A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation
Identifieur interne : 000598 ( Main/Exploration ); précédent : 000597; suivant : 000599A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation
Auteurs : Tridip Sardar [Inde] ; Indrajit Ghosh [Inde] ; Xavier Rod [Espagne] ; Joydev Chattopadhyay [Inde]Source :
- PLoS Neglected Tropical Diseases [ 1935-2727 ] ; 2020.
Abstract
Middle East Respiratory Syndrome Coronavirus (MERS-CoV) causes severe acute respiratory illness with a case fatality rate (CFR) of 35,5%. The highest number of MERS-CoV cases are from Saudi-Arabia, the major worldwide hotspot for this disease. In the absence of neither effective treatment nor a ready-to-use vaccine and with yet an incomplete understanding of its epidemiological cycle, prevention and containment measures can be derived from mathematical models of disease epidemiology. We constructed 2-strain models to predict past outbreaks in the interval 2012–2016 and derive key epidemiological information for Macca, Madina and Riyadh. We approached variability in infection through three different disease incidence functions capturing social behavior in response to an epidemic (e.g. Bilinear, BL; Non-monotone, NM; and Saturated, SAT models). The best model combination successfully anticipated the total number of MERS-CoV clinical cases for the 2015–2016 season and accurately predicted both the number of cases at the peak of seasonal incidence and the overall shape of the epidemic cycle. The evolution in the basic reproduction number (
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DOI: 10.1371/journal.pntd.0008065
PubMed: 32059047
PubMed Central: 7046297
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Kolkata, India</addr-line></nlm:aff><country xml:lang="fr">Inde</country><wicri:regionArea>Department of Mathematics, Dinabandhu Andrews College, Kolkata</wicri:regionArea><wicri:noRegion>Kolkata</wicri:noRegion></affiliation></author><author><name sortKey="Ghosh, Indrajit" sort="Ghosh, Indrajit" uniqKey="Ghosh I" first="Indrajit" last="Ghosh">Indrajit Ghosh</name><affiliation wicri:level="1"><nlm:aff id="aff002"><addr-line>Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata, India</addr-line></nlm:aff><country xml:lang="fr">Inde</country><wicri:regionArea>Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata</wicri:regionArea><wicri:noRegion>Kolkata</wicri:noRegion></affiliation></author><author><name sortKey="Rod, Xavier" sort="Rod, Xavier" uniqKey="Rod X" first="Xavier" last="Rod">Xavier Rod</name><affiliation wicri:level="3"><nlm:aff id="aff003"><addr-line>ICREA &CLIMA (Climate and Health Program), ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain</addr-line></nlm:aff><country xml:lang="fr">Espagne</country><wicri:regionArea>ICREA &CLIMA (Climate and Health Program), ISGlobal, Barcelona Institute for Global Health, Barcelona</wicri:regionArea><placeName><settlement type="city">Barcelone</settlement><region nuts="2" type="region">Catalogne</region></placeName></affiliation></author><author><name sortKey="Chattopadhyay, Joydev" sort="Chattopadhyay, Joydev" uniqKey="Chattopadhyay J" first="Joydev" last="Chattopadhyay">Joydev Chattopadhyay</name><affiliation wicri:level="1"><nlm:aff id="aff002"><addr-line>Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata, India</addr-line></nlm:aff><country xml:lang="fr">Inde</country><wicri:regionArea>Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata</wicri:regionArea><wicri:noRegion>Kolkata</wicri:noRegion></affiliation></author></analytic><series><title level="j">PLoS Neglected Tropical Diseases</title><idno type="ISSN">1935-2727</idno><idno type="eISSN">1935-2735</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Middle East Respiratory Syndrome Coronavirus (MERS-CoV) causes severe acute respiratory illness with a case fatality rate (CFR) of 35,5%. The highest number of MERS-CoV cases are from Saudi-Arabia, the major worldwide hotspot for this disease. In the absence of neither effective treatment nor a ready-to-use vaccine and with yet an incomplete understanding of its epidemiological cycle, prevention and containment measures can be derived from mathematical models of disease epidemiology. We constructed 2-strain models to predict past outbreaks in the interval 2012–2016 and derive key epidemiological information for Macca, Madina and Riyadh. We approached variability in infection through three different disease incidence functions capturing social behavior in response to an epidemic (e.g. Bilinear, BL; Non-monotone, NM; and Saturated, SAT models). The best model combination successfully anticipated the total number of MERS-CoV clinical cases for the 2015–2016 season and accurately predicted both the number of cases at the peak of seasonal incidence and the overall shape of the epidemic cycle. The evolution in the basic reproduction number (<italic>R</italic><sub><italic>0</italic></sub>) warns that MERS-CoV may easily take an epidemic form. The best model correctly captures this feature, indicating a high epidemic risk (1≤<italic>R</italic><sub><italic>0</italic></sub>≤2,5) in Riyadh and Macca and confirming the alleged co-circulation of more than one strain. Accurate predictions of the future MERS-CoV peak week, as well as the number of cases at the peak are now possible. These results indicate public health agencies should be aware that measures for strict containment are urgently needed before new epidemics take off in the region.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Sabir, J S M" uniqKey="Sabir J">J.S.M. Sabir</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Chowell, G" uniqKey="Chowell G">G. 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