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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: We aimed 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 priv...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Cold Spring Harbor Laboratory
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9387150/ https://www.ncbi.nlm.nih.gov/pubmed/35982666 http://dx.doi.org/10.1101/2022.07.22.22277932 |
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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: We aimed 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: We estimated R from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalizations, and hospital-associated deaths, using a method which models daily incidence as a weighted sum of past incidence. We also estimated R 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 case-based estimates were higher during the fourth wave. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave. DISCUSSION: 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. High R estimates for Omicron relative to earlier waves is interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights the fact 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-9387150 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-93871502022-08-19 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. medRxiv Article OBJECTIVES: We aimed 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: We estimated R from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalizations, and hospital-associated deaths, using a method which models daily incidence as a weighted sum of past incidence. We also estimated R 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 case-based estimates were higher during the fourth wave. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave. DISCUSSION: 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. High R estimates for Omicron relative to earlier waves is interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights the fact 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. Cold Spring Harbor Laboratory 2022-08-01 /pmc/articles/PMC9387150/ /pubmed/35982666 http://dx.doi.org/10.1101/2022.07.22.22277932 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | 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 | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9387150/ https://www.ncbi.nlm.nih.gov/pubmed/35982666 http://dx.doi.org/10.1101/2022.07.22.22277932 |
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