Cargando…

Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves

OBJECTIVES: The aim of this study was to quantify transmission trends in South Africa during the first four waves of the COVID-19 pandemic using estimates of the time-varying reproduction number (R) and to compare the robustness of R estimates based on three different data sources, and using data fr...

Descripción completa

Detalles Bibliográficos
Autores principales: Bingham, Jeremy, Tempia, Stefano, Moultrie, Harry, Viboud, Cecile, Jassat, Waasila, Cohen, Cheryl, Pulliam, Juliet R.C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516415/
https://www.ncbi.nlm.nih.gov/pubmed/37738280
http://dx.doi.org/10.1371/journal.pone.0287026
_version_ 1785109122959540224
author Bingham, Jeremy
Tempia, Stefano
Moultrie, Harry
Viboud, Cecile
Jassat, Waasila
Cohen, Cheryl
Pulliam, Juliet R.C.
author_facet Bingham, Jeremy
Tempia, Stefano
Moultrie, Harry
Viboud, Cecile
Jassat, Waasila
Cohen, Cheryl
Pulliam, Juliet R.C.
author_sort Bingham, Jeremy
collection PubMed
description OBJECTIVES: The aim of this study was to quantify transmission trends in South Africa during the first four waves of the COVID-19 pandemic using estimates of the time-varying reproduction number (R) and to compare the robustness of R estimates based on three different data sources, and using data from public and private sector service providers. METHODS: R was estimated from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalisations, and hospital-associated deaths, using a method that models daily incidence as a weighted sum of past incidence, as implemented in the R package EpiEstim. R was also estimated separately using public and private sector data. RESULTS: Nationally, the maximum case-based R following the introduction of lockdown measures was 1.55 (CI: 1.43–1.66), 1.56 (CI: 1.47–1.64), 1.46 (CI: 1.38–1.53) and 3.33 (CI: 2.84–3.97) during the first (Wuhan-Hu), second (Beta), third (Delta), and fourth (Omicron) waves, respectively. Estimates based on the three data sources (cases, hospitalisations, deaths) were generally similar during the first three waves, but higher during the fourth wave for case-based estimates. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave. CONCLUSION: Agreement between R estimates using different data sources during the first three waves suggests that data from any of these sources could be used in the early stages of a future pandemic. The high R estimates for Omicron relative to earlier waves are interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights that clients of the public and private sectors did not experience two separate epidemics, except perhaps to a limited extent during the strictest lockdowns in the first wave.
format Online
Article
Text
id pubmed-10516415
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-105164152023-09-23 Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves Bingham, Jeremy Tempia, Stefano Moultrie, Harry Viboud, Cecile Jassat, Waasila Cohen, Cheryl Pulliam, Juliet R.C. PLoS One Research Article OBJECTIVES: The aim of this study was to quantify transmission trends in South Africa during the first four waves of the COVID-19 pandemic using estimates of the time-varying reproduction number (R) and to compare the robustness of R estimates based on three different data sources, and using data from public and private sector service providers. METHODS: R was estimated from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalisations, and hospital-associated deaths, using a method that models daily incidence as a weighted sum of past incidence, as implemented in the R package EpiEstim. R was also estimated separately using public and private sector data. RESULTS: Nationally, the maximum case-based R following the introduction of lockdown measures was 1.55 (CI: 1.43–1.66), 1.56 (CI: 1.47–1.64), 1.46 (CI: 1.38–1.53) and 3.33 (CI: 2.84–3.97) during the first (Wuhan-Hu), second (Beta), third (Delta), and fourth (Omicron) waves, respectively. Estimates based on the three data sources (cases, hospitalisations, deaths) were generally similar during the first three waves, but higher during the fourth wave for case-based estimates. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave. CONCLUSION: Agreement between R estimates using different data sources during the first three waves suggests that data from any of these sources could be used in the early stages of a future pandemic. The high R estimates for Omicron relative to earlier waves are interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights that clients of the public and private sectors did not experience two separate epidemics, except perhaps to a limited extent during the strictest lockdowns in the first wave. Public Library of Science 2023-09-22 /pmc/articles/PMC10516415/ /pubmed/37738280 http://dx.doi.org/10.1371/journal.pone.0287026 Text en © 2023 Bingham 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
Bingham, Jeremy
Tempia, Stefano
Moultrie, Harry
Viboud, Cecile
Jassat, Waasila
Cohen, Cheryl
Pulliam, Juliet R.C.
Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves
title Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves
title_full Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves
title_fullStr Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves
title_full_unstemmed Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves
title_short Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves
title_sort estimating the time-varying reproduction number for covid-19 in south africa during the first four waves using multiple measures of incidence for public and private sectors across four waves
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516415/
https://www.ncbi.nlm.nih.gov/pubmed/37738280
http://dx.doi.org/10.1371/journal.pone.0287026
work_keys_str_mv AT binghamjeremy estimatingthetimevaryingreproductionnumberforcovid19insouthafricaduringthefirstfourwavesusingmultiplemeasuresofincidenceforpublicandprivatesectorsacrossfourwaves
AT tempiastefano estimatingthetimevaryingreproductionnumberforcovid19insouthafricaduringthefirstfourwavesusingmultiplemeasuresofincidenceforpublicandprivatesectorsacrossfourwaves
AT moultrieharry estimatingthetimevaryingreproductionnumberforcovid19insouthafricaduringthefirstfourwavesusingmultiplemeasuresofincidenceforpublicandprivatesectorsacrossfourwaves
AT viboudcecile estimatingthetimevaryingreproductionnumberforcovid19insouthafricaduringthefirstfourwavesusingmultiplemeasuresofincidenceforpublicandprivatesectorsacrossfourwaves
AT jassatwaasila estimatingthetimevaryingreproductionnumberforcovid19insouthafricaduringthefirstfourwavesusingmultiplemeasuresofincidenceforpublicandprivatesectorsacrossfourwaves
AT cohencheryl estimatingthetimevaryingreproductionnumberforcovid19insouthafricaduringthefirstfourwavesusingmultiplemeasuresofincidenceforpublicandprivatesectorsacrossfourwaves
AT pulliamjulietrc estimatingthetimevaryingreproductionnumberforcovid19insouthafricaduringthefirstfourwavesusingmultiplemeasuresofincidenceforpublicandprivatesectorsacrossfourwaves