Country-report pattern corrections of new cases allow accurate 2-week predictions of COVID-19 evolution with the Gompertz model
Author
Other authors
Publication date
2024-05-11ISSN
2045-2322
Abstract
Accurate short-term predictions of COVID-19 cases with empirical models allow Health Officials to prepare for hospital contingencies in a two–three week window given the delay between case reporting and the admission of patients in a hospital. We investigate the ability of Gompertz-type empiric models to provide accurate prediction up to two and three weeks to give a large window of preparation in case of a surge in virus transmission. We investigate the stability of the prediction and its accuracy using bi-weekly predictions during the last trimester of 2020 and 2021. Using data from 2020, we show that understanding and correcting for the daily reporting structure of cases in the different countries is key to accomplish accurate predictions. Furthermore, we found that filtering out predictions that are highly unstable to changes in the parameters of the model, which are roughly 20%, reduces strongly the number of predictions that are way-off. The method is then tested for robustness with data from 2021. We found that, for this data, only 1–2% of the one-week predictions were off by more than 50%. This increased to 3% for two-week predictions, and only for three-week predictions it reached 10%.
Document Type
Article
Document version
Published version
Language
English
Subject (CDU)
616 - Pathology. Clinical medicine
Keywords
Computational models
Epidemiology
Pages
14 p.
Publisher
Nature Research
Is part of
Scientific Reports. 2024;14:10775
Grant agreement number
info:eu-repo/grantAgreement/DEC/SGR/2021 SGR 00582
info:eu-repo/grantAgreement/AEI-MCIN/PN I+D/PID-2022-139216NB-I00
This item appears in the following Collection(s)
Rights
© L'autor/a
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/