Entropy considerations on COVID 19 pandemics in Germany, 2020-2021

Abstract

Aims: To describe and analyze Shannon and structural (MST) entropy of COVID-19 pandemics in Germany for 61 months (2020 – 2021). Material and Methods: The officially published number of infected persons, incidence and number of corona – associated deaths in the Federal Republic of Germany during the period February 28, 2020 – March 15, 2021 underwent thorough verification and detailed computation in relation to the involved public health center, date of publication, and consistency of data. Shannon entropy and structural (MST) entropy were derived from these data and associated with the health center’s location, covered population and week of publication. Python programs and self - written programs based upon DIAS (Digital Image Analysis Software (1)) served for information analysis of the COVID pandemics. Results: A total of 2,444,615 infected persons were included in the study, and 70,104 persons died because of or in association with the infection. Distance analysis between the different health centers revealed a time delay of 10 weeks of potential virus jump between the centers. The ‘hot spots’ of the infection were associated with the distance of the health centers from the Republic boundaries to its neighboring countries. No association of spread velocity and death rate was found with the size of the health centers’ community and their environment (big cities, rural towns, etc.). Interpretation: The results indicate that the well-known general features of COVID pandemics, namely distance related risk factors play a major role at the country’s borders. Population density and life conditions are of lower impact. The entropy model is a useful tool to describe and analyze ‘distance – associated alterations of social entities’ such as pandemics and related hazards.