Association between meteorological indicators and COVID-19 pandemic in Pakistan

Ambient heat and diabetes hospitalizations: Does the timing of heat exposure matter?

BACKGROUND

Although ambient heat exposure is linked with diabetes mortality, the impacts of heat exposure on diabetes-related hospitalizations remain controversial. Previous research did not examine the timing of heat-diabetes associations and relation with comorbidities/risk factors.

OBJECTIVE

We examined the association between heat exposure and diabetes-related hospitalizations in the transitional and summer months and identified populations vulnerable to heat.

METHODS

We conducted a time-stratified case-crossover study. Data on diabetes hospital admissions (primary diagnosis of type 1 and type 2 diabetes, 2013-2020) were collected by the New York State (NYS) Department of Health under the state legislative mandate. We treated temperature and air pollutants as continuous variables and defined the heat exposure as per interquartile range (IQR, a measure between the 25th and 75th percentiles) increase of daily mean temperature. Conditional logistic regressions were performed to quantify the heat-diabetes associations after controlling for air pollutants and time variant variables. Multiplicative-scale interactions between heat and demographics/comorbidities/risk factors on diabetes hospitalizations were investigated.

RESULTS

Each IQR increase in temperature was associated with significantly increased risks for diabetes admissions that occurred immediately and lasted for an entire week during multi-day lags in the transitional month of May (ranges of excess risk: 3.1 %-4.8 %) but not in the summer (June-August) (ranges of excess risk: -0.3 %-1.3 %). The significant increases in the excess risk of diabetes were also found among diabetes patients with complications of neuronopathy (excess risk: 27.7 %) and hypoglycemia (excess risk: 19.1 %). Furthermore, the modification effects on the heat-diabetes association were significantly stronger in females, Medicaid enrollees, non-compliant patients, and individuals with comorbidities of atherosclerotic heart disease and old myocardial infarction.

CONCLUSIONS

Ambient heat exposure significantly increased the burden of hospital admissions for diabetes in transitional rather than summer months indicating the importance of exposure timing. Vulnerability to heat varied by demographics and heart comorbidity.

The application of simulation methods during the COVID-19 pandemic: A scoping review

With the outbreak of COVID-19 pandemic, simulation modelling approaches have become effective tools to simulate the potential effects of different intervention measures and predict the dynamic COVID-19 trends. In this scoping review, Studies published between February 2020 and May 2022 that investigated the spread of COVID-19 using four common simulation modeling methods were systematically reported and summarized. Publication trend, characteristics, software, and code availability of included articles were analyzed. Among the included 340 studies, most articles used agent-based model (ABM; n = 258; 75.9 %), followed by the models of system dynamics (n = 42; 12.4 %), discrete event simulation (n = 25; 7.4 %), and hybrid simulation (n = 15; 4.4 %). Furthermore, our review emphasized the purposes and sample time period of included articles. We classified the purpose of the 340 included studies into five categories, most studies mainly analyzed the spread of COVID-19 under policy interventions. For the sample time period analysis, most included studies analyzed the COVID-19 spread in the second wave. Our findings play a crucial role for policymakers to make evidence-based decisions in preventing the spread of COVID-19 pandemic and help in providing scientific decision-makings resilient to similar events and infectious diseases in the future.

Impact of geographical, meteorological, demographic, and economic indicators on the trend of COVID-19: A global evidence from 202 affected countries

Research problem

Public health and the economy face immense problems because of pathogens in history globally. The outbreak of novel SARS-CoV-2 emerged in the form of coronavirus (COVID-19), which affected global health and the economy in almost all countries of the world.

Study design

The objective of this research is to examine the trend of COVID-19, deaths, and transmission rates in 202 affected countries. The virus-affected countries were grouped according to their continent, meteorological indicators, demography, and income. This is quantitative research in which we have applied the Poisson regression method to assess how temperature, precipitation, population density, and income level impact COVID-19 cases and fatalities. This has been done by using a semi-parametric and additive polynomial model.

Findings

The trend analysis depicts that COVID-19 cases per million were comparatively higher for two groups of countries i.e., (a) average temperature below 7.5 °C and (b) average temperature between 7.5 °C and 15 °C, up to the 729th day of the outbreak. However, COVID-19 cases per million were comparatively low in the countries having an average temperature between 22.5 °C and 30 °C. The day-wise trend was comparatively higher for the countries having average precipitation between (a) 1 mm and 750 mm and (b) 750 mm and 1500 mm up to the 729th day of the outbreak. The day-wise trend was comparatively higher for the countries having more than 1000 people per sq. km. Discussing the COVID-19 cases per million, the day-wise trend was higher for the HICs, followed by UMICs, LMICs, and LIC.

Conclusion

The study highlights the need for targeted interventions and responses based on the specific circumstances and factors affecting each country, including their geographical location, temperature, precipitation levels, population density, and per capita income.

Investigation of the effects of temperature and relative humidity on the propagation of COVID-19 in different climatic zones

This study aims to evaluate the effects of temperature and relative humidity on the propagation of COVID-19 for indoor heating, ventilation, and air conditioning design and policy development in different climate zones. We proposed a cumulative lag model with two specific parameters of specific average temperature and specific relative humidity to evaluate the impact of temperature and relative humidity on COVID-19 transmission by calculating the relative risk of cumulative effect and the relative risk of lag effect. We considered the temperature and relative humidity corresponding to the relative risk of cumulative effect or the relative risk of lag effect equal to 1 as the thresholds of outbreak. In this paper, we took the overall relative risk of cumulative effect equal to 1 as the thresholds. Data on daily new confirmed cases of COVID-19 since January 1, 2021, to December 31, 2021, for three sites in each of four climate zones similar to cold, mild, hot summer and cold winter, and hot summer and warm winter were selected for this study. Temperature and relative humidity had a lagged effect on COVID-19 transmission, with peaking the relative risk of lag effect at a lag of 3-7 days for most regions. All regions had different parameters areas with the relative risk of cumulative effect greater than 1. The overall relative risk of cumulative effect was greater than 1 in all regions when specific relative humidity was higher than 0.4, and when specific average temperature was higher than 0.42. In areas similar to hot summer and cold winter, temperature and the overall relative risk of cumulative effect were highly monotonically positively correlated. In areas similar to hot summer and warm winter, there was a monotonically positive correlation between relative humidity and the overall relative risk of cumulative effect. This study provides targeted recommendations for indoor air and heating, ventilation, and air conditioning system control strategies and outbreak prevention strategies to reduce the risk of COVID-19 transmission. In addition, countries should combine vaccination and non-pharmaceutical control measures, and strict containment policies are beneficial to control another pandemic of COVID-19 and similar viruses.

Ecosystem degradation and the spread of Covid-19

The linkages between the emergence of zoonotic diseases and ecosystem degradation have been widely acknowledged by the scientific community and policy makers. In this paper we investigate the relationship between human overexploitation of natural resources, represented by the Human Appropriation of Net Primary Production Index (HANPP) and the spread of Covid-19 cases during the first pandemic wave in 730 regions of 63 countries worldwide. Using a Bayesian estimation technique, we highlight the significant role of HANPP as a driver of Covid-19 diffusion, besides confirming the well-known impact of population size and the effects of other socio-economic variables. We believe that these findings could be relevant for policy makers in their effort towards a more sustainable intensive agriculture and responsible urbanisation.

Association between meteorological factors and COVID-19 transmission in low- and middle-income countries: A time-stratified case-crossover study

BACKGROUND

Evidence is limited regarding the association between meteorological factors and COVID-19 transmission in low- and middle-income countries (LMICs).

OBJECTIVE

To investigate the independent and interactive effects of temperature, relative humidity (RH), and ultraviolet (UV) radiation on the spread of COVID-19 in LMICs.

METHODS

We collected daily data on COVID-19 confirmed cases, meteorological factors and non-pharmaceutical interventions (NPIs) in 2143 city- and district-level sites from 6 LMICs during 2020. We applied a time-stratified case-crossover design with distributed lag nonlinear model to evaluate the independent and interactive effects of meteorological factors on COVID-19 transmission after controlling NPIs. We generated an overall estimate through pooling site-specific relative risks (RR) using a multivariate meta-regression model.

RESULTS

There was a positive, non-linear, association between temperature and COVID-19 confirmed cases in all study sites, while RH and UV showed negative non-linear associations. RR of the 90th percentile temperature (28.1 °C) was 1.14 [95% confidence interval (CI): 1.02, 1.28] compared with the 50th percentile temperature (24.4 °C). RR of the10th percentile UV was 1.41 (95% CI: 1.29, 1.54). High temperature and high RH were associated with increased risks in temperate climate but decreased risks in tropical climate, while UV exhibited a consistent, negative association across climate zones. Temperature, RH, and UV interacted to affect COVID-19 transmission. Temperature and RH also showed higher risks in low NPIs sites.

CONCLUSION

Temperature, RH, and UV appeared to independently and interactively affect the transmission of COVID-19 in LMICs but such associations varied with climate zones. Our results suggest that more attention should be paid to meteorological variation when the transmission of COVID-19 is still rampant in LMICs.

The Effect of Meteorological Factors on the COVID-19 Pandemic in Northeast Turkiye

Introduction Although various studies have been conducted on the relationship between meteorological factors and coronavirus disease 2019 (COVID-19), this issue has not been sufficiently clarified. In particular, there are a limited number of studies on the course of COVID-19 in the warmer-humidity seasons. Methods Patients presenting to the emergency departments of health institutions and to clinics set aside for cases of suspected COVID-19 in the province of Rize between 1 June and 31 August 2021 and who met the case definition based on the Turkish COVID-19 epidemiological guideline were included in this retrospective study. The effect of meteorological factors on case numbers throughout the study was investigated. Results During the study period, 80,490 tests were performed on patients presenting to emergency departments and clinics dedicated to patients with suspected COVID-19. The total case number was 16,270, with a median daily number of 64 (range 43-328). The total number of deaths was 103, with a median daily figure of 1.00 (range 0.00-1.25). According to the Poisson distribution analysis, it is found that the number of cases tended to increase at temperatures between 20.8 and 27.2°C. Conclusion It is predicted that the number of COVID-19 cases will not decrease with the increase in temperature in temperate regions with high rainfall. Therefore, unlike influenza, there may not be seasonal variation in the prevalence of COVID-19. The requisite measures should be adopted in health systems and hospitals to manage increases in case numbers associated with changes in meteorological factors.

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 review of strategies and their effectiveness in reducing indoor airborne transmission and improving indoor air quality

Airborne transmission arises through the inhalation of aerosol droplets exhaled by an infected person and is now thought to be the primary transmission route of COVID-19. Thus, maintaining adequate indoor air quality levels is vital in mitigating the spread of the airborne virus. The cause-and-effect flow of various agents involved in airborne transmission of viruses has been investigated through a systematic literature review. It has been identified that the airborne virus can stay infectious in the air for hours, and pollutants such as particulate matter (PM10, PM2.5), Nitrogen dioxide (NO2), Sulphur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Carbon dioxide (CO2), and Total Volatile Organic Compounds (TVOCs) and other air pollutants can enhance the incidence, spread and mortality rates of viral disease. Also, environmental quality parameters such as humidity and temperature have shown considerable influence in virus transmission in indoor spaces. The measures adopted in different research studies that can curb airborne transmission of viruses for an improved Indoor Air Quality (IAQ) have been collated for their effectiveness and limitations. A diverse set of building strategies, components, and operation techniques from the recent literature pertaining to the ongoing spread of COVID-19 disease has been systematically presented to understand the current state of techniques and building systems that can minimize the viral spread in built spaces This comprehensive review will help architects, builders, realtors, and other organizations improve or design a resilient building system to deal with COVID-19 or any such pandemic in the future.

Influence of weather factors on the incidence of COVID-19 in Spain

Introduction

Several studies have analyzed the influence of meteorological and geographical factors on the incidence of COVID-19. Seasonality could be important in the transmission of SARS-CoV-2. This study aims to evaluate the geographical pattern of COVID-19 in Spain and its relationship with different meteorological variables.

Methods

A provincial ecological study analyzing the influence of meteorological and geographical factors on the cumulative incidence of COVID-19 in the 52 (24 coastal and 28 inland) Spanish provinces during the first three waves was carried out. The cumulative incidence was calculated with data from the National Statistical Institute (INE) and the National Epidemiological Surveillance Network (RENAVE), while the meteorological variables were obtained from the Spanish Meteorological Agency (AEMET).

Results

The total cumulative incidence, in all three waves, was lower in the coastal provinces than in the inland ones (566 ± 181 vs. 782 ± 154; P = 2.5 × 10⁻⁵). The cumulative incidence correlated negatively with mean air temperature (r = −0.49; P = 2.2 × 10⁻⁴) and rainfall (r = −0.33; P = .01), and positively with altitude (r = 0.56; P = 1.4 × 10⁻⁵). The Spanish provinces with an average temperature <10 °C had almost twice the cumulative incidence than the provinces with temperatures >16 °C. The mean air temperature and rainfall were associated with the cumulative incidence of COVID-19, regardless of other factors (Beta Coefficient of −0.62; P = 3.7 × 10⁻⁷ and −0.47; P = 4.2 × 10⁻⁵ respectively)

Conclusions

Meteorological and geographical factors could influence the evolution of the pandemic in Spain. Knowledge regarding the seasonality of the virus would help to predict new waves of COVID-19 infections

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.

The role of infrastructure, socio-economic development, and food security to mitigate the loss of natural disasters

This study shows the impact of risk (hazard, exposure, and vulnerability) and resilience (infrastructure, information and communication technology, institutional quality, food security, women empowerment, economic performance, human capital, emergency workforce, and social capital) indicators on losses due to natural disasters in 24 high-income, 24 upper-middle-income, 30 lower-middle-income, and 12 low-income countries from 1995 to 2019. It develops a new disaster risk index and disaster resilience index using standard index-making procedure (indicators selection, winsorization, normalization, aggregation). The generalized additive modeling was used to explore the non-linear relationship between response and explanatory variables. There exists a positive link between damage due to natural disasters and hazard index (all panels) and exposure index in high-income countries. The decrease in damage due to natural disasters was observed due to an increase in infrastructure (upper-middle-, lower-middle-, and low-income countries), information and communication technology (high-income countries), institutional quality (high-income countries), food security (high- and upper-middle-income countries), women empowerment (lower-middle-income countries), economic performance (high- and low-income countries), human capital (low-income countries), and emergency workforce (upper-middle and lower-middle-income countries). The governments should enhance disaster resilience through Sendai Framework, having seven targets and four priority areas to increase disaster resilience.

Spatiotemporal analysis of COVID-19, air pollution, climate, and meteorological conditions in a metropolitan region of Iran

The COVID-19 pandemic has a close relationship with local environmental conditions. This study explores the effects of climate characteristics and air pollution on COVID-19 in Isfahan province, Iran. A number of COVID-19 positive cases, main air pollutants, air quality index (AQI), and climatic variables were received from March 1, 2020, to January 19, 2021. Moreover, CO, NO₂, and O₃ tropospheric levels were collected using Sentinel-5P satellite data. The spatial distribution of variables was estimated by the ordinary Kriging and inverse weighted distance (IDW) models. A generalized linear model (GLM) was used to analyze the relationship between environmental variables and COVID-19. The seasonal trend of nitrogen dioxide (NO₂), wind speed, solar energy, and rainfall like COVID-19 was upward in spring and summer. The high and low temperatures increased from April to August. All variables had a spatial autocorrelation and clustered pattern except AQI. Furthermore, COVID-19 showed a significant association with month, climate, solar energy, and NO₂. Suitable policy implications are recommended to be performed for improving people's healthcare and control of the COVID-19 pandemic. This study could survey the local spread of COVID-19, with consideration of the effect of environmental variables, and provides helpful information to health ministry decisions for mitigating harmful effects of environmental change. By means of the proposed approach, probably the COVID-19 spread can be recognized by knowing the regional climate in major cities. The present study also finds that COVID-19 may have an effect on climatic condition and air pollutants.

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.

Interventions for the Current COVID-19 Pandemic: Frontline Workers' Intention to Use Personal Protective Equipment

BACKGROUND

Frontline workers (FLWs) are at a higher risk of COVID-19 infection during care interactions than the general population. Personal protective equipment (PPE) is regarded as an effective intervention for limiting the transmission of airborne viruses. However, research examining FLWs' intention to use PPE is limited.

OBJECTIVES

This study addresses this research gap and also contributes by expanding the conceptual mechanism of planned behavior theory by incorporating three novel dimensions (perceived benefits of PPE, risk perceptions of the epidemic, and unavailability of PPE) in order to gain a better understanding of the factors that influence FLWs' intentions to use PPE.

METHOD

Analysis is based on a sample of 763 FLWs in Pakistan using a questionnaire survey, and the structural equation modeling approach is employed to evaluate the suppositions.

RESULTS

Study results indicate that attitude, perceived benefits of PPE, and risk perceptions of the epidemic have positive influence on FLWs' intention to use PPE. In comparison, the unavailability of PPE and the cost of PPE have opposite effects. Meanwhile, environmental concern has a neutral effect.

CONCLUSIONS

The study results specify the importance of publicizing COVID-19's lethal impacts on the environment and society, ensuring cheap PPE, and simultaneously enhancing workplace safety standards.

Over half of known human pathogenic diseases can be aggravated by climate change

It is relatively well accepted that climate change can affect human pathogenic diseases; however, the full extent of this risk remains poorly quantified. Here we carried out a systematic search for empirical examples about the impacts of ten climatic hazards sensitive to greenhouse gas (GHG) emissions on each known human pathogenic disease. We found that 58% (that is, 218 out of 375) of infectious diseases confronted by humanity worldwide have been at some point aggravated by climatic hazards; 16% were at times diminished. Empirical cases revealed 1,006 unique pathways in which climatic hazards, via different transmission types, led to pathogenic diseases. The human pathogenic diseases and transmission pathways aggravated by climatic hazards are too numerous for comprehensive societal adaptations, highlighting the urgent need to work at the source of the problem: reducing GHG emissions.

Influence of weather factors on the incidence of COVID-19 in Spain

INTRODUCTION

Several studies have analyzed the influence of meteorological and geographical factors on the incidence of COVID-19. Seasonality could be important in the transmission of SARS-CoV-2. This study aims to evaluate the geographical pattern of COVID-19 in Spain and its relationship with different meteorological variables.

METHODS

A provincial ecological study analyzing the influence of meteorological and geographical factors on the cumulative incidence of COVID-19 in the 52 (24 coastal and 28 inland) Spanish provinces during the first three waves was carried out. The cumulative incidence was calculated with data from the National Statistical Institute (INE) and the National Epidemiological Surveillance Network (RENAVE), while the meteorological variables were obtained from the Spanish Meteorological Agency (AEMET).

RESULTS

The total cumulative incidence, in all three waves, was lower in the coastal provinces than in the inland ones (566±181 vs. 782±154; p=2.5×10^-5). The cumulative incidence correlated negatively with mean air temperature (r=-0.49; p=2.2×10^-4) and rainfall (r=-0.33; p=0.01), and positively with altitude (r=0.56; p=1. 4×10^-5). The Spanish provinces with an average temperature <10°C had almost twice the cumulative incidence than the provinces with temperatures >16°C. The mean air temperature and rainfall were associated with the cumulative incidence of COVID-19, regardless of other factors (Beta Coefficient of -0.62; p=3.7×10^-7 and -0.47; p=4.2×10^-5 respectively).

CONCLUSIONS

Meteorological and geographical factors could influence the evolution of the pandemic in Spain. Knowledge regarding the seasonality of the virus would help to predict new waves of COVID-19 infections.

Does temperature matter for COVID-19 transmissibility? Evidence across Pakistani provinces

The outbreak of novel coronavirus (COVID-19) has become a global concern that is deteriorating environmental quality and damaging human health. Though some researchers have investigated the linkage between temperature and COVID-19 transmissibility across different geographical locations and over time, yet these studies are scarce. This study aims to bridge this gap using daily temperature and COVID-19 cases (transmissibility) by employing grey incidence analysis (GIA) models (i.e., Deng's grey incidence analysis (DGIA), the absolute degree GIA (ADGIA), the second synthetic degree GIA (SSDGIA), the conservative (maximin) model) and correlation analysis. Data on temperature are accessed from the NASA database, while the data on COVID-19 cases are collected from the official website of the government of Pakistan. Empirical results reveal the existence of linkages between temperature and COVID-19 in all Pakistani provinces. These linkages vary from a relatively stronger to a relatively weaker linkage. Based on calculated weights, the strength of linkages is ranked across provinces as follows: Gilgit Baltistan (0.715301) > Baluchistan (0.675091) > Khyber Pakhtunkhwa (0.619893) > Punjab (0.619286) > Sindh (0.601736). The disparity in the strength of linkage among provinces is explained by the discrepancy in the intensity of temperature. Besides, the diagrammatic correlation analysis shows that temperature is inversely linked to COVID-19 cases (per million persons) over time, implying that low temperatures are associated with high COVID-19 transmissibility and vice versa. This study is among the first of its kind to consider the linkages between temperature and COVID-19 transmissibility for a tropical climate country (Pakistan) using the advanced GIA models. Research findings provide an up-to-date glimpse of the outbreak and emphasize the need to raise public awareness about the devastating impacts of the COVID-19. The educational syllabus should provide information on the causes, signs, and precautions of the pandemic. Additionally, individuals should practice handwashing, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to ensure a secure and supportive atmosphere for preventing and controlling the current pandemic.

Does temperature matter for COVID-19 transmissibility? Evidence across Pakistani provinces

The outbreak of novel coronavirus (COVID-19) has become a global concern that is deteriorating environmental quality and damaging human health. Though some researchers have investigated the linkage between temperature and COVID-19 transmissibility across different geographical locations and over time, yet these studies are scarce. This study aims to bridge this gap using daily temperature and COVID-19 cases (transmissibility) by employing grey incidence analysis (GIA) models (i.e., Deng's grey incidence analysis (DGIA), the absolute degree GIA (ADGIA), the second synthetic degree GIA (SSDGIA), the conservative (maximin) model) and correlation analysis. Data on temperature are accessed from the NASA database, while the data on COVID-19 cases are collected from the official website of the government of Pakistan. Empirical results reveal the existence of linkages between temperature and COVID-19 in all Pakistani provinces. These linkages vary from a relatively stronger to a relatively weaker linkage. Based on calculated weights, the strength of linkages is ranked across provinces as follows: Gilgit Baltistan (0.715301) > Baluchistan (0.675091) > Khyber Pakhtunkhwa (0.619893) > Punjab (0.619286) > Sindh (0.601736). The disparity in the strength of linkage among provinces is explained by the discrepancy in the intensity of temperature. Besides, the diagrammatic correlation analysis shows that temperature is inversely linked to COVID-19 cases (per million persons) over time, implying that low temperatures are associated with high COVID-19 transmissibility and vice versa. This study is among the first of its kind to consider the linkages between temperature and COVID-19 transmissibility for a tropical climate country (Pakistan) using the advanced GIA models. Research findings provide an up-to-date glimpse of the outbreak and emphasize the need to raise public awareness about the devastating impacts of the COVID-19. The educational syllabus should provide information on the causes, signs, and precautions of the pandemic. Additionally, individuals should practice handwashing, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to ensure a secure and supportive atmosphere for preventing and controlling the current pandemic.

Modeling the effects of the contaminated environments on COVID-19 transmission in India

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus that caused an outbreak of typical pneumonia first in Wuhan and then globally. Although researchers focus on the human-to-human transmission of this virus but not much research is done on the dynamics of the virus in the environment and the role humans play by releasing the virus into the environment. In this paper, a novel nonlinear mathematical model of the COVID-19 epidemic is proposed and analyzed under the effects of the environmental virus on the transmission patterns. The model consists of seven population compartments with the inclusion of contaminated environments means there is a chance to get infected by the virus in the environment. We also calculated the threshold quantity R 0 to know the disease status and provide conditions that guarantee the local and global asymptotic stability of the equilibria using Volterra-type Lyapunov functions, LaSalle's invariance principle, and the Routh-Hurwitz criterion. Furthermore, the sensitivity analysis is performed for the proposed model that determines the relative importance of the disease transmission parameters. Numerical experiments are performed to illustrate the effectiveness of the obtained theoretical results.

Situation of Urban Mobility in Pakistan: Before, during, and after the COVID-19 Lockdown with Climatic Risk Perceptions

The coronavirus pandemic (COVID-19) has impacted the usual global movement patterns, atmospheric pollutants, and climatic parameters. The current study sought to assess the impact of the COVID-19 lockdown on urban mobility, atmospheric pollutants, and Pakistan’s climate. For the air pollution assessment, total column ozone (O3), sulphur dioxide (SO2), and tropospheric column nitrogen dioxide (NO2) data from the Ozone Monitoring Instrument (OMI), aerosol optical depth (AOD) data from the Multi-angle Imaging Spectroradiometer (MISR), and dust column mass density (PM2.5) data from the MERRA-2 satellite were used. Furthermore, these datasets are linked to climatic parameters (temperature, precipitation, wind speed). The Kruskal–Wallis H test (KWt) is used to compare medians among k groups (k > 2), and the Wilcoxon signed-rank sum test (WRST) is for analyzing the differences between the medians of two datasets. To make the analysis more effective, and to justify that the variations in air quality parameters are due to the COVID-19 pandemic, a Generalized Linear Model (GLM) was used. The findings revealed that the limitations on human mobility have lowered emissions, which has improved the air quality in Pakistan. The results of the study showed that the climatic parameters (precipitation, Tmax, Tmin, and Tmean) have a positive correlation and wind speed has a negative correlation with NO2 and AOD. This study found a significant decrease in air pollutants (NO2, SO2, O3, AOD) of 30–40% in Pakistan during the strict lockdown period. In this duration, the highest drop of about 28% in NO2 concentrations has been found in Karachi. Total column O3 did not show any reduction during the strict lockdown, but a minor decline was depicted as 0.38% in Lahore and 0.55% in Islamabad during the loosening lockdown. During strict lockdown, AOD was reduced up to 23% in Islamabad and 14.46% in Lahore. The results of KWt and WRST evident that all the mobility indices are significant (p < 0.05) in nature. The GLM justified that restraining human activities during the lockdown has decreased anthropogenic emissions and, as a result, improved air quality, particularly in metropolitan areas.

Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries

OBJECTIVE

To assess whether the basic reproduction number (R0) of COVID-19 is different across countries and what national-level demographic, social, and environmental factors other than interventions characterize initial vulnerability to the virus.

METHODS

We fit logistic growth curves to reported daily case numbers, up to the first epidemic peak, for 58 countries for which 16 explanatory covariates are available. This fitting has been shown to robustly estimate R0 from the specified period. We then use a generalized additive model (GAM) to discern both linear and nonlinear effects, and include 5 random effect covariates to account for potential differences in testing and reporting that can bias the estimated R0.

FINDINGS

We found that the mean R0 is 1.70 (S.D. 0.57), with a range between 1.10 (Ghana) and 3.52 (South Korea). We identified four factors-population between 20-34 years old (youth), population residing in urban agglomerates over 1 million (city), social media use to organize offline action (social media), and GINI income inequality-as having strong relationships with R0, across countries. An intermediate level of youth and GINI inequality are associated with high R0, (n-shape relationships), while high city population and high social media use are associated with high R0. Pollution, temperature, and humidity did not have strong relationships with R0 but were positive.

CONCLUSION

Countries have different characteristics that predispose them to greater intrinsic vulnerability to COVID-19. Studies that aim to measure the effectiveness of interventions across locations should account for these baseline differences in social and demographic characteristics.

Meteorological factors, COVID-19 cases, and deaths in top 10 most affected countries: an econometric investigation

This paper examines the nexus between the Covid-19 confirmed cases, deaths, meteorological factors, including an air pollutant among the world's top 10 infected countries, from 1 February 2020 through 30 June 2020, using advanced econometric techniques to address heterogeneity across the nations. The findings of the study suggest that there exists a strong cross-sectional dependence between Covid-19 cases, deaths, and all the meteorological factors for the countries under study. The findings also reveal that a long-term relationship exists between all the meteorological factors. There exists a bi-directional causality running between the Covid-19 cases and all the meteorological factors. With Covid-19 death cases as the dependent variable, there exists bi-directional causality running between the Covid-19 death cases and Covid-19 confirmed cases, air pressure, humidity, and temperature. Temperature and air pressure exhibit a statistically significant and negative impact on the Covid-19 confirmed cases. Air pollutant PM2.5 also exhibits a significant but positive impact on the Covid-19 confirmed cases. Temperature indicates a statistically significant and negative impact on the Covid-19 death cases. At the same time, Covid-19 confirmed cases and air pollutant PM2.5 exhibit a statistically significant and positive impact on the Covid-19 death cases across the ten countries under study. Hence, it is possible to postulate that cool and dry weather conditions with lower temperatures may promote indoor activities and human gatherings (assembling), leading to virus transmission. This study contributes both practically and theoretically to the concerned field of pandemic management. Our results assist in taking appropriate measures in implementing intersectoral policies and actions as necessary in a timely and efficient manner. Causal relations of Meteorological factors and Covid-19 (2 models used in the study).

Spatiotemporal variability of COVID-19 pandemic in relation to air pollution, climate and socioeconomic factors in Pakistan

Information on the spatiotemporal variability of respirable suspended particulate pollutant matter concentrations, especially of particles having size of 2.5 μm and climate are the important factors in relation to emerging COVID-19 cases around the world. This study aims at examining the association between COVID-19 cases, air pollution, climatic and socioeconomic factors using geospatial techniques in three provincial capital cities and the federal capital city of Pakistan. A series of relevant data was acquired from 3 out of 4 provinces of Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa (KPK) including the daily numbers of COVID-19 cases, PM2.5 concentration (μgm-3), a climatic factors including temperature (°F), wind speed (m/s), humidity (%), dew point (%), and pressure (Hg) from June 1 2020, to July 31 2020. Further, the possible relationships between population density and COVID-19 cases was determined. The generalized linear model (GLM) was employed to quantify the effect of PM2.5, temperature, dew point, humidity, wind speed, and pressure range on the daily COVID-19 cases. The grey relational analysis (GRA) was also implemented to examine the changes in COVID-19 cases with PM2.5 concentrations for the provincial city Lahore. About 1,92, 819 COVID-19 cases were reported in Punjab, Sindh, KPK, and Islamabad during the study period. Results indicated a significant relationship between COVID-19 cases and PM2.5 and climatic factors at p < 0.05 except for Lahore in case of humidity (r = 0.175). However, mixed correlations existed across Lahore, Karachi, Peshawar, and Islamabad. The R2 value indicates a moderate relationship between COVID-19 and population density. Findings of this study, although are preliminary, offers the first line of evidence for epidemiologists and may assist the local community to expedient for the growth of effective COVID-19 infection and health risk management guidelines. This remains to be seen.

Impact of environmental factors on COVID-19 cases and mortalities in major cities of Pakistan

Introduction

Climate factors play an important role in the transmission of viruses, such as influenza viruses, MERS-CoV, and SARS-CoV-1. This study aimed to determine the relationship between changes in temperature, humidity, rainfall, and SARS-CoV-2 contagion. Five ecologically and climatically distinct regions were considered—Karachi, Lahore, Islamabad, Peshawar, and Gilgit-Baltistan.

Method

Data on daily COVID-19 cases and deaths were retrieved from government officials, while meteorological information was collected from Pakistan Meteorological Department.. Statistical analysis was performed using SPSS version 20 and the Spearman rank correlation test was used to analyze the correlation between the meteorological factors and COVID-19 cases and deaths.

Result

Positive correlation of COVID-19 incidence was observed with all the temperature ranges (maximum, minimum and average) and negative correlation was seen with humidity, DTR and rainfall. COVID-19 deaths were positively associated with temperature and were negatively associated only with humidity. Linear regression showed that for every unit increase in humidity, there was a −3.345 daily significant decrease in COVID-19 cases, while in Karachi for every unit increase in humidity, there remained a 10.104 daily significant increase in cases. In Gilgit-Baltistan, for every unit increase in average temperature and rainfall respectively, significant increases of 0.534 and 1.286 in daily cases were found.

Conclusion

This study signifies the effect of climate factors on COVID-19 incidence and mortality rate, but climate factors are not the only variable and several other interlinked factors enhance the spread of COVID-19. Hence, effective mitigation policies, enhancing testing capacities, and developing public attitudes toward adopting precautionary measures are important to overcome this overwhelming pandemic.

Impact of wind speed and air pollution on COVID-19 transmission in Pakistan

This study investigated the effect of wind speed and air pollution on COVID-19 from March 10, 2020, to October 04, 2020, in Pakistan. Wind speed and COVID-19 had positive correlation in Pakistan and its provinces. The inverted U-shaped dose-response curve was found for wind speed and COVID-19 in Punjab. Initially, the dose-response curve showed a positive link between wind speed and COVID-19 in Pakistan, Sindh, Khyber Pakhtunkhwa, and Islamabad Capital Territory. Later, it becomes downward sloped in Sindh, Khyber Pakhtunkhwa, and Islamabad Capital Territory. The expected log count of COVID-19 was increased by 0.113 times (Pakistan), 0.074 times (Punjab), 0.042 times (Sindh), and 0.082 times (Khyber Pakhtunkhwa) for a 1 km/h increase in the wind speed. The correlation between particulate matter and COVID-19 was positive (Pakistan, Punjab, and Islamabad Capital Territory) and negative (Sindh). The dose-response curve for particulate matter and COVID-19 had inverted U-shaped (Pakistan, Punjab, and Khyber Pakhtunkhwa) positively sloped (Islamabad Capital Territory), and negatively sloped (Sindh). The inverted U-shaped association shows that the COVID-19 initially increased due to a rise in the particulate matter but reduced when the particulate matter was above the threshold level. The particulate matter was also responsible to wear face masks and restricted mobility. The expected log count of COVID-19 cases was reduced by 0.005 times in Sindh for 1 unit increase in particulate matter. It is recommended to reduce particulate matter to control respiratory problems. The government should use media (print, electronic, social) and educational syllabus to create awareness about precautionary measures.