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

Author:

Bhatia SangeetaORCID,Parag Kris V.,Wardle JackORCID,Nash Rebecca K.ORCID,Imai NatsukoORCID,Elsland Sabine L. Van,Lassmann Britta,Brownstein John S.,Desai Angel,Herringer Mark,Sewalk KaraORCID,Loeb Sarah Claire,Ramatowski JohnORCID,Cuomo-Dannenburg Gina,Jauneikaite ElitaORCID,Unwin H. Juliette T.ORCID,Riley Steven,Ferguson Neil,Donnelly Christl A.,Cori Anne,Nouvellet Pierre

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.

Funder

UK Medical Research Council

Wellcome Trust

National Institute of Health and Care Research

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

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