Abstract
As of the end of October 2020, the cumulative number of confirmed cases of COVID-19 has exceeded 45 million and the cumulative number of deaths has exceeded 1.1 million all over the world. Faced with the fatal pandemic, countries around the world have taken various prevention and control measures. One of the important issues in epidemic prevention and control is the assessment of the prevention and control effectiveness. Changes in the time series of daily new confirmed cases can reflect the impact of policies in certain regions. In this paper, a smooth transition autoregressive (STAR) model is applied to investigate the intrinsic changes during the epidemic in certain countries and regions. In order to quantitatively evaluate the influence of the epidemic control measures, the sequence is fitted to the STAR model; then, comparisons between the dates of transition points and those of releasing certain policies are applied. Our model well fits the data. Moreover, the nonlinear smooth function within the STAR model reveals that the implementation of prevention and control policies is effective in some regions with different speeds. However, the ineffectiveness is also revealed and the threat of a second wave had already emerged.
Highlights
In 2020, the COVID-19 epidemic is changing the way how people live, work, study, and socialize [1, 2]
China has adopted a series of strict control measures, including the lockdown of Wuhan starting from January 23, 2020, the establishment of Vulcan Mountain Hospital, or Mountain Hospital, and mobile cabin hospital, calling for the public to stay at home, not dining together, not visiting friends and relatives, working from home for adults, studying online at home for kids and college students, and tracing and isolating close contacts. e virus spread alarmingly fast in late January in China
According to the smooth transition autoregressive (STAR) model, the data of daily new cases in each region are fitted to the model
Summary
In 2020, the COVID-19 epidemic is changing the way how people live, work, study, and socialize [1, 2]. In order to quantitatively identify the inflection points hidden in the time series of daily new confirmed cases and further evaluate the effectiveness, a smooth transition autoregressive (STAR) model is utilized to analyze the epidemic data in different countries and regions. The inflection points are related to the quantitative parameters reflecting the switch of expansion and contraction within the sequences It is revealed by the nonlinear function in the STAR model that the effectiveness of certain policies usually showed up within 2 weeks to 2 months. E STAR model gives good fitting of the time series in consideration, and the associated nonlinear function G tells the transition within the sequence. In order to evaluate the effect of the prevention measures, the comparison between the release time of a policy and the time that transition takes place in function G can accomplish our task. Where yt, t 1, . . . , n is the time series of the real data and yt, t 1, . . . , n is the sequence of the fitted values from our model
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