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

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 M...

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Autores principales: Silal, Sheetal Prakash, Pulliam, Juliet R. C., Meyer-Rath, Gesine, Jamieson, Lise, Nichols, Brooke E., Norman, Jared, Hounsell, Rachel, Mayet, Saadiyah, Kagoro, Frank, Moultrie, Harry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10124849/
https://www.ncbi.nlm.nih.gov/pubmed/37093784
http://dx.doi.org/10.1371/journal.pgph.0001070
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author Silal, Sheetal Prakash
Pulliam, Juliet R. C.
Meyer-Rath, Gesine
Jamieson, Lise
Nichols, Brooke E.
Norman, Jared
Hounsell, Rachel
Mayet, Saadiyah
Kagoro, Frank
Moultrie, Harry
author_facet Silal, Sheetal Prakash
Pulliam, Juliet R. C.
Meyer-Rath, Gesine
Jamieson, Lise
Nichols, Brooke E.
Norman, Jared
Hounsell, Rachel
Mayet, Saadiyah
Kagoro, Frank
Moultrie, Harry
author_sort Silal, Sheetal Prakash
collection PubMed
description 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.
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spelling pubmed-101248492023-04-25 The National COVID-19 Epi Model (NCEM): Estimating cases, admissions and deaths for the first wave of COVID-19 in South Africa Silal, Sheetal Prakash Pulliam, Juliet R. C. Meyer-Rath, Gesine Jamieson, Lise Nichols, Brooke E. Norman, Jared Hounsell, Rachel Mayet, Saadiyah Kagoro, Frank Moultrie, Harry PLOS Glob Public Health Research Article 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. Public Library of Science 2023-04-24 /pmc/articles/PMC10124849/ /pubmed/37093784 http://dx.doi.org/10.1371/journal.pgph.0001070 Text en © 2023 Silal et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Silal, Sheetal Prakash
Pulliam, Juliet R. C.
Meyer-Rath, Gesine
Jamieson, Lise
Nichols, Brooke E.
Norman, Jared
Hounsell, Rachel
Mayet, Saadiyah
Kagoro, Frank
Moultrie, Harry
The National COVID-19 Epi Model (NCEM): Estimating cases, admissions and deaths for the first wave of COVID-19 in South Africa
title The National COVID-19 Epi Model (NCEM): Estimating cases, admissions and deaths for the first wave of COVID-19 in South Africa
title_full The National COVID-19 Epi Model (NCEM): Estimating cases, admissions and deaths for the first wave of COVID-19 in South Africa
title_fullStr The National COVID-19 Epi Model (NCEM): Estimating cases, admissions and deaths for the first wave of COVID-19 in South Africa
title_full_unstemmed The National COVID-19 Epi Model (NCEM): Estimating cases, admissions and deaths for the first wave of COVID-19 in South Africa
title_short The National COVID-19 Epi Model (NCEM): Estimating cases, admissions and deaths for the first wave of COVID-19 in South Africa
title_sort national covid-19 epi model (ncem): estimating cases, admissions and deaths for the first wave of covid-19 in south africa
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10124849/
https://www.ncbi.nlm.nih.gov/pubmed/37093784
http://dx.doi.org/10.1371/journal.pgph.0001070
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