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


Journal

PLOS global public health
ISSN: 2767-3375
Titre abrégé: PLOS Glob Public Health
Pays: United States
ID NLM: 9918283779606676

Informations de publication

Date de publication:
2023
Historique:
received: 22 08 2022
accepted: 24 05 2023
medline: 3 7 2023
pubmed: 3 7 2023
entrez: 3 7 2023
Statut: epublish

Résumé

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. 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. 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. 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.

Sections du résumé

BACKGROUND 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 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 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 CONCLUSIONS
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.

Identifiants

pubmed: 37399174
doi: 10.1371/journal.pgph.0001063
pii: PGPH-D-22-01376
pmc: PMC10317222
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e0001063

Informations de copyright

Copyright: © 2023 Meyer-Rath 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.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

BMJ Glob Health. 2021 Feb;6(2):
pubmed: 33627361
PLOS Glob Public Health. 2023 Apr 24;3(4):e0001070
pubmed: 37093784
Lancet. 2020 Mar 28;395(10229):1054-1062
pubmed: 32171076
Int J Infect Dis. 2020 Jul;96:288-290
pubmed: 32437933
PLOS Glob Public Health. 2023 May 17;3(5):e0001073
pubmed: 37195977
J Clin Med. 2020 May 01;9(5):
pubmed: 32369975
Science. 2020 May 1;368(6490):489-493
pubmed: 32179701
J Infect Dis. 2020 Jun 16;222(1):26-33
pubmed: 32339231
BMJ. 2020 Apr 2;369:m1375
pubmed: 32241884
Lancet Glob Health. 2021 Sep;9(9):e1216-e1225
pubmed: 34252381
Thorax. 2020 Aug;75(8):693-694
pubmed: 32461231
Lancet. 2020 Feb 15;395(10223):497-506
pubmed: 31986264
Nat Med. 2020 May;26(5):672-675
pubmed: 32296168
Ann Intern Med. 2020 May 05;172(9):577-582
pubmed: 32150748
N Engl J Med. 2020 May 28;382(22):2163-2164
pubmed: 32283004
Int J Health Policy Manag. 2021 Apr 25;:
pubmed: 33949817
Radiol Cardiothorac Imaging. 2020 Mar 17;2(2):e200110
pubmed: 33778566
Euro Surveill. 2020 Apr;25(17):
pubmed: 32372755
Euro Surveill. 2020 Feb;25(5):
pubmed: 32046819
Swiss Med Wkly. 2020 Aug 05;150:w20336
pubmed: 32757177
Emerg Infect Dis. 2020 Jul;26(7):
pubmed: 32364890
JAMA. 2020 Mar 17;323(11):1061-1069
pubmed: 32031570
Nature. 2020 May;581(7809):465-469
pubmed: 32235945
Lancet Infect Dis. 2020 Jun;20(6):669-677
pubmed: 32240634
Int J Infect Dis. 2020 Apr;93:284-286
pubmed: 32145466
Science. 2022 May 6;376(6593):eabn4947
pubmed: 35289632
Sci Adv. 2020 Aug 14;6(33):eabc1202
pubmed: 32851189
J Clin Med. 2020 Feb 17;9(2):
pubmed: 32079150

Auteurs

Gesine Meyer-Rath (G)

Faculty of Health Sciences, Health Economics and Epidemiology Research Office, University of the Witwatersrand, Johannesburg, South Africa.
Department of Global Health, School of Public Health, Boston University, Boston, Massachusetts, United States of America.
South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.

Rachel A Hounsell (RA)

Department of Statistical Sciences, Modelling and Simulation Hub, Africa, University of Cape Town, Cape Town, South Africa.
Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom.

Juliet Rc Pulliam (JR)

South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.

Lise Jamieson (L)

Faculty of Health Sciences, Health Economics and Epidemiology Research Office, University of the Witwatersrand, Johannesburg, South Africa.
South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.
Department of Medical Microbiology, Amsterdam University Medical Center, Amsterdam, the Netherlands.

Brooke E Nichols (BE)

Faculty of Health Sciences, Health Economics and Epidemiology Research Office, University of the Witwatersrand, Johannesburg, South Africa.
Department of Global Health, School of Public Health, Boston University, Boston, Massachusetts, United States of America.
Department of Medical Microbiology, Amsterdam University Medical Center, Amsterdam, the Netherlands.
Foundation for Innovative New Diagnostics, Geneva, Switzerland.

Harry Moultrie (H)

National Institute for Communicable Diseases (NICD), a division of the National Health Laboratory Service, Johannesburg, South Africa.

Sheetal P Silal (SP)

Department of Statistical Sciences, Modelling and Simulation Hub, Africa, University of Cape Town, Cape Town, South Africa.
Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom.

Classifications MeSH