A Preliminary Investigation on the Statistical Correlations between SARS-CoV-2 Spread and Local Meteorology

Abstract

The statistical correlation between meteorological parameters and the spread of Coronavirus Disease-2019 (COVID-19) was investigated in five provinces of Italy selected according to the number of infected individuals and the different trends of infection in the early stages of the epidemic: Bergamo and Brescia showed some of the highest trends of infections while nearby Cremona and Mantova, showed lower trends. Pesaro–Urbino province was included for further investigation as it was comparably affected by the epidemic despite being the area far from the Po valley. Moving means of the variables were considered to take into account the variability of incubation periods and uncertainties in the epidemiological data. The same analyzes were performed normalizing the number of new daily cases based on the number of checks performed. For each province, the moving mean of adjusted and unadjusted new daily cases were independently plotted versus each meteorological parameter, and linear regressions were determined in the period from 29th of February 2020 to 29th of March 2020. Strong positive correlations were observed between new cases and temperatures within three provinces representing 86.5% of the contagions. Strong negative correlations were observed between the moving means of new cases and relative humidity values for four provinces and more than 90% of the contagions.

Delayed positive association between air temperature and covid-19 incidence in Italy

“…The positive association between air temperature and COVID-19 incidence reported herein is in agreement with several observational studies (Bashir et al, 2020;Tosepu et al, 2020;Wang et al, 2020b) including two in Italy (Passerini et al, 2020;Zoran et al, 2020), though others have reported negative associations (Islam et al, 2020;Wang et al, 2020a;Liu et al, 2020;Luo et al 2020;Pirouz et al, 2020). Meteorological variables (Grassly and Fraser, 2006), control measures (Prem et al, 2020), and cultural practices (Bruns et al, 2020) are all plausible confounders which might account for these apparent disparities.…”

“…As highlighted by Yuan et al (2020), these studies found contradictory results, even when analysing similar data for similar regions. In the case of Italy, a preliminary analysis using moving averages from five provinces showed a negative association between relative humidity and daily incidence, but a positive association between daily temperature and daily incidence in three of five provinces (Passerini et al, 2020). The 3 latter finding does not align with predictions based on our prior understanding.…”

“…In contrast, one study using multivariate analyses for three regions in Italy that compared climatic factors in relation to daily COVID-19 confirmed cases demonstrated an inverse correlation of temperature with daily incidence (Pirouz et al, 2020). A number of studies have also attempted to model lag effects of climatic predictors, though these have so far been limited to select regions (Runkle et al, 2020), relatively narrow temperature ranges (Passerini et al, 2020;Bashir et al, 2020;Tosepu et al, 2020), relatively short time series (Shi et al, 2020) and lag periods (Tobías and Molina, 2020), all of which may induce significant biases.…”

Predicting the future SARS-COV-2 reproductive rate

“…[16] Humidity may be an important factor, with a negative correlation to Sars-CoV-2 transmission and deaths shown in a number of studies. [17], [18]…”

On the usefulness of the bioclimatic correlative models of SARS-CoV-2

“…Conceptually, it is not realistic to consider only outdoor temperature and relative humidity as the significant driving factors of the COVID-19 incidence ( Passerini et al, 2020 ). Most of the research that related weather variables with the spread of COVID-19 were retrospective observational studies.…”