COVID-19: relationship between atmospheric temperature and daily new cases growth rate

Seasonality of endemic COVID-19

IMPORTANCE

The seasonality of COVID-19 is important for effective healthcare and public health decision-making. Previous waves of SARS-CoV-2 infections have indicated that the virus will likely persist as an endemic pathogen with distinct surges. However, the timing and patterns of potentially seasonal surges remain uncertain, rendering effective public health policies uninformed and in danger of poorly anticipating opportunities for intervention, such as well-timed booster vaccination drives. Applying an evolutionary approach to long-term data on closely related circulating coronaviruses, our research provides projections of seasonal surges that should be expected at major temperate population centers. These projections enable local public health efforts that are tailored to expected surges at specific locales or regions. This knowledge is crucial for enhancing medical preparedness and facilitating the implementation of targeted public health interventions.

Short-term effect of meteorological factors on COVID-19 mortality in Qom, Iran

The present study was conducted to assess the short-term effects of the meteorological factors on the COVID-19 mortality in Qom, Iran. The GAM with a quasi-Poisson link function was used to evaluate the impact of temperature, DTR, relative humidity, and absolute humidity on the COVID-19 mortality, controlling potential confounders such as time trend, air pollutants, and day of the week. The results showed that the risk of COVID-19 mortality was reduced, in single-day lag/multiple-day average lag, per one-unit increase in absolute humidity (percentage change in lag 0=-33.64% (95% CI (-42.44, -23.49)), and relative humidity (percentage change in lag 0=-1.87% (95% CI (-2.52, -1.22)). Also, per one-unit increase in DTR value, COVID death risk increased in single-day and multiple-day average lag. This study demonstrated a significant relationship between the four meteorological variables and the COVID-19 mortality.

What are the Effects of Climate Variables on COVID-19 Pandemic? A Systematic Review and Current Update

The climatological parameters can be different in various geographical locations. Moreover, they have possible impacts on COVID-19 incidence. Therefore, the purpose of this systematic review article was to describe the effects of climatic variables on COVID-19 pandemic in different countries. Systematic literature search was performed in Scopus, ISI Web of Science, and PubMed databases using ("Climate" OR "Climate Change" OR "Global Warming" OR "Global Climate Change" OR "Meteorological Parameters" OR "Temperature" OR "Precipitation" OR "Relative Humidity" OR "Wind Speed" OR "Sunshine" OR "Climate Extremes" OR "Weather Extremes") AND ("COVID" OR "Coronavirus disease 2019" OR "COVID-19" OR "SARS-CoV-2" OR "Novel Coronavirus") keywords. From 5229 articles, 424 were screened and 149 were selected for further analysis. The relationship between meteorological parameters is variable in different geographical locations. The results indicate that among the climatic indicators, the temperature is the most significant factor that influences on COVID-19 pandemic in most countries. Some studies were proved that warm and wet climates can decrease COVID-19 incidence; however, the other studies represented that warm location can be a high risk of COVID-19 incidence. It could be suggested that all climate variables such as temperature, humidity, rainfall, precipitation, solar radiation, ultraviolet index, and wind speed could cause spread of COVID-19. Thus, it is recommended that future studies will survey the role of all meteorological variables and interaction between them on COVID-19 spread in specific small areas such as cities of each country and comparison between them.

A dynamic causal modeling of the second outbreak of COVID-19 in Italy

While the vaccination campaign against COVID-19 is having its positive impact, we retrospectively analyze the causal impact of some decisions made by the Italian government on the second outbreak of the SARS-CoV-2 pandemic in Italy, when no vaccine was available. First, we analyze the causal impact of reopenings after the first lockdown in 2020. In addition, we also analyze the impact of reopening schools in September 2020. Our results provide an unprecedented opportunity to evaluate the causal relationship between the relaxation of restrictions and the transmission in the community of a highly contagious respiratory virus that causes severe illness in the absence of prophylactic vaccination programs. We present a purely data-analytic approach based on a Bayesian methodology and discuss possible interpretations of the results obtained and implications for policy makers.

Association Between Weather Parameters and SARS-CoV-2 Confirmed Cases in Two South African Cities

Several approaches have been used in the race against time to mitigate the spread and impact of COVID-19. In this study, we investigated the role of temperature, relative humidity, and particulate matter in the spread of COVID-19 cases within two densely populated cities of South Africa-Pretoria and Cape Town. The role of different levels of COVID-19 restrictions in the air pollution levels, obtained from the Purple Air Network, of the two cities were also considered. Our results suggest that 26.73% and 43.66% reduction in PM2.5 levels were observed in Cape Town and Pretoria respectively for no lockdown (Level 0) to the strictest lockdown level (Level 5). Furthermore, our results showed a significant relationship between particulate matter and COVID-19 in the two cities. Particulate matter was found to be a good predictor, based on the significance of causality test, of COVID-19 cases in Pretoria with a lag of 7 days and more. This suggests that the effect of particulate matter on the number of cases can be felt after 7 days and beyond in Pretoria.

Effect of environmental factors on SARS-CoV-2 infectivity in northern hemisphere countries: a 2-year data analysis

OBJECTIVE

The COVID-19 pandemic that emerged in December 2019 brought human life to a standstill. With over 2-year since the pandemic originated from Wuhan, SARS-CoV-2 has caused more than 6 million deaths worldwide. With the emergence of mutant strains and COVID-19 surge waves, it becomes critically important to conduct epidemiological studies that allow us to understand the role of various environmental factors on SARS-CoV-2 infectivity. Our earlier study reported a strong negative correlation between temperature and COVID-19 incidence. This research is an extension of our previous study with an attempt to understand the global analysis of COVID-19 in northern hemisphere countries.

STUDY DESIGN

This research aims at achieving a better understanding of the correlation of environmental factors such as temperature, sunlight, and humidity with new cases of COVID-19 in northern hemisphere from March 2020 to February 2022.

METHODS

To understand the relationship between the different environmental variants and COVID-19, a statistical approach was employed using Pearson, Spearman and Kendall analysis.

RESULTS

Month-wise univariate analysis indicated a strong negative correlation of temperature and sunlight with SARS-CoV-2 infectivity, whereas inconsistencies were observed in correlation analysis in the case of humidity in winter months. Moreover, a strong negative correlation between average temperature of winter months and COVID-19 cases exists as evidenced by Pearson, Spearman, and Kendall analyses. In addition, correlation pattern between monthly temperature and COVID-19 cases of a country mimics to that of sunlight of a country.

CONCLUSION

This pilot study proposes that low temperatures and low sunlight might be additional risk factors for SARS-CoV-2 infectivity, mostly in northern hemisphere countries.

COVID-19 in Africa: An Explorative Cross-Sectional Analysis of Twenty-One African Countries From January to June 2020

INTRODUCTION

 Africa has surprisingly recorded better gains in containing the coronavirus spread than countries with the better health indices, such as the USA and UK. The low rate of coronavirus disease 2019 (COVID-19) cases and death in Africa represents a puzzle with different biological and social theories such as low COVID-19 testing capacity, substantial young population, few old people, favourable climate, genetic admixture, infectious disease antibodies, and sound community health care systems proposed. We aimed to understand the COVID-19 preventive measures in a group of twenty-one systematically selected African countries that may explain the low burden of COVID-19 in Africa.

METHODS

Data (COVID-19, health, socioeconomic, and demographics indices) of twenty-one systemically selected African countries were retrieved from the various official country and multilateral organization sources such as Worldbank, and the United nations development Programme (UNDP). The extracted data were analyzed in three large groups: international travel restrictions, physical and social distancing, and movement restrictions (lockdown measures; curfews, partial or/and national lockdowns). Data cleaning, analysis (including Pearson correlation), and visualization were done with Microsoft Excel and Graph Pad Prism version 9 (https://www.graphpad.com/).

RESULT

Southern Africa had the greatest number of cases and deaths within the period studied compared to East Africa, which was the least COVID-19 affected sub-region (in terms of COVID-19 cases and deaths). We observed that coronary artery disease death rate was highly correlated with COVID-19 death density (number of COVID-19 deaths/total population) and similarly observed a correlation between the number of cases and deaths and number of in-country arrivals, pandemic preparedness (health security index), COVID-19 containment, and health index (not correlated with deaths). Finally, we noted that the most effective preventive strategy was the 'use of a face mask'.

CONCLUSION

Africa had fewer COVID-19 cases and COVID-19 related deaths. Our data shows that the rapidity and stringency of COVID-19 preventive measures and government policies, and the low level of tourism in Africa compared to other countries (i.e., low COVID-19 seeding rate) may have been contributory to these favorable statistics. We hope these findings impact how the preparedness for pandemics can be enhanced to decrease the burden of preventable deaths and morbidity.

COVID-19 seasonality in temperate countries

INTRODUCTION

While the beneficial effect of vaccination, restrictive measures, and social distancing in reducing mortality due to SARS-CoV-2 is intuitive and taken for granted, seasonality (predictable fluctuation or pattern that recurs or repeats over a one-year period) is still poorly understood and insufficiently taken into consideration. We aimed to examine SARS-CoV-2 seasonality in countries with temperate climate.

METHODS

We identified countries with temperate climate and extracted average country temperature data from the National Center for Environmental information and from the Climate Change Knowledge Portal. We obtained mortality and vaccination rates from an open access database. We used the stringency index derived from the Oxford COVID-19 Government Response Tracker to quantify restriction policies. We used Spearman's and rank-correlation non-parametric test coefficients to investigate the association between COVID-19 mortality and temperature values. We employed multivariate regression models to analyze how containment measures, vaccinations, and monthly temperatures affected COVID-19 mortality rates.

RESULTS

The time series for daily deaths per million inhabitants and average monthly temperatures of European countries and US states with a temperate climate had a negative correlation (p < 0.0001 for all countries, 0.40 < R < 0.86). When running multivariate regression models with country fixed effects, we noted that mortality rates were significantly lower when temperature were higher. Interestingly, when adding an interaction term between monthly temperatures and vaccination rates, we found that as monthly temperatures dropped, the effect of the vaccination campaign on mortality was larger than at higher temperatures.

DISCUSSION

Deaths attributed to SARS-CoV-2 decreased during the summer period in temperate countries. We found that the effect of vaccination rates on mortality was stronger when temperatures were lower. Stakeholders should consider seasonality in managing SARS-CoV-2 and future pandemics to minimize mortality, limit the pressure on hospitals and intensive care units while maintaining economic and social activities.

Stochastic analysis of the relationship between atmospheric variables and coronavirus disease (COVID-19) in a hot, arid climate

The rapid outbreak of the coronavirus disease (COVID-19) has affected millions of people all over the world and killed hundreds of thousands. Atmospheric conditions can play a fundamental role in the transmission of a virus. The relationship between several atmospheric variables and the transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are therefore investigated in this study, in which the State of Kuwait, which has a hot, arid climate, is considered during free movement (without restriction), partial lockdown (partial restrictions), and full lockdown (full restriction). The relationship between the infection rate, growth rate, and doubling time for SARS-CoV-2 and atmospheric variables are also investigated in this study. Daily data describing the number of COVID-19 cases and atmospheric variables, such as temperature, relative humidity, wind speed, visibility, and solar radiation, were collected for the period February 24 to May 30, 2020. Stochastic models were employed to analyze how atmospheric variables can affect the transmission of SARS-CoV-2. The normal and lognormal probability and cumulative density functions (PDF and CDF) were applied to analyze the relationship between atmospheric variables and COVID-19 cases. The Spearman's rank correlation test and multiple regression model were used to investigate the correlation of the studied variables with the transmission of SARS-CoV-2 and to confirm the findings obtained from the stochastic models. The results indicate that relative humidity had a significant negative correlation with the number of COVID-19 cases, whereas positive correlations were observed for cases of infection and temperature, wind speed, and visibility. The infection rate for SARS-CoV-2 is directly proportional to the air temperature, wind speed, and visibility, whereas inversely related to the humidity. The lowest growth rate and longest doubling time of the COVID-19 infection occurred during the full lockdown period. The results in this study may help the World Health Organization (WHO) make specific recommendations about the outbreak of COVID-19 for decision-makers around the world. Integr Environ Assess Manag 2022;18:500-516. © 2021 SETAC.

Latent Growth Curve Modeling for COVID-19 Cases in Presence of Time-Variant Covariate

For the past two years, the entire world has been fighting against the COVID-19 pandemic. The rapid increase in COVID-19 cases can be attributed to several factors. Recent studies have revealed that changes in environmental temperature are associated with the growth of cases. In this study, we modeled the monthly growth rate of COVID-19 cases per million infected in 126 countries using various growth curves under structural equation modeling. Moreover, the environmental temperature has been introduced as a time-varying covariate to enhance the performance of the models. The parameters of growth curve models have been estimated, and accordingly, the results are discussed for the affected countries from August 2020 to July 2021.

Effects of climate variables on the transmission of COVID-19: a systematic review of 62 ecological studies

The new severe acute respiratory syndrome coronavirus 2 was initially discovered at the end of 2019 in Wuhan City in China and has caused one of the most serious global public health crises. A collection and analysis of studies related to the association between COVID-19 (coronavirus disease 2019) transmission and meteorological factors, such as humidity, is vital and indispensable for disease prevention and control. A comprehensive literature search using various databases, including Web of Science, PubMed, and Chinese National Knowledge Infrastructure, was systematically performed to identify eligible studies from Dec 2019 to Feb 1, 2021. We also established six criteria to screen the literature to obtain high-quality literature with consistent research purposes. This systematic review included a total of 62 publications. The study period ranged from 1 to 8 months, with 6 papers considering incubation, and the lag effect of climate factors on COVID-19 activity being taken into account in 22 studies. After quality assessment, no study was found to have a high risk of bias, 30 studies were scored as having moderate risks of bias, and 32 studies were classified as having low risks of bias. The certainty of evidence was also graded as being low. When considering the existing scientific evidence, higher temperatures may slow the progression of the COVID-19 epidemic. However, during the course of the epidemic, these climate variables alone could not account for most of the variability. Therefore, countries should focus more on health policies while also taking into account the influence of weather.

Asymmetric impact of temperature on COVID-19 spread in India: Evidence from quantile-on-quantile regression approach

The emergence of new coronavirus (SARS-CoV-2) has become a significant public health issue worldwide. Some researchers have identified a positive link between temperature and COVID-19 cases. However, no detailed research has highlighted the impact of temperature on COVID-19 spread in India. This study aims to fill this research gap by investigating the impact of temperature on COVID-19 spread in the five most affected Indian states. Quantile-on-Quantile regression (QQR) approach is employed to examine in what manner the quantiles of temperature influence the quantiles of COVID-19 cases. Empirical results confirm an asymmetric and heterogenous impact of temperature on COVID-19 spread across lower and higher quantiles of both variables. The results indicate a significant positive impact of temperature on COVID-19 spread in the three Indian states (Maharashtra, Andhra Pradesh, and Karnataka), predominantly in both low and high quantiles. Whereas, the other two states (Tamil Nadu and Uttar Pradesh) exhibit a mixed trend, as the lower quantiles in both states have a negative effect. However, this negative effect becomes weak at middle and higher quantiles. These research findings offer valuable policy recommendations.

Spatiotemporal Associations Between Social Vulnerability, Environmental Measurements, and COVID-19 in the Conterminous United States

This study summarizes the results from fitting a Bayesian hierarchical spatiotemporal model to coronavirus disease 2019 (COVID-19) cases and deaths at the county level in the United States for the year 2020. Two models were created, one for cases and one for deaths, utilizing a scaled Besag, York, Mollié model with Type I spatial-temporal interaction. Each model accounts for 16 social vulnerability and 7 environmental variables as fixed effects. The spatial pattern between COVID-19 cases and deaths is significantly different in many ways. The spatiotemporal trend of the pandemic in the United States illustrates a shift out of many of the major metropolitan areas into the United States Southeast and Southwest during the summer months and into the upper Midwest beginning in autumn. Analysis of the major social vulnerability predictors of COVID-19 infection and death found that counties with higher percentages of those not having a high school diploma, having non-White status and being Age 65 and over to be significant. Among the environmental variables, above ground level temperature had the strongest effect on relative risk to both cases and deaths. Hot and cold spots, areas of statistically significant high and low COVID-19 cases and deaths respectively, derived from the convolutional spatial effect show that areas with a high probability of above average relative risk have significantly higher Social Vulnerability Index composite scores. The same analysis utilizing the spatiotemporal interaction term exemplifies a more complex relationship between social vulnerability, environmental measurements, COVID-19 cases, and COVID-19 deaths.

Association between meteorological factors and daily new cases of COVID-19 in 188 countries: A time series analysis

By 31 December 2020, Coronavirus disease 2019 (COVID-19) had been prevalent worldwide for one year, and most countries had experienced a complete seasonal cycle. The role of the climate and environment are essential factors to consider in transmission. We explored the association between global meteorological conditions (including mean temperature, wind speed, relative humidity and diurnal temperature range) and new cases of COVID-19 in the whole past year. We assessed the relative risk of meteorological factors to the onset of COVID-19 by using generalized additive models (GAM) and further analyzed the hysteresis effects of meteorological factors using the Distributed Lag Nonlinear Model (DLNM). Our findings revealed that the mean temperature, wind speed and relative humidity were negatively correlated with daily new cases of COVID-19, and the diurnal temperature range was positively correlated with daily new cases of COVID-19. These relationships were more apparent when the temperature and relative humidity were lower than their average value (21.07°Cand 66.83%). The wind speed and diurnal temperature range were higher than the average value(3.07 m/s and 9.53 °C). The maximum RR of mean temperature was 1.30 under -23°C at lag ten days, the minimum RR of wind speed was 0.29 under 12m/s at lag 24 days, the maximum RR of range of temperature was 2.21 under 28 °C at lag 24 days, the maximum RR of relative humidity was 1.35 under 4% at lag 0 days. After a subgroup analysis of the countries included in the study, the results were still robust. As the Northern Hemisphere enters winter, the risk of global covid-19 remains high. Some countries have ushered in a new round of COVID-19 epidemic. Thus, active measures must be taken to control the source of infection, block transmission and prevent further spread of COVID-19 in winter.

Is coronavirus disease (COVID-19) seasonal? A critical analysis of empirical and epidemiological studies at global and local scales

Coronavirus disease (COVID-19) has infected more than 50 million people and killed more than one million, worldwide, during less than a year course. COVID-19, which has already become the worst pandemic in the last 100 years, is still spreading worldwide. Since the beginning of the outbreak, it has been of particular interest to understand whether COVID-19 is seasonal; the finding might help for better planning and preparation for the fight against the disease. Over the past 12 months, numerous empirical and epidemiological studies have been performed to define the distinct diffusion patterns of COVID-19. Thereby, a wealth of data has accumulated on the relationship between various seasonal meteorological factors and COVID-19 transmissibility at global and local scales. In this review, we aimed to discuss whether COVID-19 exhibits any seasonal features in a global and local perspective by collecting and providing summaries of the findings from empirical and epidemiological studies on the COVID-19 pandemic during its first seasonal cycle.

Understanding COVID-19 in Wuhan From the Perspective of Cold-Dampness: Clinical Evidences and Mechanisms

Traditional Chinese medicine (TCM) has played a significant role in the treatment of coronavirus disease 2019 (COVID-19) in Wuhan City. During the epidemic, Academician Tong Xiaolin suggested a close association of COVID-19 with cold-dampness, an etiological factor in TCM, by summarizing the characteristics of the COVID-19 patients in Wuhan. and the theory of Cold-dampness Plague was proposed. Based on the Cold-dampness Plague theory, a series of TCM drugs, such as Huoxiang Zhengqi Dropping Pills, Lianhua Qingwen Granules Hanshiyi Formula, and Tongzhi Granule were developed for the different stages, namely mild, moderate, severe, recovery, of the COVID-19. In addition, clinical evidences were obtained through randomized clinical trials or retrospective cohort studies. The Anti-SARS-CoV-2 mechanism of the TCM prescriptions were then summarized from the four aspects: targeting the ACE2 and 3CLPro, targeting cytokines, targeting acute immune responses to SARS-CoV-2, and targeting pulmonary fibrosis. Despite the clinical efficacy and therapeutic pharmacology speculation, more studies such as large-scale randomized clinical trials, cell and animal experiments are needed to further verify the theory of the Cold-dampness Plague in COVID-19 patients.

Non-linear correlation between daily new cases of COVID-19 and meteorological factors in 127 countries

Meteorological parameters are the critical factors of affecting respiratory infectious disease such as Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS) and influenza, however, the effect of meteorological parameters on coronavirus disease 2019 (COVID-19) remains controversial. This study investigated the effects of meteorological factors on daily new cases of COVID-19 in 127 countries, as of August 31 2020. The log-linear generalized additive model (GAM) was used to analyze the effect of meteorological variables on daily new cases of COVID-19. Our findings revealed that temperature, relative humidity, and wind speed are nonlinearly correlated with daily new cases, and they may be negatively correlated with the daily new cases of COVID-19 over 127 countries when temperature, relative humidity and wind speed were below 20°C, 70% and 7 m/s respectively. Temperature(>20°C) was positively correlated with daily new cases. Wind speed (when>7 m/s) and relative humidity (>70%) was not statistically associated with transmission of COVID-19. The results of this research will be a useful supplement to help healthcare policymakers in the Belt and Road countries, the Centers for Disease Control (CDC) and the World Health Organization (WHO) to develop strategies to combat COVID-19.