The National COVID-19 Epi Model (NCEM) : Estimating cases, admissions and deaths for the first wave of COVID-19 in South Africa
Copyright: © 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..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:3 |
|---|---|
| Enthalten in: |
PLOS global public health - 3(2023), 4 vom: 24., Seite e0001070 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Silal, Sheetal Prakash [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Revised 19.09.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1371/journal.pgph.0001070 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM355974908 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM355974908 | ||
| 003 | DE-627 | ||
| 005 | 20231226065459.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1371/journal.pgph.0001070 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1186.xml |
| 035 | |a (DE-627)NLM355974908 | ||
| 035 | |a (NLM)37093784 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Silal, Sheetal Prakash |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The National COVID-19 Epi Model (NCEM) |b Estimating cases, admissions and deaths for the first wave of COVID-19 in South Africa |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 19.09.2023 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Copyright: © 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. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Pulliam, Juliet R C |e verfasserin |4 aut | |
| 700 | 1 | |a Meyer-Rath, Gesine |e verfasserin |4 aut | |
| 700 | 1 | |a Jamieson, Lise |e verfasserin |4 aut | |
| 700 | 1 | |a Nichols, Brooke E |e verfasserin |4 aut | |
| 700 | 1 | |a Norman, Jared |e verfasserin |4 aut | |
| 700 | 1 | |a Hounsell, Rachel |e verfasserin |4 aut | |
| 700 | 1 | |a Mayet, Saadiyah |e verfasserin |4 aut | |
| 700 | 1 | |a Kagoro, Frank |e verfasserin |4 aut | |
| 700 | 1 | |a Moultrie, Harry |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t PLOS global public health |d 2021 |g 3(2023), 4 vom: 24., Seite e0001070 |w (DE-627)NLM332849783 |x 2767-3375 |7 nnns |
| 773 | 1 | 8 | |g volume:3 |g year:2023 |g number:4 |g day:24 |g pages:e0001070 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1371/journal.pgph.0001070 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 3 |j 2023 |e 4 |b 24 |h e0001070 | ||