Modeling and forecasting the Covid‐19 pandemic time‐series data

Datos Bibliográficos

ID10763549
AutoresJurgen A Doornik (0000-0002-0619-0955, Nuffield College, Oxford, UK), Jennifer L Castle (0000-0001-9325-8024, MAGDALEN COLLEGE, OXFORD, UK, autor de correspondencia), David F Hendry (0000-0002-8013-576X, Nuffield College, Oxford, UK)
Año2021
Volumen102
Número5
Páginas2070-2087
Fecha de publicación2021-09-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaSocial Science Quarterly (JOURNAL)
Identificadores de la revistaISSN: 0038-4941 • E-ISSN: 1540-6237
EditorialWiley (PUBLISHER • GB)
DOI10.1111/ssqu.13008
PMID34548702
OpenAlexW3165386609
IdiomaEN
Citas recibidas6
Referencias citadas15

Objective: We analyze the number of recorded cases and deaths of COVID-19 in many parts of the world, with the aim to understand the complexities of the data, and produce regular forecasts. Methods: The SARS-CoV-2 virus that causes COVID-19 has affected societies in all corners of the globe but with vastly differing experiences across countries. Health-care and economic systems vary significantly across countries, as do policy responses, including testing, intermittent lockdowns, quarantine, contact tracing, mask wearing, and social distancing. Despite these challenges, the reported data can be used in many ways to help inform policy. We describe how to decompose the reported time series of confirmed cases and deaths into a trend, seasonal, and irregular component using machine learning methods. Results: This decomposition enables statistical computation of measures of the mortality ratio and reproduction number for any country, and we conduct a counterfactual exercise assuming that the United States had a summer outcome in 2020 similar to that of the European Union. The decomposition is also used to produce forecasts of cases and deaths, and we undertake a forecast comparison which highlights the importance of seasonality in the data and the difficulties of forecasting too far into the future. Conclusion: Our adaptive data-based methods and purely statistical forecasts provide a useful complement to the output from epidemiological models

Contact tracing · Coronavirus disease 2019 (COVID-19) · Counterfactual thinking · Econometrics · Economics · Geography · Globe · Pandemic · Quarantine · Social distance · Statistics · Time series · Computer Science · COVID-19 and healthcare impacts · COVID-19 epidemiological studies · Immune responses and vaccinations · Mathematics · Medicine · Psychology