{"title":"流行病和冠状病毒的统计建模","authors":"I. Mandel, B. Zaslavsky, S. Lipovetsky","doi":"10.3233/MAS-210509","DOIUrl":null,"url":null,"abstract":"This special MASA issue is intended for the problems of statistical modeling of pandemics in general, and the Coronavirus COVID-19 one particularly. A recent analysis in Nature1 shows that the number of papers on coronavirus skyrocketed in the first 4 months of 2020 and then stabilized, more or less in accordance with behavior of the pandemic itself. The statistical modeling of the coronavirus pandemic is also flattened – yet the number of monthly publications is huge, exceeding the “normal pre-pandemic” level in 25–30 times. The goal of “modeling” is to create multiple scenarios, including the pessimistic and optimistic, to be immediately available when circumstances require it. Perhaps, the reasons for the pandemic been so devastating are that the science was not ready, the WHO recommendations were not in the place, effective government plans did not exist, and so on. The period of the preliminary preparations was just lost, which is especially sorrowful, because comparatively recent pandemics, like SARS in 2002–4 and others, gave all the reasons to be timely prepared. It seems, just Taiwan2 took all previous cases seriously and made a strategic plan, which was brazenly ignored by other countries and WHO; the difference between Taiwan and other countries outcomes is now startling. In light of that all, what could be the purpose for the special issue of the statistical journal on pandemic problems? It obviously will not help to reach the ear of decision makers in the struggle with the current wave, which seems starts to calm down. However, the different approaches presented in this issue will help in future preparation for the yet unknown pandemics or epidemics. A wide geography of the authors’ countries and variety of the topics cover somewhat different aspects of statistical modeling of pandemics. A reader should also know that all the papers were in preparation for several months earlier to this issue, while the pandemics was evolving very fast. Some of the quantitative results may look obsolete (although, the authors tried to get maximum in their data collection), but the methodological value of the proposed approaches stays to be useful. Fighting the Coronavirus COVID-19 pandemic required quick developing tests and vaccines, continuing trials and research (Mandel & Lipovetsky, 2020). As a reflection of these efforts, multiple journal articles have been published on the related topics. The coronavirus pandemic covers the most populated areas on Earth, and the spread of infection has been going fast with the global transportation and connectivity of travelers and commerce. With COVID-19 highly infectious features, high transmissivity, often asymptomatic appearance, it spreads with huge consequences in areas of dense populations and poor public health systems. In conditions of the lack of a vaccine, only the forced isolation of the infected serves to decreasing the infection rates. However, within months of the virus","PeriodicalId":35000,"journal":{"name":"Model Assisted Statistics and Applications","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/MAS-210509","citationCount":"0","resultStr":"{\"title\":\"Statistical modeling of pandemics and coronavirus\",\"authors\":\"I. Mandel, B. Zaslavsky, S. 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The period of the preliminary preparations was just lost, which is especially sorrowful, because comparatively recent pandemics, like SARS in 2002–4 and others, gave all the reasons to be timely prepared. It seems, just Taiwan2 took all previous cases seriously and made a strategic plan, which was brazenly ignored by other countries and WHO; the difference between Taiwan and other countries outcomes is now startling. In light of that all, what could be the purpose for the special issue of the statistical journal on pandemic problems? It obviously will not help to reach the ear of decision makers in the struggle with the current wave, which seems starts to calm down. However, the different approaches presented in this issue will help in future preparation for the yet unknown pandemics or epidemics. A wide geography of the authors’ countries and variety of the topics cover somewhat different aspects of statistical modeling of pandemics. A reader should also know that all the papers were in preparation for several months earlier to this issue, while the pandemics was evolving very fast. Some of the quantitative results may look obsolete (although, the authors tried to get maximum in their data collection), but the methodological value of the proposed approaches stays to be useful. Fighting the Coronavirus COVID-19 pandemic required quick developing tests and vaccines, continuing trials and research (Mandel & Lipovetsky, 2020). As a reflection of these efforts, multiple journal articles have been published on the related topics. The coronavirus pandemic covers the most populated areas on Earth, and the spread of infection has been going fast with the global transportation and connectivity of travelers and commerce. With COVID-19 highly infectious features, high transmissivity, often asymptomatic appearance, it spreads with huge consequences in areas of dense populations and poor public health systems. 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This special MASA issue is intended for the problems of statistical modeling of pandemics in general, and the Coronavirus COVID-19 one particularly. A recent analysis in Nature1 shows that the number of papers on coronavirus skyrocketed in the first 4 months of 2020 and then stabilized, more or less in accordance with behavior of the pandemic itself. The statistical modeling of the coronavirus pandemic is also flattened – yet the number of monthly publications is huge, exceeding the “normal pre-pandemic” level in 25–30 times. The goal of “modeling” is to create multiple scenarios, including the pessimistic and optimistic, to be immediately available when circumstances require it. Perhaps, the reasons for the pandemic been so devastating are that the science was not ready, the WHO recommendations were not in the place, effective government plans did not exist, and so on. The period of the preliminary preparations was just lost, which is especially sorrowful, because comparatively recent pandemics, like SARS in 2002–4 and others, gave all the reasons to be timely prepared. It seems, just Taiwan2 took all previous cases seriously and made a strategic plan, which was brazenly ignored by other countries and WHO; the difference between Taiwan and other countries outcomes is now startling. In light of that all, what could be the purpose for the special issue of the statistical journal on pandemic problems? It obviously will not help to reach the ear of decision makers in the struggle with the current wave, which seems starts to calm down. However, the different approaches presented in this issue will help in future preparation for the yet unknown pandemics or epidemics. A wide geography of the authors’ countries and variety of the topics cover somewhat different aspects of statistical modeling of pandemics. A reader should also know that all the papers were in preparation for several months earlier to this issue, while the pandemics was evolving very fast. Some of the quantitative results may look obsolete (although, the authors tried to get maximum in their data collection), but the methodological value of the proposed approaches stays to be useful. Fighting the Coronavirus COVID-19 pandemic required quick developing tests and vaccines, continuing trials and research (Mandel & Lipovetsky, 2020). As a reflection of these efforts, multiple journal articles have been published on the related topics. The coronavirus pandemic covers the most populated areas on Earth, and the spread of infection has been going fast with the global transportation and connectivity of travelers and commerce. With COVID-19 highly infectious features, high transmissivity, often asymptomatic appearance, it spreads with huge consequences in areas of dense populations and poor public health systems. In conditions of the lack of a vaccine, only the forced isolation of the infected serves to decreasing the infection rates. However, within months of the virus
期刊介绍:
Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.