Environmental determinants of COVID-19 transmission across a wide climatic gradient in Chile

Abstract

Several studies have examined the transmission dynamics of the novel COVID-19 disease in different parts of the world. Some have reported relationships with several environmental variables, suggesting that spread of the disease is enhanced in colder and drier climates. However, evidence is still scarce and mostly limited to a few countries, particularly from Asia. We examined the potential role of multiple environmental variables in COVID-19 transmission rates and patterns from February 23 to April 16 across 121 cities of Chile; this country covers a large climatic gradient (≈ 40º of latitude, ≈ 4,000 m of altitude and 5 climatic regions, from desert to tundra), but all cities share their social behaviour patterns and regulations. Our results indicated that COVID-19 transmission in Chile was mostly related to 3 main climatic factors (mean temperature, relative humidity and wind speed) and population size. Transmission was greater in colder and drier cities (although wind modulated the effect of temperature) and when wind speed was higher; finally, transmission increased with population size. The results of this study support some previous findings about the main environmental and demographic determinants of COVID-19 transmission, which may be useful for decision-making and management of the disease.

Effects of Demographic and Weather Parameters on COVID-19 Basic Reproduction Number

“…The statistically very highly significant negative correlation of the basic reproductive number of SARS-CoV-2 virus infection with both the mean temperature and humidity obtained in our research (Figure 6D) is consistent with the results of other relevant papers, e.g., (Mecenas et al, 2020). For example, a similar correlation was obtained in a study that analyzed COVID-19 outbreak in the cities of Chile-a country that covers several climate zones, but where it is still safe to assume that social patterns of behavior and introduced epidemic control measures do not drastically differ throughout the country (Correa-Araneda et al, 2020). Effectively the same conclusion-that fewer COVID-19 cases were reported in countries with higher temperatures and humidities-was reached in a study covering over 200 countries in the world (Iqbal et al, 2020).…”

“…The present paper aimed to establish relations between the COVID-19 transmissibility and a large number of demographic and weather parameters. As a measure of COVID-19 transmissibility, we have chosen the basic reproduction number R 0 -a quantity that is essentially independent of the variations in both the testing policies and the introduced social measures (as discussed in the Introduction), in distinction to many studies on transmissibility that relied on the total number of detected case counts [see e.g., (Adhikari and Yin, 2020;Correa-Araneda et al, 2020;Fareed et al, 2020;Gupta et al, 2020;Iqbal et al, 2020;Li et al, 2020;Pourghasemi et al, 2020;Rashed et al, 2020;Singh and Agarwal, 2020)]. We have covered a substantial number of demographic and weather parameters, and included in our analysis all world countries that were significantly affected by the COVID-19 pandemic (and had a reasonable consistency in tracking the early phase of infection progression).…”

“…and by the introduced infection suppression measures (where the latter are not only non-homogeneous but are also erratically observed (Cohen and Kupferschmidt, 2020). Nevertheless, the majority of the research aimed to establish connections of the weather and/or demographic parameters with the spread of COVID-19 seeks correlations exactly with the raw number of detected cases (Adhikari and Yin, 2020;Correa-Araneda et al, 2020;Fareed et al, 2020;Gupta et al, 2020;Iqbal et al, 2020;Li et al, 2020;Pourghasemi et al, 2020;Rashed et al, 2020;Singh and Agarwal, 2020). For the aforementioned reasons, the conclusions reached in this way are questionable.…”

“…In the analysis presented here, we consider 42 different weather, demographic, and health-related population factors, whose analyzed ranges correspond to their variations exhibited in 118 world countries (not all of the parameters were available for all of the countries, as discussed in Section "Demographic and Weather Data Acquisition"). While some authors prefer more coherent data samples to avoid confusing effects of too many different factors (Adhikari and Yin, 2020;Correa-Araneda et al, 2020;Fareed et al, 2020;Rashed et al, 2020;Singh and Agarwal, 2020;Tosepu et al, 2020), this consideration is outweighed by the fact that large ranges of the analyzed parameters serve to amplify the effects we are seeking to recognize and to more reliably determine the underlying correlations. For example, while the value of the Human Development Index (HDI, a composite index of life expectancy, education, and per capita income indicators) varies from 0.36 to 0.96 over the set of analyzed countries, this range would drop by an order of magnitude (Global Data Lab, 2020) if the states of the US were chosen as the scope of the study (other demographic parameters exhibit similar behavior).…”

The Effect of Covid 19 on the Performance of Industrial Wastewater Treatment Plant

“…There are researches also showing that air pollution increases pre-existing health problems such as diabetes, lung diseases, asthma, heart diseases and cancer, which cause COVID-19 to be more serious and fatal (Brazil [42], Chile [43], China [44,45], Germany [46], Iran [47], Iraq [48], Indonesia [49], India [50], Italy [51][52][53], Japan [54], Malaysia [55], Mexico [56], Norway [57], Spain [58], China, Italy and USA [59][60][61], Singapore [62], Turkey [63]).…”