Correlation analysis between Meteorological parameters and COVID-19 infection rates in six Peruvian cities

Abstract

El presente trabajo analiza la correlación estadística a través del método Spearman y desarrollo de un Modelo de Aditivos Generales (GAM) entre los parámetros ambientales como la temperatura, humedad relativa y precipitación con las tasas de infección por COVID-19 en el periodo del 03/06/2020 hasta 31/01/2021. Entre los principales resultados se resalta que la relación entre la humedad relativa y los casos por COVID-19 en la ciudad de Arequipa tienen un coeficiente alto de correlación negativa. Mientras la humedad decrece durante el periodo de mayo a julio, los casos se incrementaron a su máximo registro, y cuando la humedad aumentó, los casos se disminuyeron a valores cercanos a cero. Además, la variable precipitación no presenta correlación significativa con los casos COVID-19 registrados en todas las ciudades evaluadas. Finalmente, la variable Temperatura muestra una alta correlación con los casos COVID-19 registrados, pero varía de acuerdo a la región geográfica de la ciudad evaluada: para las ciudades de la costa y selva, relación positiva; para las ciudades de la sierra, baja correlación negativa.

Since the early ages, humankind has been affected by several hazards throughout history, being the biological hazard the most destructive in human loss terms. Currently, the COVID-19 Pandemic affects the worldwide since January 2020, where almost all the countries have reported confirmed cases and deaths, being more than 154 million cases confirmed and 3 million deaths. This study focused on statistical correlation analysis between infection rates and environmental parameters such as temperature, humidity, and precipitation in the Peruvian territory in the American continent. Based on the Spearman correlation for non-linear variables, this study demonstrates the correlation values for each meteorological parameter evaluated in the six Peruvian cities with the highest infection rates and deaths. In addition, applying the Generalised Additive Model (GAM) function, this research describes the relationship between the predictor's variables (meteorological parameters) and the response variables (infection rates) evaluated. The results show that the correlation between infection rates and local temperature (Max, Mean, and Min) has a strong negative correlation (-0.65 on average) for all the cities evaluated. Relative humidity has a moderate positive correlation coefficient ( 0.4 on average) for all towns assessed except for Arequipa and Huancayo (cities located in the Andean mountain range), where Arequipa has a strong negative correlation (-0.67) and Huancayo a low correlation coefficient (-0.15). However, precipitation values show insignificant correlation coefficients in all the cities evaluated due to their low or no record values in this period. The Generalised additive model shows a negative relationship between temperature and infection rates in all the cities evaluated. On the other hand, the variable humidity shows a positive relationship for all the cities except in the mountain regions (Arequipa, Huancayo). The variable precipitation did not show any association for all the cities evaluated. This research contributes to knowledge about the relationship between meteorological parameters and COVID-19 infection rates in tropical and developing countries, where environmental factors significantly impact daily activities and population health.