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Modeling the Covid‐19 epidemic using time series econometrics
Author(s) -
Goliński Adam,
Spencer Peter
Publication year - 2021
Publication title -
health economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.55
H-Index - 109
eISSN - 1099-1050
pISSN - 1057-9230
DOI - 10.1002/hec.4413
Subject(s) - fell , mirroring , covid-19 , econometrics , series (stratigraphy) , china , logistic regression , divergence (linguistics) , time series , statistics , climb , economics , demography , geography , mathematics , cartography , engineering , psychology , sociology , medicine , philosophy , aerospace engineering , linguistics , archaeology , pathology , biology , paleontology , communication , disease , infectious disease (medical specialty)
The classic “logistic” model has provided a realistic model of the behaviour of Covid‐19 in China and many East Asian countries. Once these countries passed the peak, the daily case count fell back, mirroring its initial climb in a symmetric way, just as the classic model predicts. However, in Italy and Spain and most other Western countries, the first wave of the epidemic was very different. The daily count fell back gradually from the peak but remained stubbornly high. The reason for the divergence from the classical model remain unclear. We take an empirical stance on this issue and develop a model framework based upon the statistical characteristics of the time series. With the possible exception of China, the workhorse logistic model is decisively rejected against more flexible alternatives.