The National COVID-19 Epi Model (NCEM): Estimating cases, admissions and deaths in South Africa

ABSTRACT

BackgroundIn March 2020 the South African COVID-19 Modelling Consortium was formed to support government planning for COVID-19 cases and related healthcare. Models were developed jointly by local disease modelling groups to estimate cases, resource needs and deaths due to COVID-19. MethodsThe National COVID-19 Epi Model (NCEM) while initially developed as a deterministic compartmental model of SARS-Cov-2 transmission in the nine provinces of South Africa, was adapted several times over the course of the first wave of infection in response to emerging local data and changing needs of government. By the end of the first wave, the NCEM had developed into a stochastic, spatially-explicit compartmental transmission model to estimate the total and reported incidence of COVID-19 across the 52 districts of South Africa. The model adopted a generalised Susceptible-Exposed-Infectious-Removed structure that accounted for the clinical profile of SARS-COV-2 (asymptomatic, mild, severe and critical cases) and avenues of treatment access (outpatient, and hospitalisation in non-ICU and ICU wards). ResultsBetween end-March and early September 2020, the model was updated several times to generate new sets of projections and scenario analyses to be shared with planners in the national and provincial Departments of Health, the National Treasury and other partners in a variety of formats such as presentations, reports and dashboards. Updates to model structure included finer spatial granularity, limited access to treatment, and the inclusion of behavioural heterogeneity in relation to the adoption of Public Health and Social Measures. These updates were made in response to local data and knowledge and the changing needs of the planners. ConclusionsThe NCEM attempted to incorporate a high level of local data to contextualise the model appropriately to address South Africas population and health system characteristics. Origin and contextualisation of data and understanding of the populations interaction with the health system played a vital role in producing and updating estimates of resource needs, demonstrating the importance of harnessing and developing local modelling capacity.