Environmental quality, climate indicators, and COVID-19 pandemic: insights from top 10 most affected states of the USA

Conditioning factors in the spreading of Covid-19 - Does geography matter?

There is evidence in literature that the spread of COVID-19 can be influenced by various geographic factors, including territorial features, climate, population density, socioeconomic conditions, and mobility. The objective of the paper is to provide an updated literature review on geographical studies analysing the factors which influenced COVID-19 spreading. This literature review took into account not only the geographical aspects but also the COVID-19-related outcomes (infections and deaths) allowing to discern the potential influencing role of the geographic factors per type of outcome. A total of 112 scientific articles were selected, reviewed and categorized according to subject area, aim, country/region of study, considered geographic and COVID-19 variables, spatial and temporal units of analysis, methodologies, and main findings. Our literature review showed that territorial features may have played a role in determining the uneven geography of COVID-19; for instance, a certain agreement was found regarding the direct relationship between urbanization degree and COVID-19 infections. For what concerns climatic factors, temperature was the variable that correlated the best with COVID-19 infections. Together with climatic factors, socio-demographic ones were extensively taken into account. Most of the analysed studies agreed that population density and human mobility had a significant and direct relationship with COVID-19 infections and deaths. The analysis of the different approaches used to investigate the role of geographic factors in the spreading of the COVID-19 pandemic revealed that the significance/representativeness of the outputs is influenced by the scale considered due to the great spatial variability of geographic aspects. In fact, a more robust and significant association between geographic factors and COVID-19 was found by studies conducted at subnational or local scale rather than at country scale.

Exploring spatiotemporal pattern in the association between short-term exposure to fine particulate matter and COVID-19 incidence in the continental United States: a Leroux-conditional-autoregression-based strategy

Background

Numerous studies have demonstrated that fine particulate matter (PM2.5) is adversely associated with COVID-19 incidence. However, few studies have explored the spatiotemporal heterogeneity in this association, which is critical for developing cost-effective pollution-related policies for a specific location and epidemic stage, as well as, understanding the temporal change of association between PM2.5 and an emerging infectious disease like COVID-19.

Methods

The outcome was state-level daily COVID-19 cases in 49 native United States between April 1, 2020 and December 31, 2021. The exposure variable was the moving average of PM2.5 with a lag range of 0-14 days. A latest proposed strategy was used to investigate the spatial distribution of PM2.5-COVID-19 association in state level. First, generalized additive models were independently constructed for each state to obtain the rough association estimations, which then were smoothed using a Leroux-prior-based conditional autoregression. Finally, a modified time-varying approach was used to analyze the temporal change of association and explore the potential causes spatiotemporal heterogeneity.

Results

In all states, a positive association between PM2.5 and COVID-19 incidence was observed. Nearly one-third of these states, mainly located in the northeastern and middle-northern United States, exhibited statistically significant. On average, a 1 μg/m3 increase in PM2.5 concentration led to an increase in COVID-19 incidence by 0.92% (95%CI: 0.63-1.23%). A U-shaped temporal change of association was examined, with the strongest association occurring in the end of 2021 and the weakest association occurring in September 1, 2020 and July 1, 2021. Vaccination rate was identified as a significant cause for the association heterogeneity, with a stronger association occurring at a higher vaccination rate.

Conclusion

Short-term exposure to PM2.5 and COVID-19 incidence presented positive association in the United States, which exhibited a significant spatiotemporal heterogeneity with strong association in the eastern and middle regions and with a U-shaped temporal change.

Explaining the higher COVID-19 mortality rates among disproportionately Black counties: A decomposition analysis

Background

Why is COVID-19 mortality higher in counties with a disproportionately large (>13.4%) share of Black residents (hereafter "Black counties") relative to others ("non-Black counties")? Existing literature points to six categories of determinants: (1) social distancing, (2) COVID-19 testing, (3) socioeconomic characteristics, (4) environmental characteristics, (5) prevalence of (pre-existing) chronic health conditions, and (6) demographic characteristics. The relative importance of these determinants has not yet been thoroughly examined.

Methods

We built a dataset consisting of 21 sub-indicators across the six categories of determinants for 3108 US counties and their COVID-19 mortality over the period of January 22, 2020-December 31, 2020. Applying the Gelbach's decomposition, we quantified which determinants were most (or least) associated with the COVID-19 mortality disparity between Black and non-Black counties.

Results

We find that COVID-19 death rates were 26 percent higher in Black counties compared to non-Black counties. This disparity was almost completely explained by the six categories of determinants included in our model. Decomposition analyses indicate that county-level demographic and population health characteristics explained most of this disparity. Among all sub-indicators considered, the greater proportion of females and smaller proportion of rural residents in Black counties were the two largest contributors to the COVID-19 mortality gap between Black and non-Black counties. Proportions of diabetic residents, uninsured residents, and the degree of income inequality also significantly contributed to the gap in COVID-19 mortality.

Conclusion

The COVID-19 mortality gap between Black and non-Black counties was largely explained by pre-pandemic differences in demographic and population health characteristics. Policies aiming to reduce the prevalence of chronic conditions and uninsured residents in Black counties would have helped narrow the COVID-19 mortality gap between Black and non-Black counties in 2020.

The Nexus Between COVID-19 Factors and Air Pollution

BACKGROUND AND OBJECTIVE

There have been significant effects of the current coronavirus-19 (COVID-19) infection outbreak on many facets of everyday life, particularly the environment. Despite the fact that a number of studies have already been published on the topic, an analysis of those studies' findings on COVID-19's effects on environmental pollution is still lacking. The goal of the research is to look into greenhouse gas emissions and air pollution in Bangladesh when COVID-19 is under rigorous lockdown. The specific drivers of the asymmetric relationship between air pollution and COVID-19 are being investigated.

METHODS

The nonlinear relationship between carbon dioxide ( C O 2 ) emissions, fine particulate matter ( P M 2 . 5 ) , and COVID-19, as well as its precise components, are also being investigated. To examine the asymmetric link between COVID-19 factors on C O 2 emissions and P M 2 . 5 , we employed the nonlinear autoregressive distributed lag (NARDL) model. Daily positive cases and daily confirmed death by COVID-19 are considered the factors of COVID-19, with lockdown as a dummy variable.

RESULTS

The bound test confirmed the existence of long-run and short-run relationships between variables. Bangladesh's strict lockdown, enforced in reaction to a surge of COVID-19 cases, reduced air pollution and dangerous gas emissions, mainly C O 2 , according to the dynamic multipliers graph.

Analyzing the role of green innovation and public-private partnerships in achieving sustainable development goals: a novel policy framework

The environment's quality is the cornerstone for every country's long-term growth. Pakistan, like other countries, is embracing modern, efficient technologies to build a sustainable environment following the SDGs. In this situation, policymakers and experts have emphasized more on environmental factors. To do this, the study explores the impact of green innovation (GI), public-private partnerships in energy (PPP), energy use (EU), economic development (ED), and power prices (PP) on CO2 emissions in Pakistan from 1980 to 2019. The research uses a novel econometric technique for estimating environmental factors, notably the dynamic autoregressive distributed lag simulations (ARDLS) model and spectral frequency domain causality (SFDC), to examine positive and negative shocks for the prediction of the short-, medium-, and long-run impact of selected determinants, respectively. Additionally, robustness checks were performed using the fully modified OLS (FMOLS), dynamic OLS (DOLS), and canonical cointegrating regression (CCR) estimations. The short and long-term empirical findings indicate that GI lowers emissions; nevertheless, PPP, EU, and ED have a significant impact on emissions in the short run, while the EU increases emissions in the long run. PP, on the other hand, reduces emissions both short and long-term. The FMOLS, DOLS, and CCR estimations indicate significant discoveries. Additionally, the SFDC finding supports the long, medium, and short-term causation theories. This research advocates green innovation for a greener manufacturing process and PPP investment in renewable energy. In addition, the Pakistani government considers these variables while designing a comprehensive protracted environmental plan to meet SDGs 7 and 13.

On the Frontline-A bibliometric Study on Sustainability, Development, Coronaviruses, and COVID-19

The COVID-19 pandemic has placed the world's population in a state of unprecedented public health and global health vulnerability. Risks to public and global health have escalated due to COVID-19 contamination. This has raised the statistics of inequity and environmental concerns. A possible outlook entails reducing the pandemic consequences by prioritizing development, biodiversity, and adaptability, offering buffer solutions. It contains vital methods for studying, comprehending, and unraveling events-examining early responses to COVID-19, sustainability, and development, relating them with overall Coronaviruses reaction. This study maps out environmental, socioeconomic, and medical/technological issues using as statistical techniques multiple correspondence analysis and validated cluster analysis. The findings encourage rapid, long-term development policy involvement to address the pandemic. The resulting crises have highlighted the necessity for the revival of health justice policies anchored in distinctive public health ethical patterns in response to them. As a general rule, resilience and preparedness will be targeted at developing and vulnerable nations and are prone to include access to vaccines, public health care, and health investment. Our findings show the relevance of innovating on sustainable development routes and yardsticks. Sustainable global health requires crucial measures in prevention, preparation, and response. Long-term policy recommendations are needed to address pandemics and their interrelated crises and foster sustained growth and socioecological protection.

Association of Meteorological Factors With COVID-19 During Harmattan in Nigeria

Harmattan is a season of dry, cold, dusty wind, and haze that is peculiar to West Africa. This season and COVID-19 share common conditions such as malaise and respiratory issues like as runny nose, cough and sneezing, and raise a question of a possible relationship that begs to be answered. This study investigated whether the meteorological factors of humidity and wind speed during harmattan have association with COVID-19 incidence and mortality in the 2 major COVID-19 epicenters of Lagos state and the Federal Capital Territory (FCT) in southern and northern geopolitical regions of Nigeria respectively. Data used were from March, 2020 to February, 2022, which corresponded to the period of 2 years after the first case of COVID-19 was detected in Nigeria. Correlation analysis was performed using incidence or mortality data on COVID-19 over the duration of 2 years and during the harmattan periods, as well as the humidity and wind speed data for the corresponding periods. Our results showed that there was no significant correlation between the humidity or wind speed and COVID-19 daily incidence or mortality during the harmattan and non-harmattan periods in Lagos state. In the FCT however, there was a significant positive correlation between humidity and COVID-19 incidence, as well as a negative correlation between wind speed and COVID-19 incidence. No significant correlation existed between humidity or wind speed and daily mortality. Taken together, the findings of this study show that weather components of the harmattan season have association with COVID-19 incidence but not mortality, and the association could vary depending on location.

Impact of meteorological factors and population density on COVID-19 pandemic in Saudi Arabia

Transmission and increase in cases and fatalities of coronavirus disease-2019 (COVID-19) are significantly influenced by the parameters of weather, human activities and population factors. However, study gap on the seasonality of COVID-19 and impact of environmental factors on the pandemic in Saudi Arabia is present. The main aim of the study is to evaluate the impact of environment on the COVID-19 pandemic. Data were analyzed from January 2020 to July 2021. The generalized estimating equation (GEE) was used to determine the effect of environmental variables on longitudinal outcomes. Spearman's rank correlation coefficient (r_s ) was used to analyze the impact of different parameters on the outcome of the pandemic. Multiple sequence alignment was performed by using ClustalW. Vaccination and fatalities (r _s = -0.85) had the highest association followed by vaccination with cases (r _s = -0.81) and population density with the fatalities (r_s  = 0.71). The growth rate had the highest correlation with sun hours (r _s = -0.63). Isolates from variant of concern alpha and beta were detected. Most of the reference sequences in Saudi Arabia were closely related with B.1.427/429 variant. Clade GH (54%) was the most prevalent followed by O (27%), GR (9%), G (6%), and S (4%), respectively. Male to female patient ratio was 1.4:1. About 95% fatality and hospitalization were reported in patients aged >60 years. This study will create a comprehensive insight of the interaction of environmental factors and the pandemic and add knowledge on seasonality of COVID-19 in Saudi Arabia.

Effects of air pollution and weather on the initial COVID-19 outbreaks in United States, Italy, Spain, and China: A comparative study

Contrasting effects have been identified in association of weather (temperature and humidity) and pollutant gases with COVID-19 infection, which could be derived from the influence of lockdowns and season change. The influence of pollutant gases and climate during the initial phases of the pandemic, before the closures and the change of season in the northern hemisphere, is unknown. Here, we used a spatial-temporal Bayesian zero-inflated-Poisson model to test for short-term associations of weather and pollutant gases with the relative risk of COVID-19 disease in China (first outbreak) and the countries with more cases during the initial pandemic (the United States, Spain and Italy), considering also the effects of season and lockdown. We found contrasting association between pollutant gases and COVID-19 risk in the United States, Italy, and Spain, while in China it was negatively associated (except for SO₂ ). COVID-19 risk was positively associated with specific humidity in all countries, while temperature presented a negative effect. Our findings showed that short-term associations of air pollutants with COVID-19 infection vary strongly between countries, while generalized effects of temperature (negative) and humidity (positive) with COVID-19 was found. Our results show novel information about the influence of pollution and weather on the initial outbreaks, which contribute to unravel the mechanisms during the beginning of the pandemic.

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.

Machine learning analysis on the impacts of COVID-19 on India's renewable energy transitions and air quality

India is severely affected by the COVID-19 pandemic and is facing an unprecedented public health emergency. While the country's immediate measures focus on combating the coronavirus spread, it is important to investigate the impacts of the current crisis on India's renewable energy transition and air quality. India's economic slowdown is mainly compounded by the collapse of global oil prices and the erosion of global energy demand. A clean energy transition is a key step in enabling the integration of energy and climate. Millions in India are affected owing to fossil fuel pollution and the increasing climate heating that has led to inconceivable health impacts. This paper attempts to study the impact of COVID-19 on India's climate and renewable energy transitions through machine learning algorithms. India is observing a massive collapse in energy demand during the lockdown as its coal generation is suffering the worst part of the ongoing pandemic. During this current COVID-19 crisis, the renewable energy sector benefits from its competitive cost and the Indian government's must-run status to run generators based on renewable energy sources. In contrast to fossil fuel-based power plants, renewable energy sources are not exposed to the same supply chain disruptions in this current pandemic situation. India has the definite potential to surprise the global community and contribute to cost-effective decarbonization. Moreover, the country has a good chance of building more flexibility into the renewable energy sector to avoid an unstable future.

Effect of ambient air pollution and meteorological factors on the potential transmission of COVID-19 in Turkey

There is a need to improve the understanding of air quality parameters and meteorological conditions on the transmission of SARS-CoV-2 in different regions of the world. In this preliminary study, we explore the relationship between short-term air quality (nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and particulate matter (PM_2.5, PM₁₀)) exposure, temperature, humidity, and wind speed on SARS-CoV-2 transmission in 41 cities of Turkey with reported weekly cases from February 8 to April 2, 2021. Both linear and non-linear relationships were explored. The nonlinear association between weekly confirmed cases and short-term exposure to predictor factors was investigated using a generalized additive model (GAM). The preliminary results indicate that there was a significant association between humidity and weekly confirmed COVID-19 cases. The cooler temperatures had a positive correlation with the occurrence of new confirmed cases. The low PM_2.5 concentrations had a negative correlation with the number of new cases, while reducing SO₂ concentrations may help decrease the number of new cases. This is the first study investigating the relationship between measured air pollutants, meteorological factors, and the number of weekly confirmed COVID-19 cases across Turkey. There are several limitations of the presented study, however, the preliminary results show that there is a need to understand the impacts of regional air quality parameters and meteorological factors on the transmission of the virus.

Understanding the COVID-19 pandemic prevalence in Africa through optimal feature selection and clustering: evidence from a statistical perspective

The COVID-19 pandemic, which outbroke in Wuhan (China) in December 2019, severely hit almost all sectors of activity in the world as a consequence of the restrictive measures imposed. Two years later, Africa still emerges as the least affected continent by the pandemic. This study analyzed COVID-19 prevalence across African countries through country-level variables prior to clustering. Using Spearman-rank correlation, multicollinearity analysis and univariate filtering, 9 country-level variables were identified from an initial set of 34 variables. These variables relate to socioeconomic status, population structure, healthcare system and environment and the climatic setting. A clustering of the 54 African countries is further carried out through the use of agglomerative hierarchical clustering (AHC) method, which generated 3 distinctive clusters. Cluster 1 (11 countries) is the most affected by COVID-19 (median of 63,508.6 confirmed cases and 946.5 deaths per million) and is composed of countries with the highest socioeconomic status. Cluster 2 (27 countries) is the least affected (median of 4473.7 confirmed cases and 81.2 deaths per million), and mainly features countries with the least socioeconomic features and international exposure. Cluster 3 (16 countries) is intermediate in terms of COVID-19 prevalence (median of 2569.3 confirmed cases and 35.7 deaths per million) and features countries the least urbanized and geographically close to the equator, with intermediate international exposure and socioeconomic features. These findings shed light on the main features of COVID-19 prevalence in Africa and might help refine effectively coping management strategies of the ongoing pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-022-02646-3.

Correlation between COVID-19 and weather variables: A meta-analysis

Background

COVID-19 has significantly impacted humans worldwide in recent times. Weather variables have a remarkable effect on COVID-19 spread all over the universe.

Objectives

The aim of this study was to find the correlation between weather variables with COVID-19 cases and COVID-19 deaths.

Methods

Five electronic databases such as PubMed, Science Direct, Scopus, Ovid (Medline), and Ovid (Embase) were searched to conduct the literature survey from January 01, 2020, to February 03, 2022. Both fixed-effects and random-effects models were used to calculate pooled correlation and 95% confidence interval (CI) for both effect measures. Included studies heterogeneity was measured by Cochrane chi-square test statistic Q, I2 and τ2. Funnel plot was used to measure publication bias. A Sensitivity analysis was also carried out.

Results

Total 38 studies were analyzed in this study. The result of this analysis showed a significantly negative impact on COVID-19 fixed effect incidence and weather variables such as temperature (r = -0.113∗∗∗), relative humidity (r = -0.019∗∗∗), precipitation (r = -0.143∗∗∗), air pressure (r = -0.073∗), and sunlight (r = -0.277∗∗∗) and also found positive impact on wind speed (r = 0.076∗∗∗) and dew point (r = 0.115∗∗∗). From this analysis, significant negative impact was also found for COVID-19 fixed effect death and weather variables such as temperature (r = -0.094∗∗∗), wind speed (r = -0.048∗∗), rainfall (r = -0.158∗∗∗), sunlight (r = -0.271∗∗∗) and positive impact for relative humidity (r = 0.059∗∗∗).

Conclusion

This meta-analysis disclosed significant correlations between weather and COVID-19 cases and deaths. The findings of this analysis would help policymakers and the health professionals to reduce the cases and fatality rate depending on weather forecast techniques and fight this pandemic using restricted assets.

Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA

The objective of this study is to compare the different methods which are effective in predicting data of the short-term effect of COVID-19 confirmed cases and DJI closed stock market in the US. Data for confirmed cases of COVID-19 has been obtained from Worldometer, the database of Johns Hopkins University and the US stock market data (DJI) was obtained from Yahoo Finance. The data starts from 20 January 2020 (first confirmed COVID-19 case the US) to 06 December 2020 and DJI data covers 21 January 2019 to 04 December 2020. COVID-19 data was tested for the period 30 November to 06 December and DJI from 25 November 2020 to 04 December. From the result, we find that the method SutteARIMA was found more suitable to calculate the daily forecasts of COVID-29 confirmed cases and DJI in the US and this method has been used in this study. For the evaluation of the prediction methods, the accuracy measure means absolute percentage error (MAPE) has been used. The MAPE value with the SutteARIMA of 0.56 and 0.60 for COVID-19 and DJI stock respectively was found to be smaller than the MAPE value with ARIMA method.

Geographically weighted regression model for physical, social, and economic factors affecting the COVID-19 pandemic spreading

This study aims to analyze the spatial distribution of the epidemic spread and the role of the physical, social, and economic characteristics in this spreading. A geographically weighted regression (GWR) model was built within a GIS environment using infection data monitored by the Iraqi Ministry of Health records for 10 months from March to December 2020. The factors adopted in this model are the size of urban interaction areas and human gatherings, movement level and accessibility, and the volume of public services and facilities that attract people. The results show that it would be possible to deal with each administrative unit in proportion to its circumstances in light of the factors that appear in it. So, there will not be a single treatment for all areas with different urban characteristics, which sometimes helps not to stop social and economic life due to the imposition of a comprehensive ban on movement and activities. Therefore, there will be other supportive policies other than the ban, depending on the urban indicators for each region, such as reducing external movement from it or relying on preventing public activities only.

Discovering the evolution of Pollution Haven Hypothesis: A literature review and future research agenda

In order to reduce environmental degradation, there has been an increased focus on identifying the main conftributors to environmental degradation and reducing carbon footprints to promote sustainable development. Although the recent focus on institutional and policy reforms has led to a higher focus on environmental discussion, little is known about the status of research on the Pollution Haven Hypothesis (PHH). Hence, the current study evaluates the research dynamics of this field by recognizing most central researchers and key publication outlets from the perspectives of most citations and productivity, research directions, common keywords, countries with the highest academic contribution, and changes in research matrices. Our selection of 494 journal articles from the WOS indicates that King Saud University and the University of Wah were the most productive research institutions, and China was the most productive geographical region. Environmental Science & Pollution Research was identified as the most common outlet for research publications. We also identified strong academic cooperation, notably between China and Pakistan. Moreover, the co-occurrence network identified the Pollution Haven Hypothesis and economic growth nexus, trade, pollution haven and developing economies and FDI, carbon emissions, and pollution haven nexus as the three main prevailing research themes. Lastly, we provide useful policy implications to maximize the impact of environmental reforms and avoid environmental degradation.

A review of the impact of environmental factors and pollutants on covid-19 transmission

The coronavirus disease (COVID-19) caused an unprecedented loss of life with colossal social and economic fallout over 237 countries and territories worldwide. Environmental conditions played a significant role in spreading the virus. Despite the availability of literature, the consecutive waves of COVID-19 in all geographical conditions create the necessity of reviewing the impact of environmental factors on it. This study synthesized and reviewed the findings of 110 previously published articles on meteorological factors and COVID-19 transmission. This study aimed to identify the diversified impacts of meteorological factors on the spread of infection and suggests future research. Temperature, rainfall, air quality, sunshine, wind speed, air pollution, and humidity were found as investigated frequently. Correlation and regression analysis have been widely used in previous studies. Most of the literature showed that temperature and humidity have a favorable relationship with the spread of COVID-19. On the other hand, 20 articles stated no relationship with humidity, and nine were revealed the negative effect of temperature. The daily number of COVID-19 confirmed cases increased by 4.86% for every 1 °C increase in temperature. Sunlight was also found as a significant factor in 10 studies. Moreover, increasing COVID-19 incidence appeared to be associated with increased air pollution, particularly PM10, PM2.5, and O₃ concentrations. Studies also indicated a negative relation between the air quality index and the COVID-19 cases. This review determined environmental variables' complex and contradictory effects on COVID-19 transmission. Hence it becomes essential to include environmental parameters into epidemiological models and controlled laboratory experiments to draw more precious results.

An outlook on the development of renewable energy, policy measures to reshape the current energy mix, and how to achieve sustainable economic growth in the post COVID-19 era

Currently, COVID-19 due to emergence of various variants shows no signs of slowing down and has affected every aspect of life with significant negative impact on economic and energy structures around the world. As a result, the governments around the world have introduced policy responses to help businesses and industrial units to overcome the consequences of compliance with COVID-19 strategies. Our analysis indicates that global energy sector is one of the most severely affected industries as energy price mechanisms, energy demand, and energy supply have shown great uncertainty under these unprecedented economic and social changes. In this regard, we provide brief overview of demand, supply, and pricing structure of energy products as well as policy mechanisms to provide better outlook about how industrial sector can cope with energy consumption in the post pandemic era. We further propose changes in the existing policy mechanisms so that transition towards renewable energy sources under different environmental agreements can be achieved. Moreover, as a reference, we outline major challenges and policy recommendations to ease energy transition from fossil fuels to environmental friendly energy mix.

Exploring the Deep-Learning Techniques in Detecting the Presence of Coronavirus in the Chest X-Ray Images: A Comprehensive Review

The deadly coronavirus (COVID-19) is one of the dangerous diseases affecting the entire world and is fastly spreading disease. This spread can be reduced by detecting and quarantining the patients at an earlier stage. The most common diagnostic tool for detecting the coronavirus is the Reverse transcription-polymerase chain reaction (RT-PCR) test which is time-consuming and also needs more equipment and manpower. Furthermore, many countries had a deficit of RTPCR kits. This is why it is exceptionally very crucial to develop artificial intelligence (AI) techniques to detect the outbreak of coronavirus. This motivated many researchers to involve deep-learning methods using X-ray images for more decisive analysis. Thus, this paper outlines many papers that used traditional and pre-trained deep learning methods that are newly developed to reduce the spread of COVID-19 disease. Specifically, advanced deep learning methods play a critical role in extracting the features from the chest X-ray images. These features are then used to classify whether the patient is affected with coronavirus or not. Besides, this paper shows that deep learning techniques have probable applications in the medical field.

Artificial Intelligence to Combat the Sting of the Pandemic on the Psychological Realms of Human Brain

We are inhabitants of the universe in this anomalous time due to the novel corona virus. COVID-19, which WHO has annotated as a pandemic, is an infectious and contagious disease hastened by the most freshly perceived coronavirus. COVID-19 has gravely hit different people in different ways, some with physical symptoms and some patients will likely be more susceptible to insignificant and extreme symptoms of mental illness. Such patients often with pre-existing mobility limitations get imprisoned in their own homes. The current pandemic beseeches for intense contemplation of the mental health of patients to reduce their worry and heal their fear, depression and anxiety due to quarantine. Artificial intelligence (AI) is more and more remarkable in public, academic, and clinical provinces. This paper aims in addressing the mental health problems faced by patients affected by COVID-19 and recommends artificial intelligence integrated virtual counsellor who can provide advice to their problems. The strategy of AI is being developed and enhanced which will possibly help in addressing the problems in this pandemic time.

Role of alternative and nuclear energy in stimulating environmental sustainability: impact of government expenditures

Environmental sustainability is fundamental to the survival of our planet and ourselves, as polluted air, water, and land severely affect communities and society to thrive and damage the quality of life. This study examined the role of alternative and nuclear energy in stimulating the environment sustainably while mediating the function of government expenditure and economic growth in the top three highest CO₂ emitter countries. We apply advanced econometric methodologies for empirical analysis from 1981 to 2016 and find long-run relationships among the variables that suggest general government final consumption expenditure and economic growth are positively related to CO₂ emissions. Moreover, alternative and nuclear energy and the square root of economic growth (EKC) improve environmental sustainability. The general government's final consumption expenditure and economic growth deteriorate environmental sustainability. Policymakers in the top three highest CO₂ emitter countries are encouraged to adopt a comprehensive approach to access the compatibility of alternative and nuclear energy sources, changing the source of uranium from mined ore to seawater, encourage, tide, and include macroeconomic stabilization, public and private fiscal position goals with the environmental sustainability policies. Moreover, governments are suggested to incorporate green fiscal policies to address the global environmental challenges and promote a green economy. Aligning government expenditures with environmental goals, reflecting externalities in prices, broader fiscal reform by making fiscal space for clean and green investment is highly encouraged to achieve the sustainable development goals' target. Study limitations and directions for future research in the area are presented.

Evaluation of the effects of meteorological factors on COVID-19 prevalence by the distributed lag nonlinear model

Background

Although numerous studies have explored the impact of meteorological factors on the epidemic of COVID-19, their relationship remains controversial and needs to be clarified.

Methods

We assessed the risk effect of various meteorological factors on COVID-19 infection using the distributed lag nonlinear model, based on related data from July 1, 2020, to June 30, 2021, in eight countries, including Portugal, Greece, Egypt, South Africa, Paraguay, Uruguay, South Korea, and Japan, which are in Europe, Africa, South America, and Asia, respectively. We also explored associations between COVID-19 prevalence and individual meteorological factors by the Spearman’s rank correlation test.

Results

There were significant non-linear relationships between both temperature and relative humidity and COVID-19 prevalence. In the countries located in the Northern Hemisphere with similar latitudes, the risk of COVID-19 infection was the highest at temperature below 5 ℃. In the countries located in the Southern Hemisphere with similar latitudes, their highest infection risk occurred at around 15 ℃. Nevertheless, in most countries, high temperature showed no significant association with reduced risk of COVID-19 infection. The effect pattern of relative humidity on COVID-19 depended on the range of its variation in countries. Overall, low relative humidity was correlated with increased risk of COVID-19 infection, while the high risk of infection at extremely high relative humidity could occur in some countries. In addition, relative humidity had a longer lag effect on COVID-19 than temperature.

Conclusions

The effects of meteorological factors on COVID-19 prevalence are nonlinear and hysteretic. Although low temperature and relative humidity may lower the risk of COVID-19, high temperature or relative humidity could also be associated with a high prevalence of COVID-19 in some regions.

Impact of air temperature and containment measures on mitigating the intrahousehold transmission of SARS-CoV-2: a data-based modelling analysis

OBJECTIVES

Air temperature has been considered a modifiable and contributable variable in COVID-19 transmission. Implementation of non-pharmaceutical interventions (NPIs) has also made an impact on COVID-19 transmission, changing the transmission pattern to intrahousehold transmission under stringent containment measures. Therefore, it is necessary to re-estimate the influence of air temperature on COVID-19 transmission while excluding the influence of NPIs.

DESIGN, SETTING AND PARTICIPANTS

This study is a data-based comprehensive modelling analysis. A stochastic epidemiological model, the ScEIQR model (contactable susceptible-exposed-infected-quarantined-removed), was established to evaluate the influence of air temperature and containment measures on the intrahousehold spread of COVID-19. Epidemic data on COVID-19, including daily confirmed cases, number of close contacts, etc, were collected from the National Health Commission of China.

OUTCOME MEASURES

The model was fitted using the Metropolis-Hastings algorithm with a cost function based on the least squares method. The LOESS (locally weighted scatterplot smoothing) regression function was used to assess the relationship between air temperature and rate of COVID-19 transmission within the ScEIQR model.

RESULTS

The ScEIQR model indicated that the optimal temperature for spread of COVID-19 peaked at 10℃ (50℉), ranging from 5℃ to 14℃ (41℉-57.2℉). In the fitted model, the fitted intrahousehold transmission rate (β') of COVID-19 was 10.22 (IQR 8.47-12.35) across mainland China. The association between air temperature and β' of COVID-19 suggests that COVID-19 might be seasonal. Our model also validated the effectiveness of NPIs, demonstrating that diminishing contactable susceptibility (Sc) and avoiding delay in diagnosis and hospitalisation (η) were more effective than contact tracing (κ and ρ).

CONCLUSIONS

We constructed a novel epidemic model to estimate the effect of air temperature on COVID-19 transmission beyond implementation of NPIs, which can inform public health strategy and predict the transmission of COVID-19.

Impact of climate indicators on the COVID-19 pandemic in Saudi Arabia

The novel coronavirus (COVID-19) outbreak has left a major impact on daily lifestyle and human activities. Many recent studies confirmed that the COVID-19 pandemic has human-to-human transmissibility. Additional studies claimed that other factors affect the viability, transmissibility, and propagation range of COVID-19. The effect of weather factors on the spread of COVID-19 has gained much attention among researchers. The current study investigates the relationship between climate indicators and daily detected COVID-19 cases in Saudi Arabia, focusing on the top five cities with confirmed cases. The examined climate indicators were temperature (°F), dew point (°F), humidity (%), wind speed (mph), and pressure (Hg). Using data from Spring 2020 and 2021, we conducted spatio-temporal correlation, regression, and time series analyses. The results provide preliminary evidence that the COVID-19 pandemic spread in most of the considered cities is significantly correlated with temperature (positive correlation) and pressure (negative correlation). The discrepancies in the results from different cites addressed in this study suggest that non-meteorological factors need to be explored in conjunction with weather attributes in a sufficiently long-term analysis to provide meaningful policy measures for the future.

Do environmental pollutants carrier to COVID-19 pandemic? A cross-sectional analysis

The coronavirus disease (COVID-19) is a highly transmitted disease that spreads all over the globe in a short period. Environmental pollutants are considered one of the carriers to spread the COVID-19 pandemic through health damages. Carbon emissions, PM2.5 emissions, nitrous oxide emissions, GHG, and other GHG emissions are mainly judged separately in the earlier studies in different economic settings. The study hypothesizes that environmental pollutants adversely affect healthcare outcomes, likely to infected people by contagious diseases, including coronavirus cases. The subject matter is vital to analyze the preventive healthcare theory by using different environmental pollutants on the COVID-19 factors: total infected cases, total death cases, and case fatality ratio, in a large cross-section of 119 countries. The study employed the generalized least square (GLS) method for robust inferences. The results show that GHG and CO2 emissions are critical factors likely to increase total coronavirus cases and death rates. On the other hand, nitrous oxide, carbon, and transport emissions increase the case fatality ratio through healthcare damages. The study concludes that stringent environmental policies and improving healthcare infrastructure can control coronavirus cases across countries.

The effects of air pollution, meteorological parameters, and climate change on COVID-19 comorbidity and health disparities: A systematic review

Air pollutants, especially particulate matter, and other meteorological factors serve as important carriers of infectious microbes and play a critical role in the spread of disease. However, there remains uncertainty about the relationship among particulate matter, other air pollutants, meteorological conditions and climate change and the spread of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), hereafter referred to as COVID-19. A systematic review was conducted using PRISMA guidelines to identify the relationship between air quality, meteorological conditions and climate change, and COVID-19 risk and outcomes, host related factors, co-morbidities and disparities. Out of a total of 170,296 scientific publications screened, 63 studies were identified that focused on the relationship between air pollutants and COVID-19. Additionally, the contribution of host related-factors, co-morbidities, and health disparities was discussed. This review found a preponderance of evidence of a positive relationship between PM_2.5, other air pollutants, and meteorological conditions and climate change on COVID-19 risk and outcomes. The effects of PM_2.5, air pollutants, and meteorological conditions on COVID-19 mortalities were most commonly experienced by socially disadvantaged and vulnerable populations. Results however, were not entirely consistent, and varied by geographic region and study. Opportunities for using data to guide local response to COVID-19 are identified.

Long-term statistical assessment of meteorological indicators and COVID-19 outbreak in hot and arid climate, Bahrain

The COVID-19 pandemic has significantly impacted the global lifestyle, and the spreading of the virus is unprecedented. This study is aimed at assessing the association between the meteorological indicators such as air temperature (°C), relative humidity (%), wind speed (w/s), solar radiation, and PM_2.5 with the COVID-19 infected cases in the hot, arid climate of Bahrain. Kendall and Spearman rank correlation coefficients and quantile on quantile regression were used as main econometric analysis to determine the degree of the relationship between related variables. The dataset analysis was performed from 05 April 2020, to 10 January 2021. The empirical findings indicate that the air temperature, humidity, solar radiation, wind speed indicators, and PM2.5 have a significant association with the COVID-19 newly infected cases. The current study findings allow us to suggest that Bahrain's relatively successful response to neighboring GULF economies can be attributed to the successful environmental reforms and significant upgrades to the health care facilities. We further report that a long-term empirical analysis between meteorological factors and respiratory illness threats will provide useful policy measures against future outbreaks.

The impact of air pollution on COVID-19 pandemic varied within different cities in South America using different models

There is a rising concern that air pollution plays an important role in the COVID-19 pandemic. However, the results were not consistent on the association between air pollution and the spread of COVID-19. In the study, air pollution data and the confirmed cases of COVID-19 were both gathered from five severe cities across three countries in South America. Daily real-time population regeneration (R_t) was calculated to assess the spread of COVID-19. Two frequently used models, generalized additive models (GAM) and multiple linear regression, were both used to explore the impact of environmental pollutants on the epidemic. Wide ranges of all six air pollutants were detected across the five cities. Spearman's correlation analysis confirmed the positive correlation within six pollutants. Rt value showed a gradual decline in all the five cities. Further analysis showed that the association between air pollution and COVID-19 varied across five cities. According to our research results, even for the same region, varied models gave inconsistent results. For example, in Sao Paulo, both models show SO₂ and O₃ are significant independent variables, however, the GAM model shows that PM₁₀ has a nonlinear negative correlation with R_t, while PM₁₀ has no significant correlation in the multiple linear model. Moreover, in the case of multiple regions, currently used models should be selected according to local conditions. Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which will help states, health practitioners, and policy makers in combating the COVID-19 pandemic in South America.

Health impacts of daily weather fluctuations: Empirical evidence from COVID-19 in U.S. counties

The emergence of the novel coronavirus has necessitated immense research efforts to understand how several non-environmental and environmental factors affect transmission. With the United States leading the path in terms of case incidence, it is important to investigate how weather variables influence the spread of the disease in the country. This paper assembles a detailed and comprehensive dataset comprising COVID-19 cases and climatological variables for all counties in the continental U.S. and uses a developed econometric approach to estimate the causal effect of certain weather factors on the growth rate of infection. The results indicate a non-linear and significant negative relationship between the individual weather measures and the growth rate of COVID-19 in the U.S. Specifically, the paper finds that a 1 °C rise in daily temperature will reduce daily covid growth rate in the U.S. by approximately 6 percent in the following week, while a marginal increase in relative humidity reduces the same outcome by 1 percent over a similar period. In comparison, a 1 m/s increase in daily wind speed will bring about an 8 percent drop in daily growth rate of COVID-19 in the country. These results differ by location and are robust to several sensitivity checks, so large deviations are unexpected.

Impact of meteorological parameters and population density on variants of SARS-CoV-2 and outcome of COVID-19 pandemic in Japan

Although vaccines have become available, emergence and rapid transmission of new variants have added new paradigm in the coronavirus disease-2019 (COVID-19) pandemic. Weather, population and host immunity have been detected as the regulatory elements of COVID-19. This study aims to investigate the effects of weather, population and host factors on the outcome of COVID-19 and mutation frequency in Japan. Data were collected during January 2020 to February 2021. About 92% isolates were form GR clades. Variants 501Y.V1 (53%) and 452R.V1 (24%) were most prevalent in Japan. The strongest correlation was detected between fatalities and population density (rs = 0.81) followed by total population (rs = 0.72). Relative humidity had the highest correlation (rs = -0.71) with the case fatality rate. Cluster mutations namely N501Y (45%), E484K (30%), N439K (16%), K417N (6%) and T478I (3%) at spike protein have increased during January to February 2021. Above 90% fatality was detected in patients aged >60 years. The ratio of male to female patients of COVID-19 was 1.35:1. This study will help to understand the seasonality of COVID-19 and impact of weather on the outcome which will add knowledge to reduce the health burden of COVID-19 by the international organisations and policy makers.