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Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts

Sangeeta Bhatia, Kris V Parag, Jack Wardle, Rebecca K Nash, Natsuko Imai, Sabine L Van Elsland, Britta Lassmann, John S Brownstein, Angel Desai, Mark Herringer, Kara Sewalk, Sarah Claire Loeb, John Ramatowski, Gina Cuomo-Dannenburg, Elita Jauneikaite, H Juliette T Unwin, Steven Riley, Neil Ferguson, Christl A Donnelly, Anne Cori and Pierre Nouvellet

PLOS ONE, 2023, vol. 18, issue 10, 1-17

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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0286199

DOI: 10.1371/journal.pone.0286199

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