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

Silal, Sheetal P; Pulliam, Juliet R. C.; Meyer‐Rath, Gesine; Jamieson, Lise; Nichols, Brooke E; Norman, Jared; Hounsell, Rachel A.; Mayet, Saadiyah; Kagoro, Frank; Moultrie, Harry

doi:10.1101/2022.09.05.22279174

Cited by 6 publications

(18 citation statements)

References 12 publications

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“…While these projections incorporated a certain level of effectiveness of PHSM in their scenarios, they underestimated the severity and effect of government restrictions and population adherence to them and thus assumed that all exposure would happen during a single wave. A full set of projections for all nine provinces is available in our detailed reports [42, 43].…”

Section: Resultsmentioning

confidence: 99%

“…This is particularly relevant for important but rapidly changing inputs such as available hospital capacity, which has been difficult to estimate throughout the epidemic as facility capacity changed regularly. [42-44]. Additionally, we realised that, while our initial projections had intentionally projected the number of beds needed without taking capacity constraints into account, as well as the cost of providing oxygen and other resources to all critical cases, the actual capacity to accommodate severe and critical cases was much lower, leading to the addition of outputs (including budgets) based on actual bed use .…”

Section: Discussionmentioning

confidence: 99%

“…As such, the adaptation of plug-and-play models from resource-richer countries remains an inferior option, as these are usually ill-equipped to provide reliable, ongoing, real-time decision-making support tailored to local needs, data and epidemic specifics. One example of this is our mis-specification of mortality rates in v1 of our model which was a result of a misinterpretation of international data on case fatality rates-in March/ April, 2020, the only available data [42]. Incidentally, updating the model with locally derived mortality rates after the first South African wave resulted in very similar, albeit more correct, estimates of the number of overall deaths [44].…”

Section: Discussionmentioning

confidence: 99%

“…At the start of the epidemic, the most pressing need was for short- and long-term projections of COVID-19 cases, including the number of severe and critical cases requiring hospital admission, and deaths under different PHSM scenarios. To fulfil these needs, the SACMC developed the National COVID-19 Epi Model (NCEM), a compartmental transmission model following a generalised Susceptible-Exposed-Infectious-Removed structure that accounts for disease severity (asymptomatic, mild, severe, and critical cases) and treatment pathways (outpatient, inpatient non-ICU and ICU care) [3].…”

Section: Methodsmentioning

confidence: 99%

“…The aim is to highlight the dynamic, multidisciplinary nature of policy-driven modelling of an emergency in a country with severely constrained resources. A full description of the models developed during wave 1 is provided in [3], and our analysis of COVID-19 related hospitalisations in [4].…”

Section: Introductionmentioning

confidence: 99%

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The Role of Modelling and Analytics in South African COVID-19 Planning and Budgeting

Meyer‐Rath

Hounsell

Pulliam

et al. 2022

Preprint

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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 of time. 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 of time, 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.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Role of Modelling and Analytics in South African COVID-19 Planning and Budgeting

Meyer‐Rath

Hounsell

Pulliam

et al. 2022

Preprint

Self Cite

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A multistage mixed-methods evaluation protocol for the national testing response during the COVID-19 pandemic in England

Naidoo

Andersen-waine

Dahal

et al. 2022

Preprint

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Introduction: In 2020, the UK Health Security Agency (UKHSA) established a large-scale testing programme to rapidly identify individuals in England who were infected with SARS-CoV-2 and had COVID-19. This comprised part of the UK government′s COVID-19 response strategy, to protect those at risk of severe COVID-19 disease and death and to reduce the burden on the health system. To assess the success of this approach, UKHSA commissioned an independent evaluation of the activities delivered by the NHS testing programme in England. The primary purpose of this evaluation is to capture key learnings from the rollout of testing to different target populations via various testing services between October 2020 and March 2022 and to use these insights to formulate recommendations for future pandemic preparedness strategy. Methods and analysis: The proposed study involves a stepwise mixed-methods approach, aligned with established methods for the evaluation of complex interventions in health, with retrospective and prospective components. A bottom-up approach will be taken, focusing on each of nine population-specific service settings. We will use a Theory of Change to understand the causal pathways and intended and unintended outcomes of each service, also exploring the effect of context on each individual service setting′s intended outcomes. Subsequently, the insights gained will be synthesised to identify process and outcome indicators to evaluate how the combined aims of the testing programme were achieved. A forward-looking, prospective component of this work will aim to inform testing strategy in preparation for future pandemics, through a participatory modelling simulation and policy analysis exercise. Disclaimer: This is a provisional draft protocol that represents research in progress. This research was commissioned and funded by UKHSA, to be performed between August 2022 and March 2023. The scope and depth of testing services and channels covered by this research were pre-agreed with UKHSA and are limited to the availability and provision of data available at the time this protocol was written.

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

Meyer‐Rath

Hounsell

Pulliam

et al. 2023

PLOS Glob Public Health

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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.

show abstract

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

Cited by 6 publications

References 12 publications

The Role of Modelling and Analytics in South African COVID-19 Planning and Budgeting

The Role of Modelling and Analytics in South African COVID-19 Planning and Budgeting

A multistage mixed-methods evaluation protocol for the national testing response during the COVID-19 pandemic in England

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

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