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Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts.
Bhatia, Sangeeta; Parag, Kris V; Wardle, Jack; Nash, Rebecca K; Imai, Natsuko; Elsland, Sabine L Van; Lassmann, Britta; Brownstein, John S; Desai, Angel; Herringer, Mark; Sewalk, Kara; Loeb, Sarah Claire; Ramatowski, John; Cuomo-Dannenburg, Gina; Jauneikaite, Elita; Unwin, H Juliette T; Riley, Steven; Ferguson, Neil; Donnelly, Christl A; Cori, Anne; Nouvellet, Pierre.
Afiliación
  • Bhatia S; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.
  • Parag KV; NIHR Health Protection Research Unit in Modelling and Health Economics, Modelling & Economics Unit, UK Health Security Agency, London, United Kingdom.
  • Wardle J; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.
  • Nash RK; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.
  • Imai N; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.
  • Elsland SLV; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.
  • Lassmann B; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.
  • Brownstein JS; ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America.
  • Desai A; Boston Children's Hospital, Computational Epidemiology Lab, Boston, MA, United States of America.
  • Herringer M; ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America.
  • Sewalk K; Division of Infectious Diseases, Department of Internal Medicine, University of California Davis, Sacramento, California, United States of America.
  • Loeb SC; Healthsites.io, The Global Healthsites Mapping Project, London, United Kingdom.
  • Ramatowski J; Boston Children's Hospital, Computational Epidemiology Lab, Boston, MA, United States of America.
  • Cuomo-Dannenburg G; ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America.
  • Jauneikaite E; ProMED-mail, International Society for Infectious Diseases, Brookline, MA, United States of America.
  • Unwin HJT; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.
  • Riley S; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.
  • Ferguson N; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.
  • Donnelly CA; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.
  • Cori A; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.
  • Nouvellet P; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.
PLoS One ; 18(10): e0286199, 2023.
Article en En | MEDLINE | ID: mdl-37851661
ABSTRACT
Since 8th March 2020 up to the time of writing, we have been producing near real-time weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for all countries with evidence of sustained transmission, shared online. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. Here we present a retrospective evaluation of the forecasts produced between 8th March to 29th November 2020 for 81 countries. We evaluated the robustness of the forecasts produced in real-time using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. The retrospective evaluation of our models shows that simple transmission models calibrated using routine disease surveillance data can reliably capture the epidemic trajectory in multiple countries. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Epidemias / COVID-19 Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Epidemias / COVID-19 Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Reino Unido
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