The Quixotic Task of Forecasting Peaks of Covid-19

Rather Focus on Forward and Backward Projections

Bibliographic Data

ID22082051
AuthorsRuy Freitas Reis (0000-0001-5215-3744, Universidade Federal de Juiz de Fora), Rafael Sachetto Oliveira (0000-0003-0800-5984, Federal University of São João del-Rei), Bárbara de Melo Quintela (0000-0002-5320-4109, Universidade Federal de Juiz de Fora), Joventino de Oliveira Campos (0000-0003-1899-9441, Federal Center for Technological Education of Minas Gerais), Johnny Moreira Gomes (0000-0001-6679-047X, Universidade Federal de Juiz de Fora), Bernardo Martins Rocha (0000-0002-0508-8959, Universidade Federal de Juiz de Fora), Marcelo Lobosco (0000-0002-7205-9509, Universidade Federal de Juiz de Fora), Rodrigo Weber Dos Santos (0000-0002-0633-1391, Universidade Federal de Juiz de Fora, corresponding author)
Year2021
Volume9
Pages623521-623521
Publication date2021-03-16
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2021.623521
PMID33796495
OpenAlexW3136225073
LanguageEN
Citations received2
References cited49

Over the last months, mathematical models have been extensively used to help control the COVID-19 pandemic worldwide. Although extremely useful in many tasks, most models have performed poorly in forecasting the pandemic peaks. We investigate this common pitfall by forecasting four countries' pandemic peak: Austria, Germany, Italy, and South Korea. Far from the peaks, our models can forecast the pandemic dynamics 20 days ahead. Nevertheless, when calibrating our models close to the day of the pandemic peak, all forecasts fail. Uncertainty quantification and sensitivity analysis revealed the main obstacle: the misestimation of the transmission rate. Inverse uncertainty quantification has shown that significant changes in transmission rate commonly precede a peak. These changes are a key factor in forecasting the pandemic peak. Long forecasts of the pandemic peak are therefore undermined by the lack of models that can forecast changes in the transmission rate, i.e., how a particular society behaves, changes of mitigation policies, or how society chooses to respond to them. In addition, our studies revealed that even short forecasts of the pandemic peak are challenging. Backward projections have shown us that the correct estimation of any temporal change in the transmission rate is only possible many days ahead. Our results suggest that the distance between a change in the transmission rate and its correct identification in the curve of active infected cases can be as long as 15 days. This is intrinsic to the phenomenon and how it affects epidemic data: a new case is usually only reported after an incubation period followed by a delay associated with the test. In summary, our results suggest the phenomenon itself challenges the task of forecasting the peak of the COVID-19 pandemic when only epidemic data is available. Nevertheless, we show that exciting results can be obtained when using the same models to project different scenarios of reduced transmission rates. Therefore, our results highlight that mathematical modeling can help control COVID-19 pandemic by backward projections that characterize the phenomena' essential features and forward projections when different scenarios and strategies can be tested and used for decision-making

Biology · Econometrics · Economics · Pandemic · Statistics · Transmission rate · Computer Science · COVID-19 epidemiological studies · Mathematics · Medicine · SARS-CoV-2 and COVID-19 Research · Viral Infections and Outbreaks Research

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