Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures.
COVID-19
Coronavirus
Lockdown
Machine learning
Journal
Journal of population economics
ISSN: 0933-1433
Titre abrégé: J Popul Econ
Pays: Germany
ID NLM: 101084221
Informations de publication
Date de publication:
2021
2021
Historique:
received:
05
05
2020
accepted:
19
08
2020
pubmed:
2
9
2020
medline:
2
9
2020
entrez:
2
9
2020
Statut:
ppublish
Résumé
Identifying structural breaks in the dynamics of COVID-19 contagion is crucial to promptly assess policies and evaluate the effectiveness of lockdown measures. However, official data record infections after a critical and unpredictable delay. Moreover, people react to the health risks of the virus and also anticipate lockdowns. All of this makes it complex to quickly and accurately detect changing patterns in the virus's infection dynamic. We propose a machine learning procedure to identify structural breaks in the time series of COVID-19 cases. We consider the case of Italy, an early-affected country that was unprepared for the situation, and detect the dates of structural breaks induced by three national lockdowns so as to evaluate their effects and identify some related policy issues. The strong but significantly delayed effect of the first lockdown suggests a relevant announcement effect. In contrast, the last lockdown had significantly less impact. The proposed methodology is robust as a real-time procedure for early detection of the structural breaks: the impact of the first two lockdowns could have been correctly identified just the day after they actually occurred.
Identifiants
pubmed: 32868965
doi: 10.1007/s00148-020-00799-x
pii: 799
pmc: PMC7449634
doi:
Types de publication
Journal Article
Langues
eng
Pagination
275-301Informations de copyright
© Springer-Verlag GmbH Germany, part of Springer Nature 2020.
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