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 R 0 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.

Herd Immunity against Severe Acute Respiratory Syndrome Coronavirus 2 Infection in 10 Communities, Qatar

“…Factors observed included the large number of diagnosed infections in CMWs (4), the large proportion of infections that were asymptomatic (4,15,16), the high real-time RT-PCR positivity rates in the random testing campaigns conducted around the epidemic peak in different CMW communities (4), the observed susceptible-infected-recovered epidemic curve with steady decreases in incidence for 8 months despite the gradual easing of the social and physical distancing restrictions (4,17), and evidence indicating an efficacy >90% for natural infection against reinfection that lasts for >7 months (18; L.J. Abu-Raddad et al, unpub.…”

Epidemiological impact of prioritising SARS-CoV-2 vaccination by antibody status: mathematical modelling analyses

“…3 17-19 The model description is summarised below, and further details can be found in the previous publications. 3 19 The model consisted of a set of coupled, non-linear differential equations and was structured by age (0-9, 10-19, …, ≥80 years) and grouped by the major nationalities of the population of Qatar. Unvaccinated and vaccinated populations were further stratified based on infection status (uninfected, infected), infection stage (mild/asymptomatic, severe, critical) and disease stage (severe disease requiring acute-care bed hospitalisation, critical disease requiring ICU-care bed hospitalisation) (online supplemental figure S1).…”

“…20 Duration of hospital stay in an acute-care bed and duration of hospital stay in an ICU-care bed were estimated through model fitting, at 7.4 days and 16.2 days, respectively. 19 The model assumes that infected persons are equally infectious regardless of symptoms. 19 The model was coded, fitted and analysed using MATLAB R2019a.…”

“…19 The model assumes that infected persons are equally infectious regardless of symptoms. 19 The model was coded, fitted and analysed using MATLAB R2019a. 21…”

“…The resulting distribution for each outcome predicted by the model was then used to derive the means and associated 95% uncertainty intervals for vaccine effectiveness at each time point. Further details about this type of uncertainty analysis can be found in the study by Ayoub et al 19 Given that the variants of concern may reduce the efficacy of the vaccines, 35 impact of the vaccine was assessed in a sensitivity analysis in which both VES and VEP were reduced and varied between 50% and 95%. In addition, the impact of the vaccine was assessed in another sensitivity analysis in which the vaccine duration of protection varied between 6 and 12 months.…”