiratory Syndrome Coronavirus infections related to a likely unrecognized asymptomatic or mild case</article-title><source>Int J Infect Dis</source><volume>17</volume><year>2013</year><fpage>e668</fpage><lpage>e672</lpage><pub-id pub-id-type="pmid">23916548</pub-id></element-citation></ref><ref id="bb0135"><label>27</label><element-citation publication-type="journal" id="rf0135"><person-group person-group-type="author"><name><surname>Donnelly</surname><given-names>C.A.</given-names></name><name><surname>Ghani</surname><given-names>A.C.</given-names></name><name><surname>Leung</surname><given-names>G.M.</given-names></name><name><surname>Hedley</surname><given-names>A.J.</given-names></name><name><surname>Fraser</surname><given-names>C.</given-names></name></person-group><article-title>Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong</article-title><source>Lancet</source><volume>361</volume><year>2003</year><fpage>1761</fpage><lpage>1766</lpage><pub-id pub-id-type="pmid">12781533</pub-id></element-citation></ref><ref id="bb0140"><label>28</label><element-citation publication-type="journal" id="rf0140"><person-group person-group-type="author"><name><surname>Kwok</surname><given-names>K.O.</given-names></name><name><surname>Leung</surname><given-names>G.M.</given-names></name><name><surname>Lam</surname><given-names>W.Y.</given-names></name><name><surname>Riley</surname><given-names>S.</given-names></name></person-group><article-title>Using models to identify routes of nosocomial infection: a large hospital outbreak of SARS in Hong Kong</article-title><source>Proc Biol Sci</source><volume>274</volume><year>2007</year><fpage>611</fpage><lpage>617</lpage><pub-id pub-id-type="pmid">17254984</pub-id></element-citation></ref><ref id="bb0145"><label>29</label><element-citation publication-type="journal" id="rf0145"><person-group person-group-type="author"><name><surname>Majumder</surname><given-names>M.S.</given-names></name><name><surname>Brownstein</surname><given-names>J.S.</given-names></name><name><surname>Finkelstein</surname><given-names>S.N.</given-names></name><name><surname>Larson</surname><given-names>R.C.</given-names></name><name><surname>Bourouiba</surname><given-names>L.</given-names></name></person-group><article-title>Nosocomial amplification of MERS-coronavirus in South Korea, 2015</article-title><source>Trans R Soc Trop Med Hyg</source><volume>111</volume><year>2017</year><fpage>261</fpage><lpage>269</lpage><pub-id pub-id-type="pmid">29044371</pub-id></element-citation></ref><ref id="bb0150"><label>30</label><element-citation publication-type="journal" id="rf0150"><person-group person-group-type="author"><name><surname>Day</surname><given-names>T.</given-names></name><name><surname>Park</surname><given-names>A.</given-names></name><name><surname>Madras</surname><given-names>N.</given-names></name><name><surname>Gumel</surname><given-names>A.</given-names></name><name><surname>Wu</surname><given-names>J.</given-names></name></person-group><article-title>When is quarantine a useful control strategy for emerging infectious diseases?</article-title><source>Am J Epidemiol</source><volume>163</volume><year>2006</year><fpage>479</fpage><lpage>485</lpage><pub-id pub-id-type="pmid">16421244</pub-id></element-citation></ref><ref id="bb0155"><label>31</label><element-citation publication-type="journal" id="rf0155"><person-group person-group-type="author"><name><surname>Rivers</surname><given-names>C.M.</given-names></name><name><surname>Lofgren</surname><given-names>E.T.</given-names></name><name><surname>Marathe</surname><given-names>M.</given-names></name><name><surname>Eubank</surname><given-names>S.</given-names></name><name><surname>Lewis</surname><given-names>B.L.</given-names></name></person-group><article-title>Modeling the impact of interventions on an epidemic of ebola in Sierra Leone and Liberia</article-title><source>PLoS Curr</source><volume>6</volume><year>2014</year></element-citation></ref><ref id="bb0160"><label>32</label><element-citation publication-type="journal" id="rf0160"><person-group person-group-type="author"><name><surname>Webb</surname><given-names>G.</given-names></name><name><surname>Browne</surname><given-names>C.</given-names></name><name><surname>Huo</surname><given-names>X.</given-names></name><name><surname>Seydi</surname><given-names>O.</given-names></name><name><surname>Seydi</surname><given-names>M.</given-names></name></person-group><article-title>A model of the 2014 ebola epidemic in West Africa with contact tracing</article-title><source>PLoS Curr</source><volume>7</volume><year>2015</year></element-citation></ref><ref id="bb0165"><label>33</label><element-citation publication-type="journal" id="rf0165"><person-group person-group-type="author"><name><surname>Kuk</surname><given-names>A.Y.</given-names></name><name><surname>Ma</surname><given-names>S.</given-names></name></person-group><article-title>The estimation of SARS incubation distribution from serial interval data using a convolution likelihood</article-title><source>Stat Med</source><volume>24</volume><year>2005</year><fpage>2525</fpage><lpage>2537</lpage><pub-id pub-id-type="pmid">16013037</pub-id></element-citation></ref><ref id="bb0170"><label>34</label><element-citation publication-type="journal" id="rf0170"><person-group person-group-type="author"><name><surname>Pitzer</surname><given-names>V.E.</given-names></name><name><surname>Leung</surname><given-names>G.M.</given-names></name><name><surname>Lipsitch</surname><given-names>M.</given-names></name></person-group><article-title>Estimating variability in the transmission of severe acute respiratory syndrome to household contacts in Hong Kong, China</article-title><source>Am J Epidemiol</source><volume>166</volume><year>2007</year><fpage>355</fpage><lpage>363</lpage><pub-id pub-id-type="pmid">17493952</pub-id></element-citation></ref><ref id="bb0175"><label>35</label><element-citation publication-type="journal" id="rf0175"><person-group person-group-type="author"><name><surname>Wearing</surname><given-names>H.J.</given-names></name><name><surname>Rohani</surname><given-names>P.</given-names></name><name><surname>Keeling</surname><given-names>M.J.</given-names></name></person-group><article-title>Appropriate models for the management of infectious diseases</article-title><source>PLoS Med</source><volume>2</volume><year>2005</year><fpage>e174</fpage><pub-id pub-id-type="pmid">16013892</pub-id></element-citation></ref><ref id="bb0180"><label>36</label><element-citation publication-type="journal" id="rf0180"><person-group person-group-type="author"><name><surname>Chowell</surname><given-names>G.</given-names></name><name><surname>Nishiura</surname><given-names>H.</given-names></name></person-group><article-title>Transmission dynamics and control of Ebola virus disease (EVD): a review</article-title><source>BMC Med</source><volume>12</volume><year>2014</year><fpage>196</fpage><pub-id pub-id-type="pmid">25300956</pub-id></element-citation></ref><ref id="bb0185"><label>37</label><element-citation publication-type="journal" id="rf0185"><person-group person-group-type="author"><name><surname>Lloyd</surname><given-names>A.L.</given-names></name></person-group><article-title>Realistic distributions of infectious periods in epidemic models: changing patterns of persistence and dynamics</article-title><source>Theor Popul Biol</source><volume>60</volume><year>2001</year><fpage>59</fpage><lpage>71</lpage><pub-id pub-id-type="pmid">11589638</pub-id></element-citation></ref><ref id="bb0190"><label>38</label><element-citation publication-type="journal" id="rf0190"><person-group person-group-type="author"><name><surname>Wilkinson</surname><given-names>R.R.</given-names></name><name><surname>Sharkey</surname><given-names>K.J.</given-names></name></person-group><article-title>Impact of the infectious period on epidemics</article-title><source>Phys Rev E</source><volume>97</volume><year>2018</year><object-id pub-id-type="publisher-id">052403</object-id></element-citation></ref><ref id="bb0195"><label>39</label><element-citation publication-type="journal" id="rf0195"><person-group person-group-type="author"><name><surname>Kaplan</surname><given-names>E.H.</given-names></name><name><surname>Craft</surname><given-names>D.L.</given-names></name><name><surname>Wein</surname><given-names>L.M.</given-names></name></person-group><article-title>Emergency response to a smallpox attack: the case for mass vaccination</article-title><source>Proc Natl Acad Sci U S A</source><volume>99</volume><year>2002</year><fpage>10935</fpage><lpage>10940</lpage><pub-id pub-id-type="pmid">12118122</pub-id></element-citation></ref><ref id="bb0200"><label>40</label><element-citation publication-type="journal" id="rf0200"><person-group person-group-type="author"><name><surname>Halloran</surname><given-names>M.E.</given-names></name><name><surname>Longini</surname><given-names>I.M.</given-names><suffix>Jr.</suffix></name><name><surname>Nizam</surname><given-names>A.</given-names></name><name><surname>Yang</surname><given-names>Y.</given-names></name></person-group><article-title>Containing bioterrorist smallpox</article-title><source>Science</source><volume>298</volume><year>2002</year><fpage>1428</fpage><lpage>1432</lpage><pub-id pub-id-type="pmid">12434061</pub-id></element-citation></ref><ref id="bb0205"><label>41</label><element-citation publication-type="journal" id="rf0205"><person-group person-group-type="author"><name><surname>Kwok</surname><given-names>K.O.</given-names></name><name><surname>Cowling</surname><given-names>B.</given-names></name><name><surname>Wei</surname><given-names>V.</given-names></name><name><surname>Riley</surname><given-names>S.</given-names></name><name><surname>Read</surname><given-names>J.M.</given-names></name></person-group><article-title>Temporal variation of human encounters and the number of locations in which they occur: a longitudinal study of Hong Kong residents</article-title><source>J R Soc Interface</source><volume>15</volume><year>2018</year></element-citation></ref><ref id="bb0210"><label>42</label><element-citation publication-type="journal" id="rf0210"><person-group person-group-type="author"><name><surname>Kwok</surname><given-names>K.O.</given-names></name><name><surname>Cowling</surname><given-names>B.J.</given-names></name><name><surname>Wei</surname><given-names>V.W.</given-names></name><name><surname>Wu</surname><given-names>K.M.</given-names></name><name><surname>Read</surname><given-names>J.M.</given-names></name></person-group><article-title>Social contacts and the locations in which they occur as risk factors for influenza infection</article-title><source>Proc Biol Sci</source><volume>281</volume><year>2014</year><fpage>20140709</fpage><pub-id pub-id-type="pmid">25009062</pub-id></element-citation></ref><ref id="bb0215"><label>43</label><element-citation publication-type="journal" id="rf0215"><person-group person-group-type="author"><name><surname>Huerta</surname><given-names>R.</given-names></name><name><surname>Tsimring</surname><given-names>L.S.</given-names></name></person-group><article-title>Contact tracing and epidemics control in social networks</article-title><source>Phys Rev E Stat Nonlin Soft Matter Phys</source><volume>66</volume><year>2002</year><fpage>056115</fpage><pub-id pub-id-type="pmid">12513564</pub-id></element-citation></ref><ref id="bb0220"><label>44</label><element-citation publication-type="book" id="rf0220"><source>South Korean man infected by MERS virus, first case in 3 years</source><year>2018</year><comment>[Reuters/AFP/aj]</comment></element-citation></ref><ref id="bb0225"><label>45</label><element-citation publication-type="journal" id="rf0225"><person-group person-group-type="author"><name><surname>Galvani</surname><given-names>A.P.</given-names></name><name><surname>Lei</surname><given-names>X.</given-names></name><name><surname>Jewell</surname><given-names>N.P.</given-names></name></person-group><article-title>Severe acute respiratory syndrome: temporal stability and geographic variation in case-fatality rates and doubling times</article-title><source>Emerg Infect Dis</source><volume>9</volume><year>2003</year><fpage>991</fpage><lpage>994</lpage><pub-id pub-id-type="pmid">12967499</pub-id></element-citation></ref><ref id="bb0230"><label>46</label><element-citation publication-type="journal" id="rf0230"><person-group person-group-type="author"><name><surname>McKinney</surname><given-names>K.R.</given-names></name><name><surname>Gong</surname><given-names>Y.Y.</given-names></name><name><surname>Lewis</surname><given-names>T.G.</given-names></name></person-group><article-title>Environmental transmission of SARS at Amoy Gardens</article-title><source>J Environ Health</source><volume>68</volume><year>2006</year><fpage>26</fpage><lpage>30</lpage><comment>[quiz 51-22]</comment></element-citation></ref><ref id="bb0235"><label>47</label><element-citation publication-type="journal" id="rf0235"><person-group person-group-type="author"><name><surname>Yu</surname><given-names>I.T.</given-names></name><name><surname>Li</surname><given-names>Y.</given-names></name><name><surname>Wong</surname><given-names>T.W.</given-names></name><name><surname>Tam</surname><given-names>W.</given-names></name><name><surname>Chan</surname><given-names>A.T.</given-names></name></person-group><article-title>Evidence of airborne transmission of the severe acute respiratory syndrome virus</article-title><source>N Engl J Med</source><volume>350</volume><year>2004</year><fpage>1731</fpage><lpage>1739</lpage><pub-id pub-id-type="pmid">15102999</pub-id></element-citation></ref></ref-list></back></pmc></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/SrasV1/Data/Pmc/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001721 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Pmc/Corpus/biblio.hfd -nk 001721 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Sante
|area= SrasV1
|flux= Pmc
|étape= Corpus
|type= RBID
|clé= PMC:6376160
|texte= Epidemic Models of Contact Tracing: Systematic Review of Transmission Studies of Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Pmc/Corpus/RBID.i -Sk "pubmed:30809323" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Pmc/Corpus/biblio.hfd \
| NlmPubMed2Wicri -a SrasV1
| This area was generated with Dilib version V0.6.33. |  |