Abstract
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. This paper focuses on data analytics regarding COVID-19 infection data. The goal is data mining considering model uncertainty, pandemic spreading behavior with lockdown impact in Germany, Italy, Japan, New Zealand and France in first and second wave. Furthermore, a comparison with other infectious diseases -measles and influenza- is made. 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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