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?:abstract
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In December 2019, the world was confronted with the outbreak of the respiratory disease COVID-19 (Corona). The first infection (confirmed case) was detected in the City Wuhan, Hubei, China. First, it was an epidemic in China, but in the first quarter of 2020, it evolved into a pandemic, which continues to this day. The COVID-19 pandemic with its incredible speed of spread shows the vulnerability of a globalized and networked world. The first months of the pandemic were characterized by heavy burden on health systems. Worldwide, the population of countries was affected with severe restrictions, like educational system shutdown, public traffic system breakdown or a comprehensive lockdown. The severity of the burden was dependent on many factors, e.g. government, culture or health system. However, the burden happened regarding each country with slight time lags, cf. Bracke et al. (2020). This paper focuses on data analytics regarding infection data of the COVID-19 pandemic. It is a continuation of the research study COVID-19 pandemic data analytics: Data heterogeneity, spreading behavior, and lockdown impact, published by Bracke et al. (2020). The goal of this assessment is the evaluation/analysis of infection data mining considering model uncertainty, pandemic spreading behavior with lockdown impact and early second wave in Germany, Italy, Japan, New Zealand and France. Furthermore, a comparison with other infectious diseases (measles and influenza) is made. The used data base from Johns Hopkins University (JHU) runs from 01/22/2020 until 09/22/2020 with daily data, the dynamic development after 09/22/2020 is not considered. The measles/influenza analytics are based on Robert Koch Institute (RKI) data base 09/22/2020. Statistical models and methods from reliability engineering like Weibull distribution model or trend test are used to analyze the occurrence of infection.
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