Temperature Extreme May Exaggerate the Mortality Risk of COVID-19 in the Low- and Middle-income Countries: A Global Analysis (original) (raw)
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Environment, Development and Sustainability
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.
Effects of Temperature on Global Trends in Epidemiology of COVID-19
Identifying the effects of temperature on coronavirus disease (COVID-19) outbreak is essential for modelling studies and guiding intervention strategies all over the world. The aim of the study is to understand the effect of temperature on global epidemic trends, geographic distribution, and
2020
Background With the aim of providing a dynamic evaluation of the effects of basic environmental parameters on COVID-19-related death rate, we assessed the correlation between average monthly high temperatures and population density, with death/rate (monthly number of deaths/1M people) for the months of March (start of the analysis and beginning of local epidemic in most of the Western World, except in Italy where it started in February) and April 2020 (continuation of the epidemic). Different geographical areas of the Northern Hemisphere in the United States and in Europe were selected in order to provide a wide range among the different parameters. The death rates were gathered from an available dataset. As a further control, we also included latitude, as a proxy for temperature. Methods Utilizing a publicly available dataset, we retrieved data for the months of March and April 2020 for 25 areas in Europe and in the US. We computed the monthly number of deaths/1M people of confirme...
Scientific Reports
Epidemiological studies have yielded conflicting results regarding climate and incident SARS-CoV-2 infection, and seasonality of infection rates is debated. Moreover, few studies have focused on COVD-19 deaths. We studied the association of average ambient temperature with subsequent COVID-19 mortality in the OECD countries and the individual United States (US), while accounting for other important meteorological and non-meteorological co-variates. The exposure of interest was average temperature and other weather conditions, measured at 25 days prior and 25 days after the first reported COVID-19 death was collected in the OECD countries and US states. The outcome of interest was cumulative COVID-19 mortality, assessed for each region at 25, 30, 35, and 40 days after the first reported death. Analyses were performed with negative binomial regression and adjusted for other weather conditions, particulate matter, sociodemographic factors, smoking, obesity, ICU beds, and social distanc...
Investigation of the Importance of Climatic Factors in COVID-19 Worldwide Intensity
International Journal of Environmental Research and Public Health, 2020
The transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the severity of the related disease (COVID-19) are influenced by a large number of factors. This study aimed to investigate the correlation of COVID-19 case and death rates with possible causal climatological and sociodemographic factors for the March to May 2020 (first wave) period in a worldwide scale by statistically processing data for over one hundred countries. The weather parameters considered herein were air temperature, relative humidity, cumulative precipitation, and cloud cover, while sociodemographic factors included population density, median age, and government measures in response to the pandemic. The results of this study indicate that there is a statistically significant correlation between average atmospheric temperature and the COVID-19 case and death rates, with chi-square test p-values in the 0.001-0.02 range. Regarding sociodemographic factors, there is an even stronger dependence of the case and death rates on the population median age (p = 0.0006-0.0012). Multivariate linear regression analysis using Lasso and the forward stepwise approach revealed that the median age ranks first in importance among the examined variables, followed by the temperature and the delays in taking first governmental measures or issuing stay-at-home orders.
COVID-19 and climatic factors: A global analysis
Environmental Research, 2020
Background:It is unknown if COVID-19 will exhibit seasonal pattern as other diseases e.g., seasonal influenza. Similarly, some environmental factors (e.g., temperature, humidity) have been shown to be associated with transmission of SARS-CoV and MERS-CoV, but global data on their association with COVID-19 are scarce.Objective: To examine the association between climatic factors and COVID-19.Methods: We used multilevel mixed-effects (two-level random-intercepts) negative binomial regression models to examine the association between 7- and 14-day-lagged temperature, humidity (relative and absolute), wind speed and UV index and COVID-19 cases, adjusting for Gross Domestic Products, Global Health Security Index, cloud cover (%), precipitation (mm), sea-level air-pressure (mb), and daytime length. The effects estimates are reported as adjusted rate ratio (aRR) and their corresponding 95% confidence interval (CI).Results: Data from 206 countries (until April 20, 2020) with ≥100 reported cases each showed no association between COVID-19 cases and 7-day-lagged temperature, relative humidity, UV index, and wind speed, after adjusting for potential confounders, but a positive association with 14-day-lagged temperature and a negative association with 14-day-lagged wind speed. Compared to an absolute humidity <5g/m3, an absolute humidity of 5-10g/m3 was associated with a 23% (95% CI:6-42%) higher rate of COVID-19 cases, while absolute humidity >10g/m3 did not have a significant effect. These findings were robust in the 14-day-lagged analysis.Conclusion: Our results of higher COVID-19 cases (through April 20) at absolute humidity of 5-10g/m3 may be suggestive of a ‘sweet point’ for viral transmission, however only controlled laboratory experiments can decisively prove it.
2024
Background: The study sought to establish environmental and social factors that influenced the transmission and mortalities of COVID-19 in developing and developed nations. The factors that were assessed included temperature, average age of the population, urbanization, population density, and percentage of old-aged people in the population. The dependent variables were COVID-19 transmission and COV-ID-19-related deaths. Methods: The study employed a pragmatism research philosophy. It also relied on a deductive research approach and a descriptive research design. It adopted a mixed-method approach as it used both qualitative and quantitative data. It was a cross-sectional study, given its data measurement at a particular point in time. Data was analyzed and presented using descriptive techniques. Results: Statistical analyses were conducted to quantify the relationships between various factors and COVID-19 outcomes. A Kendall's Tau test revealed a robust negative correlation between COVID-19 cases and temperature (Tb =-0.560, p<0.005). This result was further confirmed by Spearman's rank correlation, showing a strong negative correlation with r(13) =-0.684, p<0.007. Similarly, a strong negative correlation was observed between COVID-19 deaths and average annual temperature using both Kendall's Tau (Tb =-0.495, p<0.014) and Spearman's rank correlation (r(13) =-0.648, p<0.012). Age emerged as a significant factor, with a strong positive correlation found between age and both COVID-19 infections (Tb = 0.516, p<0.010; r(13) = 0.670, p<0.009) and COVID-19-related mortalities (r(13) = 0.516, p<0.029). Urbanization was also positively correlated with COVID-19 infections (Tb = 0.530, p<0.008; r(13) = 0.640, p<0.014) and COVID-19 deaths (Tb = 0.398, p<0.048; r(13) = 0.561, p<0.037). Interestingly, no significant correlation was found between population density and COVID-19 infections or deaths in both developed and developing countries, as evidenced by Kendall's Tau (TB = 0.331, p<0.1; Tb = 0.133, p<0.511) and Spearman's rank correlation (r(13) = 0.425, p<0.130; r(13) = 0.161, p<0.583), respectively. Moreover, the percentage of elderly individuals in a country exhibited a strong positive correlation with both COVID-19 infections (Tb = 0.464, p<0.021; r(13) = 0.642, p<0.013) and COVID-19-related deaths (r(13) = 0.541, p<0.046). Conclusion: The study focused on social, demographic, and environmental factors influencing COVID-19 incidence and mortality in developing and developed nations. The study highlights significant COVID-19 transmission and mortality disparities between developed and developing countries. Developed countries exhibited higher infection and mortality rates, coupled with elevated death rates per million and infection rates per million, as compared to their developing counterparts. The research identified a correlation between lower average annual temperatures and increased mortality in developed countries. Contrary to this, high average annual temperatures were associated with a decline in COVID-19 infections. Moreover, developed countries, characterized by higher urbanization levels, population densities, and percentages of aged individuals, experienced elevated COVID-19 infection rates. The study also unveiled a positive correlation between age and COVID-19 infections, with developed countries hosting signifi
Factors Affecting COVID-19 Outbreaks across the Globe: Role of Extreme Climate Change
Sustainability, 2021
The Coronavirus Disease 2019 (COVID-19) pandemic poses a serious threat to global health system and economy. It was first reported in Wuhan, China, and later appeared in Central Asia, Europe, North America, and South America. The spatial COVID-19 distribution pattern highly resembles the global population distribution and international travel routes. We select 48 cities in 16 countries across 4 continents having infection counts higher than 10,000 (by 25 April 2020) as the COVID-19 epicenters. At the initial stage, daily COVID-19 counts co-varies strongly with local temperature and humidity, which are clustered within 0–10 °C and 70–95%, respectively. Later, it spreads in colder (−15 °C) and warmer (25 °C) countries, due to faster adaptability in diverse environmental conditions. We introduce a combined temperature-humidity profile, which is essential for prediction of COVID-19 cases based on environmental conditions. The COVID-19 epicenters are collocated on global CO2 emission hot...
Relationship between monthly climatic variables and worldwide confirmed COVID-19 cases
SSRN, 2020
Most transmittable diseases appear in a specific season and the effect of climate on COVID-19 is of special interest. This study aimed to investigate the relationship between climatic variables and detected COVID-19 cases at the global scale. The total of monthly confirmed cases COVID-19 and climatic data of each country per month from January to April 2020 used in the study. The Pearson correlation coefficient was used to magnitude the relationship between logarithm of COVID-19 cases and climatic variables. While the order of examined climatic variables was estimated by Sobol B-spline smoothing method. The results displayed a significant (p-value < 0.001) weak inverse relationship between logarithm of COVID-19 cases and wind speed (R =-0.33), specific humidity (R =-0.28), dew point temperature (R =-0.23) and air temperature (R =-0.22). It means lower COVID-19 cases were recorded with high wind speed, specific humidity, dew point and air temperature. Moreover, the average confirmed cases in low temperatures are five times more than in high temperatures. In order, climatic factors that affect number of cases COVID-19 are wind speed (Si= 0.123) and air temperature (Si= 0.116). Inverse relationship of COVID-19 cases and air temperature perhaps a positive sign of the possibility of decreasing numbers in the summer months. However, it does not allow one to conclude that governments should weaken their measures to limit the rate of infection of COVID-19 because many socioeconomic factors could influence the virus transmission and should be taken into consideration.