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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.
Bingham, Jeremy; Tempia, Stefano; Moultrie, Harry; Viboud, Cecile; Jassat, Waasila; Cohen, Cheryl; Pulliam, Juliet R C.
Afiliação
  • Bingham J; South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.
  • Tempia S; Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa.
  • Moultrie H; School of Public Health, University of the Witwatersrand, Johannesburg, South Africa.
  • Viboud C; Centre for Tuberculosis, National Institute for Communicable Diseases, Division of the National Health Laboratory Service, Johannesburg, South Africa.
  • Jassat W; School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
  • Cohen C; Fogarty International Center, NIH, Bethesda, MD, USA.
  • Pulliam JRC; Division of Public Health Surveillance and Response, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa.
medRxiv ; 2022 Aug 01.
Article em En | MEDLINE | ID: mdl-35982666
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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Incidence_studies / Risk_factors_studies Idioma: En Revista: MedRxiv Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Incidence_studies / Risk_factors_studies Idioma: En Revista: MedRxiv Ano de publicação: 2022 Tipo de documento: Article