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. 2023 Oct 25:11:1222389.
doi: 10.3389/fpubh.2023.1222389. eCollection 2023.

Effects of environmental conditions on COVID-19 morbidity as an example of multicausality: a multi-city case study in Italy

Andrea Murari #  1   2 , Michela Gelfusa #  3 , Teddy Craciunescu  4 , Claudio Gelfusa  3 , Pasquale Gaudio  3 , Gianluigi Bovesecchi  5 , Riccardo Rossi  3
Affiliations

Effects of environmental conditions on COVID-19 morbidity as an example of multicausality: a multi-city case study in Italy

Andrea Murari et al. Front Public Health. .

Abstract

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), broke out in December 2019 in Wuhan city, in the Hubei province of China. Since then, it has spread practically all over the world, disrupting many human activities. In temperate climates overwhelming evidence indicates that its incidence increases significantly during the cold season. Italy was one of the first nations, in which COVID-19 reached epidemic proportions, already at the beginning of 2020. There is therefore enough data to perform a systematic investigation of the correlation between the spread of the virus and the environmental conditions. The objective of this study is the investigation of the relationship between the virus diffusion and the weather, including temperature, wind, humidity and air quality, before the rollout of any vaccine and including rapid variation of the pollutants (not only their long term effects as reported in the literature). Regarding them methodology, given the complexity of the problem and the sparse data, robust statistical tools based on ranking (Spearman and Kendall correlation coefficients) and innovative dynamical system analysis techniques (recurrence plots) have been deployed to disentangle the different influences. In terms of results, the evidence indicates that, even if temperature plays a fundamental role, the morbidity of COVID-19 depends also on other factors. At the aggregate level of major cities, air pollution and the environmental quantities affecting it, particularly the wind intensity, have no negligible effect. This evidence should motivate a rethinking of the public policies related to the containment of this type of airborne infectious diseases, particularly information gathering and traffic management.

Keywords: COVID-19; air quality; particulate; pollutants; public policies; traffic; wind.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Time evolution of temperature and number of COVID-19 cases. Left: city of Bergamo Italy. Right: city of Palermo Italy.
Figure 2
Figure 2
Number of COVID-19 deaths (right) and cases (left) in various European countries vs. the average annual temperature of their capital city. All the details about the values reported in the plots can be found in Appendix A.
Figure 3
Figure 3
Trend of the number of COVID-19 cases, wind intensity, pollutant amounts and humidity with time for three representative Italian cities. When the winds decrease and the particulates (PM2.5 and PM10) increase, the virulence of SARS-CoV-2 becomes clearly worse. Relative humidity shows a completely different type of correlation.
Figure 4
Figure 4
Geographical localization of the cities included in the investigated database.
Figure 5
Figure 5
Time evolution of the number of infected people and the various candidate correlates for all the most representative cities investigated.
Figure 6
Figure 6
Fast Fourier transform of the quantities in the database for three representative cities.
Figure 7
Figure 7
Top row: Spearman’s correlation coefficients mediated over the nine cities (left) and corresponding standard deviations (right) between the quantities in the database. Bottom row: Kendall’s correlation coefficients mediated over the nine cities (left) and corresponding standard deviations (right) between the quantities in the database. W, wind; T, temperature; H, humidity; PM, particulate; dI, number of new infections. In the top row, each cell of the tables reports the Spearman’s correlation coefficient or the standard deviation of the quantities in the corresponding row and column. In the bottom row, each cell of the tables reports the Kendall’s correlation coefficient or the standard deviation of the quantities in the corresponding row and column.
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