Insights into the relationship between weather parameters and COVID-19 outbreak in Lombardy, Italy

ABSTRACT

Meteorological conditions are essential factors affecting the spread of infectious diseases caused by viruses. The aim of this study is to investigate the relationship between the weather parameters and the number of confirmed Corona Virus Disease 2019 (COVID-19) cases in Lombardy, Italy, one of the regions where the first cases in Europe are reported. For this purpose, the reported COVID-19 data between February 20, 2020 and April 21, 2020 was collected from the Italian National Health Ministry website. Besides, daily data on weather conditions of Lombardy were obtained from the Lombardy Regional Agency for Environmental Protection (ARPA Lombardia), which provides a large open data portal to users. The average values of temperature, relative humidity, wind speed, and air pressure were selected as weather parameters. Spearman's rank correlation coefficient test was used to analyze the relationship of weather variables with the COVID-19 outbreak. The analysis results showed that the total number of confirmed COVID-19 cases was positively associated with average temperature (r = 0.619). In contrast, a negative correlation was observed with average relative humidity (r = −0.371). The results of this study can help decision-makers to create economic and containment policies related to the COVID-19 pandemic.

KEYWORDS:

• Weather
• COVID-19
• outbreak
• meteorological conditions
• climate
• correlation

Acknowledgements

The author thanks the Lombardy Regional Environmental Protection Agency (ARPA Lombardia) for providing the data that made this study possible.

Disclosure statement

The author(s) declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Notes on contributors

Zeynep Ceylan

Zeynep Ceylan is currently working as an Assistant Professor at the Industrial Engineering Department of Samsun University, Samsun, Turkey. She received her BSc degree from Gaziantep University, Industrial Engineering Department, and her MSc and PhD degrees from Marmara University, Department of Industrial Engineering. Her main research interests are Artificial Intelligence, Scheduling, Data Mining, and Metaheuristic Algorithms.