Journal article
The National COVID-19 Epi Model (NCEM): estimating cases, admissions and deaths for the first wave of COVID-19 in South Africa
- Abstract:
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In 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. The 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). Between end-March and early September 2020, the model was updated 11 times with four key releases 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. 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. The NCEM attempted to incorporate a high level of local data to contextualise the model appropriately to address South Africa’s population and health system characteristics that played a vital role in producing and updating estimates of resource needs, demonstrating the importance of harnessing and developing local modelling capacity.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.6MB, Terms of use)
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- Publisher copy:
- 10.1371/journal.pgph.0001070
Authors
- Publisher:
- Public Library of Science
- Journal:
- PLOS Global Public Health More from this journal
- Volume:
- 3
- Issue:
- 4
- Article number:
- e0001070
- Publication date:
- 2023-04-24
- Acceptance date:
- 2023-03-27
- DOI:
- EISSN:
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2767-3375
- ISSN:
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2767-3375
- Pmid:
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37093784
- Language:
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English
- Keywords:
- Pubs id:
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1339137
- Local pid:
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pubs:1339137
- Deposit date:
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2023-05-10
- ARK identifier:
Terms of use
- Copyright holder:
- Silal et al.
- Copyright date:
- 2023
- Rights statement:
- © 2023 Silal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Notes:
- This research was funded in whole or in part by Wellcome Trust [GN: 2114236/Z/18Z]. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission.
- Licence:
- CC Attribution (CC BY)
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