Evidence map›Paper›PMID 37093784›Full record

ArticlePLOS global public health2023

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

Sheetal Prakash Silal, Juliet R C Pulliam, Gesine Meyer-Rath, Lise Jamieson, Brooke E Nichols, Jared Norman, Rachel Hounsell, Saadiyah Mayet, Frank Kagoro, Harry Moultrie

Open access · goldAbstract read
In one paragraph

Article in PLOS global public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 5 countries.

Sheetal Prakash SilalDepartment of Statistical Sciences, Modelling and Simulation Hub, Africa (MASHA), University of Cape Town, Cape Town, South Africa.ORCID https://orcid.org/0000-0002-3007-5383
Juliet R C PulliamSouth African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.ORCID https://orcid.org/0000-0003-3314-8223
Gesine Meyer-RathDepartment of Internal Medicine, Health Economics and Epidemiology Research Office, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.ORCID https://orcid.org/0000-0003-0439-381X
Lise JamiesonDepartment of Internal Medicine, Health Economics and Epidemiology Research Office, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.ORCID https://orcid.org/0000-0003-2354-4580
Brooke E NicholsDepartment of Internal Medicine, Health Economics and Epidemiology Research Office, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.ORCID https://orcid.org/0000-0003-4682-4999
Jared NormanDepartment of Statistical Sciences, Modelling and Simulation Hub, Africa (MASHA), University of Cape Town, Cape Town, South Africa.ORCID https://orcid.org/0000-0002-5692-999X
Rachel HounsellDepartment of Statistical Sciences, Modelling and Simulation Hub, Africa (MASHA), University of Cape Town, Cape Town, South Africa.ORCID https://orcid.org/0000-0003-1393-4294
Saadiyah MayetDepartment of Statistical Sciences, Modelling and Simulation Hub, Africa (MASHA), University of Cape Town, Cape Town, South Africa.
Frank KagoroDivision of Clinical Pharmacology, Department of Medicine, Collaborating Centre for Optimising Antimalarial Therapy, University of Cape Town, Cape Town, South Africa.ORCID https://orcid.org/0000-0003-4608-7167
Harry MoultrieNational Institute for Communicable Diseases (NICD), a Division of the National Health Laboratory Service, Johannesburg, South Africa.ORCID https://orcid.org/0000-0001-9722-0699
University of Cape Town · ZABoston University · USNational Health Laboratory Service · ZAStellenbosch University · ZAUniversity of the Witwatersrand · ZA

Funding

Wellcome Trust
6 · The paper itself

Abstract

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.

Identifiers

PMID37093784
PMCPMC10124849
OpenAlexW4366819626

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.