tetano
Editor, Senior Moderator
J Glob Health
. 2021 Jan 16;11:05005.
doi: 10.7189/jogh.11.05005.
Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19
Houssein H Ayoub[SUP] 1 [/SUP], Hiam Chemaitelly[SUP] 2 3 [/SUP], Shaheen Seedat[SUP] 2 3 4 [/SUP], Monia Makhoul[SUP] 2 3 4 [/SUP], Zaina Al Kanaani[SUP] 5 [/SUP], Abdullatif Al Khal[SUP] 5 [/SUP], Einas Al Kuwari[SUP] 5 [/SUP], Adeel A Butt[SUP] 4 5 [/SUP], Peter Coyle[SUP] 5 [/SUP], Andrew Jeremijenko[SUP] 5 [/SUP], Anvar Hassan Kaleeckal[SUP] 5 [/SUP], Ali Nizar Latif[SUP] 5 [/SUP], Riyazuddin Mohammad Shaik[SUP] 5 [/SUP], Hanan Abdul Rahim[SUP] 6 [/SUP], Hadi M Yassine[SUP] 7 8 [/SUP], Mohamed G Al Kuwari[SUP] 9 [/SUP], Hamad Eid Al Romaihi[SUP] 10 [/SUP], Mohamed H Al-Thani[SUP] 10 [/SUP], Roberto Bertollini[SUP] 10 [/SUP], Laith J Abu Raddad[SUP] 2 3 4 [/SUP]
Affiliations
Abstract
Background: 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 ICU-care hospital admissions and occupancy were forecast accurately and precisely. By October 15, 2020, the basic reproduction number R[SUB]0[/SUB] 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 R[SUB]t[/SUB] tuning, gradual easing of restrictions was rationalized and justified to start on June 15, 2020, when R[SUB]t[/SUB] 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.
. 2021 Jan 16;11:05005.
doi: 10.7189/jogh.11.05005.
Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19
Houssein H Ayoub[SUP] 1 [/SUP], Hiam Chemaitelly[SUP] 2 3 [/SUP], Shaheen Seedat[SUP] 2 3 4 [/SUP], Monia Makhoul[SUP] 2 3 4 [/SUP], Zaina Al Kanaani[SUP] 5 [/SUP], Abdullatif Al Khal[SUP] 5 [/SUP], Einas Al Kuwari[SUP] 5 [/SUP], Adeel A Butt[SUP] 4 5 [/SUP], Peter Coyle[SUP] 5 [/SUP], Andrew Jeremijenko[SUP] 5 [/SUP], Anvar Hassan Kaleeckal[SUP] 5 [/SUP], Ali Nizar Latif[SUP] 5 [/SUP], Riyazuddin Mohammad Shaik[SUP] 5 [/SUP], Hanan Abdul Rahim[SUP] 6 [/SUP], Hadi M Yassine[SUP] 7 8 [/SUP], Mohamed G Al Kuwari[SUP] 9 [/SUP], Hamad Eid Al Romaihi[SUP] 10 [/SUP], Mohamed H Al-Thani[SUP] 10 [/SUP], Roberto Bertollini[SUP] 10 [/SUP], Laith J Abu Raddad[SUP] 2 3 4 [/SUP]
Affiliations
- PMID: 33643638
- PMCID: PMC7897910
- DOI: 10.7189/jogh.11.05005
Abstract
Background: 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 ICU-care hospital admissions and occupancy were forecast accurately and precisely. By October 15, 2020, the basic reproduction number R[SUB]0[/SUB] 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 R[SUB]t[/SUB] tuning, gradual easing of restrictions was rationalized and justified to start on June 15, 2020, when R[SUB]t[/SUB] 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.