Modeling of Various Spatial Patterns of SARS-CoV-2: The Case of Germany

Abstract

Among numerous publications about the SARS-CoV-2, many articles present research from the geographic point of view. The cartographic research method used in this area of science can be successfully applied to analyze the spatiotemporal characteristics of the pandemic using limited data and can be useful for a quick and preliminary assessment of the spread of infections. In this paper, research on the spatial differentiation of the structure and homogeneity of the system in which SARS-CoV-2 occurs, as well as spatial concentration of people infected was undertaken. The phenomena were investigated in a period of two infection waves in Germany: in spring and autumn 2020. We applied the potential model, entropy, centrographic method, and Lorenz curve in spatial analysis. The potentials model made it possible to distinguish core regions with a high level of the growth of new infections, along with areas of their impact, and regions with a low level of generation of new infections. The entropy showed the spatial distribution of differentiation of the studied system and the change of these characteristics between spring and autumn. The concentration method allowed for spatial and numerical demonstration of the concentration of infected population in a given area. We wanted to show that it is possible to draw meaningful conclusions about the pandemic characteristics using only basic data about infections, along with proper cartographic methods. The results can be used to designate the zones of the greatest threats, and thus, the areas where the most intense actions should be taken.

The Assessment of COVID-19 Vulnerability Risk for Crisis Management

“…The above leads to the determination of the most significant factors, enabling the prediction and modelling of the spatial patterns of virus spread. The researchers commonly use spatial statistic tools such as linear and non-linear regression [50], Bayesian Belief Networks [68], Adaboost algorithm [69], Potential Model [70], Joinpoint analysis [71], machine learning [50,72] in modelling COVID-19 spatial pattern. As a result, it is possible to forecast the COVID spread and to deliver an effective response in cluster containment for crisis situations with intelligent computing [20,62,70,73,74].…”

Temporal variation of spatial autocorrelation of COVID-19 cases identified in Poland during the year from the beginning of the pandemic

“…The dominance of publications from China, the USA and Brazil is not so strong anymore. Most of the available studies are those that concern the development of the COVID-19 epidemic in individual countries (Danon et al, 2020;Gomes et al, 2020;Hernández-Flores et al, 2020;Hohl et al, 2020;Kim et al, 2020;Liu et al, 2020;Mollalo et al, 2020;Niu et al, 2020;Ramirez-Aldana, 2020;Castro et al, 2021;Gaudart et al, 2021;Gupta et al, 2021;Huang et al, 2021;Lipsitt et al, 2021;Mościcka et al, 2021;Vaz, 2021). Although so far rare, there are also available results of analyzes carried out on a supra-national scale, especially concerning the European Union (Mounir Amdaoud et al, 2020;Hass & Jokar Arsanjani, 2021;Sannigrahi et al, 2020), entire continents (Weiss et al, 2020 -Africa) and even the whole Earth (Shadi Nazari et al, 2020).…”