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. 2021 Mar 10;18(6):2803.
doi: 10.3390/ijerph18062803.

The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces

Frederik Seeup Hass  1 , Jamal Jokar Arsanjani  1
Affiliations

The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces

Frederik Seeup Hass et al. Int J Environ Res Public Health. .

Abstract

The Covid-19 pandemic emerged and evolved so quickly that societies were not able to respond quickly enough, mainly due to the nature of the Covid-19 virus' rate of spread and also the largely open societies that we live in. While we have been willingly moving towards open societies and reducing movement barriers, there is a need to be prepared for minimizing the openness of society on occasions such as large pandemics, which are low probability events with massive impacts. Certainly, similar to many phenomena, the Covid-19 pandemic has shown us its own geography presenting its emergence and evolving patterns as well as taking advantage of our geographical settings for escalating its spread. Hence, this study aims at presenting a data-driven approach for exploring the spatio-temporal patterns of the pandemic over a regional scale, i.e., Europe and a country scale, i.e., Denmark, and also what geographical variables potentially contribute to expediting its spread. We used official regional infection rates, points of interest, temperature and air pollution data for monitoring the pandemic's spread across Europe and also applied geospatial methods such as spatial autocorrelation and space-time autocorrelation to extract relevant indicators that could explain the dynamics of the pandemic. Furthermore, we applied statistical methods, e.g., ordinary least squares, geographically weighted regression, as well as machine learning methods, e.g., random forest for exploring the potential correlation between the chosen underlying factors and the pandemic spread. Our findings indicate that population density, amenities such as cafes and bars, and pollution levels are the most influential explanatory variables while pollution levels can be explicitly used to monitor lockdown measures and infection rates at country level. The choice of data and methods used in this study along with the achieved results and presented discussions can empower health authorities and decision makers with an interactive decision support tool, which can be useful for imposing geographically varying lockdowns and protectives measures using historical data.

Keywords: Covid-19 pandemic; machine learning; public health; spatial autocorrelation; spatio-temporal analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Cumulative Covid-19 infection rates per 100,000 inhabitant, from 23 March until 8 November 2020.
Figure 2
Figure 2
Bi-weekly infection rates at week 14, week 26 and week 42.
Figure 3
Figure 3
Hot and cold spots of total accumulated Covid-19 cases in the EU regions (left) and the municipalities of Denmark (right).
Figure 4
Figure 4
Emerging hotspot results for total cumulative infection rates from week 13 to week 45 in Europe at 90% (left) and 75% (right) time interval setting.
Figure 5
Figure 5
Emerging hotspot results for total cumulative infection rates from week 13 to week 45 at 55% time interval setting.
Figure 6
Figure 6
Emerging hotspot results for total cumulative infection rates from week 13 to week 45 in Denmark at 90% (left) and 75% (right) time interval setting.
Figure 7
Figure 7
Emerging hotspot results for total cumulative infection rates in Denmark for April 2020 (left) and September 2020 (right) at 75% time interval setting.
Figure 8
Figure 8
Emerging hotspot results for total cumulative infection rates in Denmark for November 2020 at 75% time interval setting.
Figure 9
Figure 9
The coefficients of the 5 most significant variables in explaining total Covid-19 infections.
Figure 10
Figure 10
The significance of the 5 most significant variables in explaining total Covid-19 infections.
Figure 11
Figure 11
Geographic distribution of Condition Number, Local R2 values and Standardized Residuals for the GWR model of total Covid-19 infections.
Figure 12
Figure 12
Variable Importance for Random Forest Regression of all EU regions.
See this image and copyright information in PMC

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