Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts
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Author(s)
Type
Journal Article
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 Issued
2023-10-18
Date Acceptance
2023-05-11
Citation
PLOS ONE, 2023, 18 (10)
ISSN
1932-6203
Publisher
PLOS
Journal / Book Title
PLOS ONE
Volume
18
Issue
10
Copyright Statement
© 2023 Bhatia et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
License URL
Identifier
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0286199
Subjects
EPIDEMIC
Multidisciplinary Sciences
Science & Technology
Science & Technology - Other Topics
Publication Status
Published
Article Number
e0286199
Date Publish Online
2023-10-18