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AuthorAyoub, Houssein H.
AuthorChemaitelly, Hiam
AuthorSeedat, Shaheen
AuthorMakhoul, Monia
AuthorKanaani, Zaina Al
AuthorKhal, Abdullatif Al
AuthorKuwari, Einas Al
AuthorButt, Adeel A.
AuthorCoyle, Peter
AuthorJeremijenko, Andrew
AuthorKaleeckal, Anvar Hassan
AuthorLatif, Ali Nizar
AuthorShaik, Riyazuddin Mohammad
AuthorRahim, Hanan Abdul
AuthorYassine, Hadi M.
AuthorKuwari, Mohamed G.Al
AuthorRomaihi, Hamad Eid Al
AuthorAl-Thani, Mohamed H.
AuthorBertollini, Roberto
AuthorRaddad, Laith J.Abu
Available date2022-09-14T11:36:53Z
Publication Date2021-01-01
Publication NameJournal of Global Health
Identifierhttp://dx.doi.org/10.7189/jogh.11.05005
CitationAyoub, H. H., Chemaitelly, H., Seedat, S., Makhoul, M., Al Kanaani, Z., Al Khal, A., ... & Raddad, L. J. A. (2021). Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19. Journal of global health, 11.‏
ISSN20472978
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85101248378&origin=inward
URIhttp://hdl.handle.net/10576/34026
AbstractBackground Mathematical modeling constitutes an important tool for planning robust responses to epidemics. This study was conducted to guide the Qatari national response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic. The study investigated the epidemic’s time-course, forecasted health care needs, predicted the impact of social and physical distancing restrictions, and rationalized and justified easing of restrictions. Methods An age-structured deterministic model was constructed to describe SARS-CoV-2 transmission dynamics and disease progression throughout the population. Results The enforced social and physical distancing interventions flattened the epidemic curve, reducing the peaks for incidence, prevalence, acute-care hospitalization, and intensive care unit (ICU) hospitalizations by 87%, 86%, 76%, and 78%, respectively. The daily number of new infections was predicted to peak at 12 750 on May 23, and active-infection prevalence was predicted to peak at 3.2% on May 25. Daily acute-care and ICUcare hospital admissions and occupancy were forecast accurately and precisely. By October 15, 2020, the basic reproduction number R0 had varied between 1.07-2.78, and 50.8% of the population were estimated to have been infected (1.43 million infections). The proportion of actual infections diagnosed was estimated at 11.6%. Applying the concept of Rt tuning, gradual easing of restrictions was rationalized and justified to start on June 15, 2020, when Rt declined to 0.7, to buffer the increased interpersonal contact with easing of restrictions and to minimize the risk of a second wave. No second wave has materialized as of October 15, 2020, five months after the epidemic peak. Conclusions Use of modeling and forecasting to guide the national response proved to be a successful strategy, reducing the toll of the epidemic to a manageable level for the health care system.
Languageen
PublisherUniversity of Edinburgh
Subjectepidemic
epidemiology
forecasting
hospitalization
human
incidence
intensive care unit
TitleMathematical modeling of the SARSCoV-2 epidemic in Qatar and its impact on the national response to COVID-19
TypeArticle
Volume Number11
dc.accessType Open Access


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