A global analysis on the effect of temperature, socio-economic and environmental factors on the spread and mortality rate of the COVID-19 pandemic

Abstract

We performed a global analysis with data from 149 countries to test whether temperature can explain the spatial variability of the spread rate and mortality of COVID-19 at the global scale. We performed partial correlation analysis and linear mixed effect modelling to evaluate the association of the spread rate and motility of COVID-19 with maximum, minimum, average temperatures and diurnal temperature variation (difference between daytime maximum and night-time minimum temperature) and other environmental and socioeconomic parameters. After controlling the effect of the duration since the first positive case, partial correlation analysis revealed that temperature was not related with the spatial variability of the spread rate of COVID-19 at the global scale. Mortality was negatively related with temperature in the countries with high-income economies. In contrast, diurnal temperature variation was significantly and positively correlated with mortality in the lowand middle-income countries. Taking the country heterogeneity into account, mixed effect modelling revealed that inclusion of temperature as a fixed factor in the model significantly improved model skill predicting mortality in the low-and middle-income countries. Our analysis suggests that warm climate may reduce the mortality rate in high-income economies, but in low-and middle-income countries, high diurnal temperature variation may increase the mortality risk.

Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors

“…Descriptive statistical results showed that COVID-19 outbreaks and related data did not meet the preconditions of Pearson correlation analysis and were mainly manifested as the non-Gaussian normal distribution, spatial autocorrelation, and possible nonlinear relations. In general, Spearman's rank correlation is an appropriate nonparametric estimator for estimating the correlation between two variables with unknown or non-Gaussian statistical distributions, and the relationship between these variables does not need to be linear ( Rahman et al, 2020 ). It is usually measured in terms of Spearman's rank correlation coefficient ρ; the formula is as follows: n is the total number of samples, and y i are the ranks of i and Y i , respectively, ρ represents the Spearman rank correlation coefficient.…”

“…This coefficient varies between -1 and + 1, and the greater the absolute value of ρ, the stronger the relationship between the two variables. Like Pearson's coefficient, Spearman's absolute value of ρ in the range of 0.8-1 indicates a strong correlation, 0.6-0.8 strong correlation, 0.4-0.6 medium correlation, 0.2-0.4 weak correlation, and 0-0.2 uncorrelation ( Rahman et al, 2020 ).…”

Inferring the Association between the Risk of COVID-19 Case Fatality and N501Y Substitution in SARS-CoV-2

“…The overall trends of rCFR gradually increased from 1.0% in September to 2.2% in November 2020, and stabilized at this level thereafter. We suspect that the increase trend of rCFR between September and November might associate with the decreasing air temperature during the same period in the UK, about which the evidence is found in previous studies for COVID-19 [ 50 , 51 , 52 , 53 , 54 ]. The instantaneous CFR estimates may also have the potential to monitor the mortality risk on a real-time basis, and to further examine the associations with its potential determinants, for example, pathogenic evolution, supply of critical care resources [ 55 ] and exposure to environmental factors [ 56 ].…”

Climate change and antibiotic resistance: a deadly combination

“…As to the effects of climate on COVID-19 outcomes, the data are mixed, with some studies showing effects of temperature on mortality and others finding no association. [53][54][55][56] However, climate change, with its concomitant extreme weather events and particulate matter pollution, contributes to cardiopulmonary morbidity, a known risk factor for poor COVID-19 outcomes. Regardless of the nature of the interaction, because of its ubiquity, climate change is likely to affect the COVID-19 pandemic and its victims.…”