TY - JOUR
T1 - Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts
AU - Bhatia, Sangeeta
AU - Parag, Kris V.
AU - Wardle, Jack
AU - Nash, Rebecca K.
AU - Imai, Natsuko
AU - Van Elsland, Sabine L.
AU - Lassmann, Britta
AU - Brownstein, John S.
AU - Desai, Angel
AU - Herringer, Mark
AU - Sewalk, Kara
AU - Loeb, Sarah Claire
AU - Ramatowski, John
AU - Cuomo-Dannenburg, Gina
AU - Jauneikaite, Elita
AU - Unwin, H. T.Juliette
AU - Riley, Steven
AU - Ferguson, Neil
AU - Donnelly, Christl A.
AU - Cori, Anne
AU - Nouvellet, Pierre
N1 - Publisher Copyright:
© 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.
PY - 2023/10
Y1 - 2023/10
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85174749018
U2 - 10.1371/journal.pone.0286199
DO - 10.1371/journal.pone.0286199
M3 - Article
C2 - 37851661
AN - SCOPUS:85174749018
SN - 1932-6203
VL - 18
JO - PLoS ONE
JF - PLoS ONE
IS - 10 October
M1 - e0286199
ER -
