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 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.
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UpdateIn: PLoS One. 2023 Sep 22;18(9):e0287026. - PMID 37738280 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2022 |
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| Enthalten in: |
medRxiv : the preprint server for health sciences - (2022) vom: 01. Aug. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bingham, Jeremy [VerfasserIn] |
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Date Revised 29.09.2023 published: Electronic UpdateIn: PLoS One. 2023 Sep 22;18(9):e0287026. - PMID 37738280 Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1101/2022.07.22.22277932 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM345012410 |
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| 100 | 1 | |a Bingham, Jeremy |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a 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 |
| 264 | 1 | |c 2022 | |
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| 500 | |a Date Revised 29.09.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a UpdateIn: PLoS One. 2023 Sep 22;18(9):e0287026. - PMID 37738280 | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Preprint | |
| 700 | 1 | |a Tempia, Stefano |e verfasserin |4 aut | |
| 700 | 1 | |a Moultrie, Harry |e verfasserin |4 aut | |
| 700 | 1 | |a Viboud, Cecile |e verfasserin |4 aut | |
| 700 | 1 | |a Jassat, Waasila |e verfasserin |4 aut | |
| 700 | 1 | |a Cohen, Cheryl |e verfasserin |4 aut | |
| 700 | 1 | |a Pulliam, Juliet R C |e verfasserin |4 aut | |
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