Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19

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 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 R_t tuning, gradual easing of restrictions was rationalized and justified to start on June 15, 2020, when R_t 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.

Figure 1

Model predictions for evolution of SARS-CoV-2 infections in the total population of Qatar. Panel A. Incidence (number of daily new infections). Panel B. Cumulative number of infections. Panel C. Active-infection prevalence (those latently infected or infectious). Panel D. Attack rate (proportion ever infected).

Figure 2

Model predictions for evolution of COVID-19 disease cases. Panel A. Daily hospital admissions in acute-care beds. Panel B. Daily hospital admissions in ICU-care beds. Panel C. Cumulative number of hospitalizations in acute-care beds. Panel D. Cumulative number of hospitalizations in ICU-care beds. Panel E. Hospital occupancy of COVID-19 patients (number of beds occupied at any given time) in acute-care beds. Panel F. Hospital occupancy of COVID-19 patients (number of beds occupied at any given time) in ICU-care beds.

Figure 3

Rationale and criteria used for the start of easing of social and physical distancing restrictions. Panels A-C show the model fit and results at the time when the policy decision was actually made. An updated prediction for R_t is in Figure S4 of the Online Supplementary Document. Panel A. Effective reproduction number R_t and easing of social and physical distancing restrictions. Panel B. Prediction of the number of daily new infections with early easing of restrictions, three weeks before the epidemic peak. Panel C. Prediction of the number of daily new infections with delayed easing of restrictions, three weeks after the epidemic peak. Panel D. The number of daily new diagnosed and laboratory-confirmed infections.

Figure 4

Impact of social and physical distancing interventions. Panel A. Number of daily new infections. Panel B. Active-infection prevalence (those latently infected or infectious). Panel C. Daily hospital admissions in acute-care beds. Panel D. Daily hospital admissions in ICU-care beds.