The role of modelling and analytics in South African Covid-19 planning and budgeting

Bibliographic Data

ID19594318
AuthorsGesine Meyer‐Rath (0000-0003-0439-381X, Boston University, corresponding author), Rachel Hounsell (0000-0003-1393-4294, University of Cape Town, corresponding author), Rachel A Hounsell, Juliet R C Pulliam (0000-0003-3314-8223, Stellenbosch University, corresponding author), Lise Jamieson (0000-0003-2354-4580, University of the Witwatersrand, corresponding author), Brooke E Nichols (0000-0003-4682-4999, Boston University, corresponding author), Harry Moultrie (0000-0001-9722-0699, National Health Laboratory Service, corresponding author), Sheetal Silal (0000-0002-3007-5383, University of Cape Town, corresponding author)
EditorsEunha Shim (0000-0003-3135-6360)
Year2023
Volume3
Issue7
Pagese0001063
Publication date2023-07-03
Open AccessYes
TypeARTICLE
VenuePLOS Global Public Health (JOURNAL)
Journal identifiersISSN: 2767-3375 • E-ISSN: 2767-3375
PublisherPublic Library of Science (PLoS) (PUBLISHER)
DOI10.1371/journal.pgph.0001063
PMID37399174
OpenAlexW4382932357
LanguageEN
References cited17

Background The South African COVID-19 Modelling Consortium (SACMC) was established in late March 2020 to support planning and budgeting for COVID-19 related healthcare in South Africa. We developed several tools in response to the needs of decision makers in the different stages of the epidemic, allowing the South African government to plan several months ahead. Methods Our tools included epidemic projection models, several cost and budget impact models, and online dashboards to help government and the public visualise our projections, track case development and forecast hospital admissions. Information on new variants, including Delta and Omicron, were incorporated in real time to allow the shifting of scarce resources when necessary. Results Given the rapidly changing nature of the outbreak globally and in South Africa, the model projections were updated regularly. The updates reflected 1) the changing policy priorities over the course of the epidemic; 2) the availability of new data from South African data systems; and 3) the evolving response to COVID-19 in South Africa, such as changes in lockdown levels and ensuing mobility and contact rates, testing and contact tracing strategies and hospitalisation criteria. Insights into population behaviour required updates by incorporating notions of behavioural heterogeneity and behavioural responses to observed changes in mortality. We incorporated these aspects into developing scenarios for the third wave and developed additional methodology that allowed us to forecast required inpatient capacity. Finally, real-time analyses of the most important characteristics of the Omicron variant first identified in South Africa in November 2021 allowed us to advise policymakers early in the fourth wave that a relatively lower admission rate was likely. Conclusion The SACMC’s models, developed rapidly in an emergency setting and regularly updated with local data, supported national and provincial government to plan several months ahead, expand hospital capacity when needed, allocate budgets and procure additional resources where possible. Across four waves of COVID-19 cases, the SACMC continued to serve the planning needs of the government, tracking waves and supporting the national vaccine rollout

2019-20 coronavirus outbreak · Analytics · Business · Data science · Computer Science · COVID-19 epidemiological studies · Medicine · Vaccine Coverage and Hesitancy · Viral Infections and Outbreaks Research · Internal Medicine · Virology

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