Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries

•A segmented Poisson model incorporating the power law and the exponential law was proposed to study the COVID-19 outbreaks.•Turning point, final size, duration and the attack rate were estimated.•The data of daily new cases of the six Western countries in the Group of Seven was analyzed. In this pa...

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Published in:	Chaos, solitons and fractals Vol. 135; p. 109829
Main Authors:	Zhang, Xiaolei, Ma, Renjun, Wang, Lin
Format:	Journal Article
Language:	English
Published:	England Elsevier Ltd 01.06.2020
Subjects:	Attack rate COVID-19 pandemic Poisson regression Turning point COVID-19 pandemic Attack rate Poisson regression Turning point
ISSN:	0960-0779, 1873-2887, 0960-0779
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Abstract	•A segmented Poisson model incorporating the power law and the exponential law was proposed to study the COVID-19 outbreaks.•Turning point, final size, duration and the attack rate were estimated.•The data of daily new cases of the six Western countries in the Group of Seven was analyzed. In this paper, we employed a segmented Poisson model to analyze the available daily new cases data of the COVID-19 outbreaks in the six Western countries of the Group of Seven, namely, Canada, France, Germany, Italy, UK and USA. We incorporated the governments’ interventions (stay-at-home advises/orders, lockdowns, quarantines and social distancing) against COVID-19 into consideration. Our analysis allowed us to make a statistical prediction on the turning point (the time that the daily new cases peak), the duration (the period that the outbreak lasts) and the attack rate (the percentage of the total population that will be infected over the course of the outbreak) for these countries.
AbstractList	• A segmented Poisson model incorporating the power law and the exponential law was proposed to study the COVID-19 outbreaks. • Turning point, final size, duration and the attack rate were estimated. • The data of daily new cases of the six Western countries in the Group of Seven was analyzed. In this paper, we employed a segmented Poisson model to analyze the available daily new cases data of the COVID-19 outbreaks in the six Western countries of the Group of Seven, namely, Canada, France, Germany, Italy, UK and USA. We incorporated the governments’ interventions (stay-at-home advises/orders, lockdowns, quarantines and social distancing) against COVID-19 into consideration. Our analysis allowed us to make a statistical prediction on the turning point (the time that the daily new cases peak), the duration (the period that the outbreak lasts) and the attack rate (the percentage of the total population that will be infected over the course of the outbreak) for these countries. In this paper, we employed a segmented Poisson model to analyze the available daily new cases data of the COVID-19 outbreaks in the six Western countries of the Group of Seven, namely, Canada, France, Germany, Italy, UK and USA. We incorporated the governments' interventions (stay-at-home advises/orders, lockdowns, quarantines and social distancing) against COVID-19 into consideration. Our analysis allowed us to make a statistical prediction on the turning point (the time that the daily new cases peak), the duration (the period that the outbreak lasts) and the attack rate (the percentage of the total population that will be infected over the course of the outbreak) for these countries.In this paper, we employed a segmented Poisson model to analyze the available daily new cases data of the COVID-19 outbreaks in the six Western countries of the Group of Seven, namely, Canada, France, Germany, Italy, UK and USA. We incorporated the governments' interventions (stay-at-home advises/orders, lockdowns, quarantines and social distancing) against COVID-19 into consideration. Our analysis allowed us to make a statistical prediction on the turning point (the time that the daily new cases peak), the duration (the period that the outbreak lasts) and the attack rate (the percentage of the total population that will be infected over the course of the outbreak) for these countries. In this paper, we employed a segmented Poisson model to analyze the available daily new cases data of the COVID-19 outbreaks in the six Western countries of the Group of Seven, namely, Canada, France, Germany, Italy, UK and USA. We incorporated the governments' interventions (stay-at-home advises/orders, lockdowns, quarantines and social distancing) against COVID-19 into consideration. Our analysis allowed us to make a statistical prediction on the turning point (the time that the daily new cases peak), the duration (the period that the outbreak lasts) and the attack rate (the percentage of the total population that will be infected over the course of the outbreak) for these countries. •A segmented Poisson model incorporating the power law and the exponential law was proposed to study the COVID-19 outbreaks.•Turning point, final size, duration and the attack rate were estimated.•The data of daily new cases of the six Western countries in the Group of Seven was analyzed. In this paper, we employed a segmented Poisson model to analyze the available daily new cases data of the COVID-19 outbreaks in the six Western countries of the Group of Seven, namely, Canada, France, Germany, Italy, UK and USA. We incorporated the governments’ interventions (stay-at-home advises/orders, lockdowns, quarantines and social distancing) against COVID-19 into consideration. Our analysis allowed us to make a statistical prediction on the turning point (the time that the daily new cases peak), the duration (the period that the outbreak lasts) and the attack rate (the percentage of the total population that will be infected over the course of the outbreak) for these countries.
ArticleNumber	109829
Author	Ma, Renjun Zhang, Xiaolei Wang, Lin
Author_xml	– sequence: 1 givenname: Xiaolei surname: Zhang fullname: Zhang, Xiaolei organization: Pan-Asia Business School, Yunnan Normal University, Kunming, PR China – sequence: 2 givenname: Renjun orcidid: 0000-0001-5243-5426 surname: Ma fullname: Ma, Renjun organization: Department of Mathematics and Statistics, University of New Brunswick, Fredericton, E3B 5A3, Canada – sequence: 3 givenname: Lin orcidid: 0000-0002-7215-8750 surname: Wang fullname: Wang, Lin email: lwang2@unb.ca organization: Department of Mathematics and Statistics, University of New Brunswick, Fredericton, E3B 5A3, Canada
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Cites_doi	10.1016/j.chaos.2020.109761 10.2139/ssrn.3562044 10.1126/science.abb6105 10.1016/S0140-6736(20)30260-9 10.3390/jcm9020523 10.1038/s41421-020-0148-0
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Keywords	COVID-19 pandemic Attack rate Poisson regression Turning point
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References	Tang, Tang, Bragazzi (bib0007) 2020; 50 He D., Gao D., Zhuang Z., Cao P., Lou Y., Yang L. The attack rate of the COVID-19 in a year (march 27). 2020. Available at SSRN Li, Feng (bib0006) 2020 Tian, Liu, Li (bib0008) 2020 Wang, Wang, Dong (bib0009) 2020; 6 Wu, Leung, Leung (bib0010) 2020; 395 Jung, Akhmetzhanov, Hayashi (bib0005) 2020; 9 WHO director-general’s opening remarks. . Fanelli, Piazza (bib0003) 2020; 134 doi Tian (10.1016/j.chaos.2020.109829_bib0008) 2020 Jung (10.1016/j.chaos.2020.109829_bib0005) 2020; 9 Wu (10.1016/j.chaos.2020.109829_bib0010) 2020; 395 10.1016/j.chaos.2020.109829_bib0004 Tang (10.1016/j.chaos.2020.109829_bib0007) 2020; 50 10.1016/j.chaos.2020.109829_bib0011 10.1016/j.chaos.2020.109829_bib0001 10.1016/j.chaos.2020.109829_bib0012 10.1016/j.chaos.2020.109829_bib0002 Fanelli (10.1016/j.chaos.2020.109829_bib0003) 2020; 134 Wang (10.1016/j.chaos.2020.109829_bib0009) 2020; 6 Li (10.1016/j.chaos.2020.109829_sbref0003) 2020
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Snippet	•A segmented Poisson model incorporating the power law and the exponential law was proposed to study the COVID-19 outbreaks.•Turning point, final size,... In this paper, we employed a segmented Poisson model to analyze the available daily new cases data of the COVID-19 outbreaks in the six Western countries of... • A segmented Poisson model incorporating the power law and the exponential law was proposed to study the COVID-19 outbreaks. • Turning point, final size,...
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Title	Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries
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