Abstract

BackgroundThere are studies that analyze the role of meteorological variables on the incidence and severity of COVID-19, and others that explore the role played by air pollutants, but currently there are very few studies that analyze the impact of both effects together. This is the aim of the current study. We analyzed data corresponding to the period from February 1 to May 31, 2020 for the City of Madrid. As meteorological variables, maximum daily temperature (Tmax) in ºC and mean daily absolute humidity (AH) in g/m3 were used corresponding to the mean values recorded by all Spanish Meteorological Agency (AEMET) observatories in the Madrid region. Atmospheric pollutant data for PM10 and NO2 in µg/m3 for the Madrid region were provided by the Spanish Environmental Ministry (MITECO). Daily incidence, daily hospital admissions per 100.000 inhabitants, daily ICU admissions and daily death rates per million inhabitants were used as dependent variables. These data were provided by the ISCIII Spanish National Epidemiology Center. Generalized linear models with Poisson link were performed between the dependent and independent variables, controlling for seasonality, trend and the autoregressive nature of the series.ResultsThe results of the single-variable models showed a negative association between Tmax and all of the dependent variables considered, except in the case of deaths, in which lower temperatures were associated with higher rates. AH also showed the same behavior with the COVID-19 variables analyzed and with the lags, similar to those obtained with Tmax. In terms of atmospheric pollutants PM10 and NO2, both showed a positive association with the dependent variables. Only PM10 was associated with the death rate. Associations were established between lags 12 and 21 for PM10 and between 0 and 28 for NO2, indicating a short-term association of NO2 with the disease. In the two-variable models, the role of NO2 was predominant compared to PM10.ConclusionsThe results of this study indicate that the environmental variables analyzed are related to the incidence and severity of COVID-19 in the Community of Madrid. In general, low temperatures and low humidity in the atmosphere affect the spread of the virus. Air pollution, especially NO2, is associated with a higher incidence and severity of the disease. The impact that these environmental factors are small (in terms of relative risk) and by themselves cannot explain the behavior of the incidence and severity of COVID-19.

Highlights

  • There is currently no clear scientific evidence that environmental factors such as temperature and humidity affect the spread of the new SARS-CoV-2 virus or the slowing of transmission

  • It is worth highlighting that, in terms of average values, 47.1% of detected cases were admitted to the hospital, 2.9% were admitted to the intensive care unit (ICU), and 11.9% died

  • The relative risks (RR) of these environmental factors are small and, by themselves, cannot explain the behavior of the incidence and severity of Coronavirus disease (COVID)-19, which is explained by social distancing and public health measures not considered in our analysis, this assumption is similar to the findings founded in the First report of the WMO COVID-19 Task team [58]

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Summary

Introduction

There is currently no clear scientific evidence that environmental factors such as temperature and humidity affect the spread of the new SARS-CoV-2 virus or the slowing of transmission. The data from one study [3] suggest that there is an association between a country’s latitude and mortality rates of COVID-19. This gradient can be observed within a country, such as Italy, in which the North of the country is more affected than the South [4]. Daily hospital admissions per 100.000 inhabitants, daily ICU admissions and daily death rates per million inhabitants were used as dependent variables. These data were provided by the ISCIII Spanish National Epidemiology Center. Generalized linear models with Poisson link were performed between the dependent and independent variables, controlling for seasonality, trend and the autoregressive nature of the series

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