The sensitivity and specificity analyses of ambient temperature and population size on the transmission rate of the novel coronavirus (COVID-19) in different provinces of Iran

Multi-feature SEIR model for epidemic analysis and vaccine prioritization

The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.

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.

Predictors of receiving COVID-19 vaccine among adult population in Iran: an observational study

BACKGROUND

Vaccination is one of the best ways to stop the transmission of coronavirus disease 2019 (COVID-19). In this regard, uunderstanding the features related to the intention of different populations to receive the COVID-19 vaccine is essential for an effective vaccination program. This study aimed to investigate the vaccination intention predictors in the general adult population of Iran.

METHODS

A cross-sectional, web-based survey was conducted on social networks, including Telegram, WhatsApp, LinkedIn, Instagram, and Facebook. Multinomial logistic regression models were used to investigate predictors associated with the intention to receive COVID-19 vaccines, including sociodemographic characteristics, trust, worry, sources of information, and conspiracy beliefs. The main outcomes included unwillingness, undecidedness, and intention to receive the COVID-19 vaccine.

RESULTS

Out of 780 respondents, 481 (61.6%) reported an intention to be vaccinated, 214 (27.4%) expressed their undecided status, and 85 (10.9%) reported unwillingness to receive any type of COVID-19 vaccine. A higher age (OR undecided = 0.97, 95% CI (0.96-0.99)), (OR unwilling = 0.97, 95% CI (0.95-0.99)); exposure with COVID-19 (OR unwilling = 0.82, 95% CI (0.76-0.89)), (OR undecided = 0.87, 95% CI (0.83-0.93)) were positively associated with vaccination intentions. No/low trust in vaccines, institutions, concerns about the future of the pandemic, and conspiracy beliefs were strongly and negatively associated with COVID-19 vaccination intentions.

CONCLUSION

Most Iranians intended to get a COVID-19 vaccine. Higher vaccine acceptance needs to consider demographic features, exposure history, confidence in vaccines, trust in institutions, concerns, and conspiracy beliefs of people.

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.

The dynamics of early-stage transmission of COVID-19: A novel quantification of the role of global temperature

The global outbreak of COVID-19 has emerged as one of the most devastating and challenging threats to humanity. As many frontline workers are fighting against this disease, researchers are struggling to obtain a better understanding of the pathways and challenges of this pandemic. This paper evaluates the concept that the transmission of COVID-19 is intrinsically linked to temperature. Some complex nonlinear functional forms, such as the cubic function, are introduced to the empirical models to understand the interaction between temperature and the "growth" in the number of infected cases. An accurate quantitative interaction between temperature and the confirmed COVID-19 cases is obtained as log(Y) = -0.000146(temp_H)3 + 0.007410(temp_H)2 -0.063332 temp_H + 7.793842, where Y is the periodic growth in confirmed COVID-19 cases, and temp_H is the maximum daily temperature. This equation alone may be the first confirmed way to measure the quantitative interaction between temperature and human transmission of COVID-19. In addition, four important regions are identified in terms of maximum daily temperature (in Celsius) to understand the dynamics in the transmission of COVID-19 related to temperature. First, the transmission decreases within the range of -50 °C to 5.02 °C. Second, the transmission accelerates in the range of 5.02 °C to 16.92 °C. Essentially, this is the temperature range for an outbreak. Third, the transmission increases more slowly in the range of 16.92 °C to 28.82 °C. Within this range, the number of infections continues to grow, but at a slower pace. Finally, the transmission decreases in the range of 28.82 °C to 50 °C. Thus, according to this hypothesis, the threshold of 16.92 °C is the most critical, as the point at which the infection rate is the greatest. This result sheds light on the mechanism in the cyclicity of the ongoing COVID-19 pandemic worldwide. The implications of these results on policy issues are also discussed concerning a possible cyclical fluctuation pattern between the Northern and Southern Hemispheres.

An overview and thematic analysis of research on cities and the COVID-19 pandemic: Toward just, resilient, and sustainable urban planning and design

Since early 2020, researchers have made efforts to study various issues related to cities and the pandemic. Despite the wealth of research on this topic, there are only a few review articles that explore multiple issues related to it. This is partly because of the rapid pace of publications that makes systematic literature review challenging. To address this issue, in the present study, we rely on bibliometric analysis techniques to gain an overview of the knowledge structure and map key themes and trends of research on cities and the pandemic. Results of the analysis of 2,799 articles show that research mainly focuses on six broad themes: air quality, meteorological factors, built environment factors, transportation, socio-economic disparities, and smart cities, with the first three being dominant. Based on the findings, we discuss major lessons that can be learned from the pandemic and highlight key areas that need further research.

A Review of Challenges and Approaches to Effective Medical Solid Waste Management During the COVID-19 Pandemic in India

With the emergence of the Covid-19 pandemic, medical solid waste management became a crucial element to control the spread of the virus. Lack of manpower, infrastructure, and knowledge have jeopardised the waste management system in India. In this respect, the present study intends to recollect and discusses the policies and guidelines of solid waste management by outlining the challenges associated with its implementation. The study adopted a review approach, where a collective appraisal and analysis of prior research, reports, lead to the evaluation of the present situation and advocated remedial measures. The study discussed measures recommended by various international organisations for effective medical waste management to deal with the present situation, as well as to eliminate and confront similar challenges in the event of future probable epidemics. Moreover, the study is a guide to the policymakers, regulatory authorities and the community for efficient medical waste management during and post-pandemic days.

Did unprecedented air pollution levels cause spike in Delhi's COVID cases during second wave?

The onset of the second wave of COVID-19 devastated many countries worldwide. Compared with the first wave, the second wave was more aggressive regarding infections and deaths. Numerous studies were conducted on the association of air pollutants and meteorological parameters during the first wave of COVID-19. However, little is known about their associations during the severe second wave of COVID-19. The present study is based on the air quality in Delhi during the second wave. Pollutant concentrations decreased during the lockdown period compared to pre-lockdown period (PM2.5: 67 µg m-3 (lockdown) versus 81 µg m-3 (pre-lockdown); PM10: 171 µg m-3 versus 235 µg m-3; CO: 0.9 mg m-3 versus 1.1 mg m-3) except ozone which increased during the lockdown period (57 µg m-3 versus 39 µg m-3). The variation in pollutant concentrations revealed that PM2.5, PM10 and CO were higher during the pre-COVID-19 period, followed by the second wave lockdown and the lowest in the first wave lockdown. These variations are corroborated by the spatiotemporal variability of the pollutants mapped using ArcGIS. During the lockdown period, the pollutants and meteorological variables explained 85% and 52% variability in COVID-19 confirmed cases and deaths (determined by General Linear Model). The results suggests that air pollution combined with meteorology acted as a driving force for the phenomenal growth of COVID-19 during the second wave. In addition to developing new drugs and vaccines, governments should focus on prediction models to better understand the effect of air pollution levels on COVID-19 cases. Policy and decision-makers can use the results from this study to implement the necessary guidelines for reducing air pollution. Also, the information presented here can help the public make informed decisions to improve the environment and human health significantly.

Impact of COVID-19 on electricity energy consumption: A quantitative analysis on electricity

In addition to the tremendous loss of life due to coronavirus disease 2019 (COVID-19), the pandemic created challenges for the energy system, as strict confinement measures such as lockdown and social distancing compelled by governments worldwide resulted in a significant reduction in energy demand. In this study, a novel, quantitative and uncomplex method for estimating the energy consumption loss due to the pandemic, which was derived from epidemiological data in the beginning stages, is provided; the method bonds a data-driven prediction (LSTM network) of energy consumption due to COVID-19 to an econometric model (ARDL) so that the long- and short-term impact can be synthesized with adequate statistical validation. The results show that energy loss is statistically correlated with the time-changing effective reproductive number (R_t) of the disease, which can be viewed as quantifying confinement intensity and the severity of the earlier stages of the pandemic. We detected a 1.62% decrease in electricity consumption loss caused by each percent decrease in R_t on average. We verify our method by applying it to Germany and 5 U.S. states with various social features and discuss implications and universality. Our results bridge the knowledge gap between key energy and epidemiological parameters and provide policymakers with a more precise estimate of the pandemic’s impact on electricity demand so that strategies can be formulated to minimize losses caused by similar crises.

Weather Conditions and COVID-19 Cases: Insights from the GCC Countries

The prediction of new COVID-19 cases is crucial for decision makers in many countries. Researchers are continually proposing new models to forecast the future tendencies of this pandemic, among which long short-term memory (LSTM) artificial neural networks have exhibited relative superiority compared to other forecasting techniques. Moreover, the correlation between the spread of COVID-19 and exogenous factors, specifically weather features, has been explored to improve forecasting models. However, contradictory results have been reported regarding the incorporation of weather features into COVID-19 forecasting models. Therefore, this study compares uni-variate with bi- and multi-variate LSTM forecasting models for predicting COVID-19 cases, among which the latter models consider weather features. LSTM models were used to forecast COVID-19 cases in the six Gulf Cooperation Council countries. The root mean square error (RMSE) and coefficient of determination (R2) were employed to measure the accuracy of the LSTM forecasting models. Despite similar weather conditions, the weather features that exhibited the strongest correlation with COVID-19 cases differed among the six countries. Moreover, according to the statistical comparisons that were conducted, the improvements gained by including weather features were insignificant in terms of the RMSE values and marginally significant in terms of the R2 values. Consequently, it is concluded that the uni-variate LSTM models were as good as the best bi- and multi-variate LSTM models; therefore, weather features need not be included. Furthermore, we could not identify a single weather feature that can consistently improve the forecasting accuracy.

Impact of climate on COVID-19 transmission: A study over Indian states

Coronavirus Disease-2019 (COVID-19) started in Wuhan province of China in November 2019 and within a short time, it was declared as a worldwide pandemic by World Health Organisation due to the very fast worldwide spread of the virus. There are a few studies that look for the correlation with infected individuals and different environmental parameters using early data of COVID-19 but there is no study so far that deals with the variation of effective reproduction number and environmental factors. Effective reproduction number is the driving parameter of the spread of a pandemic and it is important to study the effect of various environmental factors on effective reproduction number to understand the effect of those factors on the spread of the virus. We have used time-dependent models to investigate the variation of different time-dependent driving parameters of COVID-19 like effective reproduction number and contact rate using data from India as a test case. India is a large population country that is highly affected due to the COVID-19 pandemic and has a wide span of different temperature and humidity regions and is ideal for such study. We have studied the impact of temperature and humidity on the spread of the virus of different Indian states using time-dependent epidemiological models SIRD, and SEIRD for a long time scale. We have used a linear regression method to look for any dependency between the effective reproduction number with the relative humidity, absolute humidity, and temperature. The effective reproduction number shows a negative correlation with both relative and absolute humidity for most of the Indian states, which are statistically significant. This implies that relative and absolute humidity may have an important role in the variation of effective reproduction number. Most of the states (six out of ten) show a positive correlation while two (out of ten) show a negative correlation between effective reproduction number and average air temperature for both SIRD and SEIRD models.

Effects of Meteorological Factors and Air Pollutants on COVID-19 Transmission under the Action of Control Measures

At present, COVID-19 is still spreading, and its transmission patterns and the main factors that affect transmission behavior still need to be thoroughly explored. To this end, this study collected the cumulative confirmed cases of COVID-19 in China by 8 April 2020. Firstly, the spatial characteristics of the COVID-19 transmission were investigated by the spatial autocorrelation method. Then, the factors affecting the COVID-19 incidence rates were analyzed by the generalized linear mixed effect model (GLMMs) and geographically weighted regression model (GWR). Finally, the geological detector (GeoDetector) was introduced to explore the influence of interactive effects between factors on the COVID-19 incidence rates. The results showed that: (1) COVID-19 had obvious spatial aggregation. (2) The control measures had the largest impact on the COVID-19 incidence rates, which can explain the difference of 34.2% in the COVID-19 incidence rates, while meteorological factors and pollutant factors can only explain the difference of 1% in the COVID-19 incidence rates. It explains that some of the literature overestimates the impact of meteorological factors on the spread of the epidemic. (3) The influence of meteorological factors was stronger than that of air pollution factors, and the interactive effects between factors were stronger than their individual effects. The interaction between relative humidity and NO2 was stronger. The results of this study will provide a reference for further prevention and control of COVID-19.

Transmission Dynamics of COVID-19 in Ghana and the Impact of Public Health Interventions

This study characterized COVID-19 transmission in Ghana in 2020 and 2021 by estimating the time-varying reproduction number (Rt) and exploring its association with various public health interventions at the national and regional levels. Ghana experienced four pandemic waves, with epidemic peaks in July 2020 and January, August, and December 2021. The epidemic peak was the highest nationwide in December 2021 with Rt ≥ 2. Throughout 2020 and 2021, per-capita cumulative case count by region increased with population size. Mobility data suggested a negative correlation between Rt and staying home during the first 90 days of the pandemic. The relaxation of movement restrictions and religious gatherings was not associated with increased Rt in the regions with fewer case burdens. Rt decreased from > 1 when schools reopened in January 2021 to < 1 after vaccination rollout in March 2021. Findings indicated most public health interventions were associated with Rt reduction at the national and regional levels.

Rethinking Outdoor Courtyard Spaces on University Campuses to Enhance Health and Wellbeing: The Anti-Virus Built Environment

Responding to the events surrounding the COVID-19 pandemic, this study explores how to improve health and wellbeing and reduce infections in outdoor open spaces on university campuses to maximize their potential as a response to future crises. The study identifies the relationship between human behavior (social) and the various physical and environmental elements of these spaces. A case study and mixed-methods approach were undertaken, comprising four modes of inspection: user analysis layer using questionnaires and observations to survey students’ needs and behavior; context analysis layer using space syntax and CFD to examine the space’s physical and environmental conditions; design solutions reflecting an understanding of virus transmission; and a performance analysis layer to test the performance of ‘anti-virus’ courtyards. The findings demonstrated that students are willing to use the open spaces that they used before the pandemic, at the same frequency. This indicates a need to redesign the current spaces to prevent the spread of viruses. The study highlights the social, physical, and environmental implications to be considered in designs for outdoor anti-virus spaces. It provides a comprehensive process for transforming outdoor spaces on university campuses into anti-virus spaces that meet users’ needs. These findings have implications for the designing and retrofitting of open spaces to reduce infection.

Geographical and Meteorological Evaluations of COVID-19 Spread in Iran

Since late 2019 many people all over the world have become infected and have died due to coronavirus. There have been many general studies about the spread of the virus. In this study, new and accumulated confirmed cases (NCC and ACC), new and accumulated recovered cases (NRC and ARC), and new and accumulated deaths (ND and AD) were evaluated by geographical properties, meteorological parameters and air particulate matters between 3 April 2020 and 11 June 2020 within 15 provinces in Iran. Meteorological parameters, air particulate matters and COVID-19 data were collected from Iran Meteorological Organization, the Environmental Protection Agency and Aftabnews website, respectively. The results of the study show that provinces in dry lands (i.e., Kerman and South Khorasan) not only had low admission of NCC, ACC, ARC and AD but also presented lower rates of NCC, ACC and AD per 105 population. Air temperature showed positive and significant correlation with the number of COVID-19 cases. This is because of hot outdoor air especially in costal and equatorial regions that forces people to stay in closed environments with no ventilation and with closed-cycle air conditioners. Maximum air pressure was found to be the most frequent (66%) and significant parameter correlating with health outcomes associated with COVID-19. The most engaged province in this study was Khuzestan, while provinces in dry lands (i.e., Kerman and South Khorasan) showed low number of health endpoints associated with COVID-19. The highest rate of accumulated and new recovered cases per 105 population were also found in Khuzestan and Kerman provinces. North Khorasan also showed the worst rate of N&ARC/105 population. Therefore, air temperature, dry lands and population were the most important factors for the control of coronavirus spread.

The influence of weather conditions on the COVID-19 epidemic: Evidence from 279 prefecture-level panel data in China

Studying the influence of weather conditions on the COVID-19 epidemic is an emerging field. However, existing studies in this area tend to utilize time-series data, which have certain limitations and fail to consider individual, social, and economic factors. Therefore, this study aimed to fill this gap. In this paper, we explored the influence of weather conditions on the COVID-19 epidemic using COVID-19-related prefecture-daily panel data collected in mainland China between January 1, 2020, and February 19, 2020. A two-way fixed effect model was applied taking into account factors including public health measures, effective distance to Wuhan, population density, economic development level, health, and medical conditions. We also used a piecewise linear regression to determine the relationship in detail. We found that there is a conditional negative relationship between weather conditions and the epidemic. Each 1 °C rise in mean temperature led to a 0.49% increase in the confirmed cases growth rate when mean temperature was above -7 °C. Similarly, when the relative humidity was greater than 46%, it was negatively correlated with the epidemic, where a 1% increase in relative humidity decreased the rate of confirmed cases by 0.19%. Furthermore, prefecture-level administrative regions, such as Chifeng (included as "warning cities") have more days of "dangerous weather", which is favorable for outbreaks. In addition, we found that the impact of mean temperature is greatest in the east, the influence of relative humidity is most pronounced in the central region, and the significance of weather conditions is more important in the coastal region. Finally, we found that rising diurnal temperatures decreased the negative impact of weather conditions on the spread of COVID-19. We also observed that strict public health measures and high social concern can mitigate the adverse effects of cold and dry weather on the spread of the epidemic. To the best of our knowledge, this is the first study which applies the two-way fixed effect model to investigate the influence of weather conditions on the COVID-19 epidemic, takes into account socio-economic factors and draws new conclusions.

Global COVID-19 pandemic trends and their relationship with meteorological variables, air pollutants and socioeconomic aspects

Meteorological variables, air pollutants, and socioeconomic factors are associated with COVID-19 transmission. However, it is unclear what impact their interactions have on COVID-19 transmission, whether their impact on COVID-19 transmission is linear or non-linear, and where the inflexion points are. This study examined 1) the spatial and temporal trends in COVID-19 monthly infection rate of new confirmed cases per 100,000 people (R_n) in 188 countries/regions worldwide from March to November 2020; 2) the linear correlation between meteorological variables (temperature (T), rainfall (R), wind speed (WS), relative humidity (RH), air pressure (AP)), air pollutants (nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), ozone (O₃)) and socioeconomic aspects (population density (PD), gross domestic product per capita (GDP), domestic general government health expenditure per capita (GHE)) and R_n, and 3) the interaction and non-linear effects of the different variables on R_n, based on GeoDetector and Boosted regression tree. The results showed that the global R_n had was spatially clustered, and the average R_n increased From March to November 2020. Global R_n was negatively correlated with meteorological variables (T, R, WS, AP) and positively correlated with air pollutants (NO₂, SO₂, O₃) and socioeconomic aspects (GDP, GHE). The interaction of SO₂ and O₃, SO₂ and RH, and O₃ and T strongly affected R_n. The variables effect on COVID-19 transmission was non-linear, with one or more inflexion points. The findings of this work can provide a basis for developing a global response to COVID-19 for global sustainable development.

Association between temperature and COVID-19 transmission in 153 countries

The WHO characterized coronavirus disease 2019 (COVID-19) as a global pandemic. The influence of temperature on COVID-19 remains unclear. The objective of this study was to investigate the correlation between temperature and daily newly confirmed COVID-19 cases by different climate regions and temperature levels worldwide. Daily data on average temperature (AT), maximum temperature (MAXT), minimum temperature (MINT), and new COVID-19 cases were collected from 153 countries and 31 provinces of mainland China. We used the spline function method to preliminarily explore the relationship between R₀ and temperature. The generalized additive model (GAM) was used to analyze the association between temperature and daily new cases of COVID-19, and a random effects meta-analysis was conducted to calculate the pooled results in different regions in the second stage. Our findings revealed that temperature was positively related to daily new cases at low temperature but negatively related to daily new cases at high temperature. When the temperature was below the smoothing plot peak, in the temperate zone or at a low temperature level (e.g., <25th percentiles), the RRs were 1.09 (95% CI: 1.04, 1.15), 1.10 (95% CI: 1.05, 1.15), and 1.14 (95% CI: 1.06, 1.23) associated with a 1°C increase in AT, respectively. Whereas temperature was above the smoothing plot peak, in a tropical zone or at a high temperature level (e.g., >75th percentiles), the RRs were 0.79 (95% CI: 0.68, 0.93), 0.60 (95% CI: 0.43, 0.83), and 0.48 (95% CI: 0.28, 0.81) associated with a 1°C increase in AT, respectively. The results were confirmed to be similar regarding MINT, MAXT, and sensitivity analysis. These findings provide preliminary evidence for the prevention and control of COVID-19 in different regions and temperature levels.

A hierarchical study for urban statistical indicators on the prevalence of COVID-19 in Chinese city clusters based on multiple linear regression (MLR) and polynomial best subset regression (PBSR) analysis

With evidence-based measures, COVID-19 can be effectively controlled by advanced data analysis and prediction. However, while valuable insights are available, there is a shortage of robust and rigorous research on what factors shape COVID-19 transmissions at the city cluster level. Therefore, to bridge the research gap, we adopted a data-driven hierarchical modeling approach to identify the most influential factors in shaping COVID-19 transmissions across different Chinese cities and clusters. The data used in this study are from Chinese officials, and hierarchical modeling conclusions drawn from the analysis are systematic, multifaceted, and comprehensive. To further improve research rigor, the study utilizes SPSS, Python and RStudio to conduct multiple linear regression and polynomial best subset regression (PBSR) analysis for the hierarchical modeling. The regression model utilizes the magnitude of various relative factors in nine Chinese city clusters, including 45 cities at a different level of clusters, to examine these aspects from the city cluster scale, exploring the correlation between various factors of the cities. These initial 12 factors are comprised of ‘Urban population ratio’, ‘Retail sales of consumer goods’, ‘Number of tourists’, ‘Tourism Income’, ‘Ratio of the elderly population (> 60 year old) in this city’, ‘population density’, ‘Mobility scale (move in/inbound) during the spring festival’, ‘Ratio of Population and Health facilities’, ‘Jobless rate (%)’, ‘The straight-line distance from original epicenter Wuhan to this city’, ‘urban per capita GDP’, and ‘the prevalence of the COVID-19’. The study’s results provide rigorously-tested and evidence-based insights on most instrumental factors that shape COVID-19 transmissions across cities and regions in China. Overall, the study findings found that per capita GDP and population mobility rates were the most affected factors in the prevalence of COVID-19 in a city, which could inform health experts and government officials to design and develop evidence-based and effective public health policies that could curb the spread of the COVID-19 pandemic.

A Theoretical Model to Investigate the Influence of Temperature, Reactions of the Population and the Government on the COVID-19 Outbreak in Turkey

OBJECTIVES

The ongoing coronavirus disease 2019 (COVID-19) pandemic, which was initially identified in December 2019 in the city of Wuhan in China, poses a major threat to worldwide health care. By August 04, 2020, there were globally 695,848 deaths (Johns Hopkins University, https://coronavirus.jhu.edu/map.html). A total of 5765 of them come from Turkey (Johns Hopkins University, https://coronavirus.jhu.edu/map.html). As a result, various governments and their respective populations have taken strong measures to control the spread of the pandemic. In this study, a model that is by construction able to describe both government actions and individual reactions in addition to the well-known exponential spread is presented. Moreover, the influence of the weather is included. This approach demonstrates a quantitative method to track these dynamic influences. This makes it possible to numerically estimate the influence that various private or state measures that were put into effect to contain the pandemic had at time t. This might serve governments across the world by allowing them to plan their actions based on quantitative data to minimize the social and economic consequences of their containment strategies.

METHODS

A compartmental model based on SEIR that includes the risk perception of the population by an additional differential equation and uses an implicit time-dependent transmission rate is constructed. Within this model, the transmission rate depends on temperature, population, and government actions, which in turn depend on time. The model was tested using different scenarios, with the different dynamic influences being mathematically switched on and off. In addition, the real data of infected coronavirus cases in Turkey were compared with the results of the model.

RESULTS

The mathematical study of the influence of the different parameters is presented through different scenarios. Remarkably, the last scenario is also an example of a theoretical mitigation strategy that shows its maximum in August 2020. In addition, the results of the model are compared with the real data from Turkey using conventional fitting that shows good agreement.

CONCLUSIONS

Although most countries activated their pandemic plans, significant disruptions in health-care systems occurred. The framework of this model seems to be valid for a numerical analysis of dynamic processes that occur during the COVID-19 outbreak due to weather and human reactions. As a result, the effects of the measures introduced could be better planned in advance by use of this model.

Experience of adherence to treatment among patients with coronary artery disease during the COVID-19 pandemic: A qualitative study

Background: The coronavirus disease 2019 (COVID-19) pandemic has caused patients with chronic diseases to face various challenges. The present qualitative study aimed to explore adherence to treatment in patients with coronary artery disease (CAD) during the COVID-19 pandemic. Methods: This qualitative content analysis was conducted from September 2020 to February 2021. Online in-depth interviews were conducted with 15 patients with CAD after discharge from Nemazi and Al-Zahra heart hospitals, Shiraz, Iran. Data management was done via MAXQDA 12 software using conventional content analysis based on the method proposed by Graneheim and Lundman. Results: The results revealed three main categories, nine subcategories, and 431 primary codes. The first category was 'improved self-care in the shadow of COVID-19' (Improving self-care due to fear of COVID-19, 'utilization of alternative strategies, and reinforcement of self-care beliefs). The second category was 'redefinition of support systems' (need for a support system, seeking for alternative support systems, and changes in social interactions). The last category was 'barriers to treatment adherence' (shortage of financial resources, need to adjust with working conditions, and mental conflicts). Conclusion: The results indicated that the COVID-19 threats encouraged the patients with CAD to adhere to their care principles. Nonetheless, the restrictions resulting from the pandemic caused problems in adherence to treatment. Thus, redefinition of the support systems in accordance with the present conditions are recommended.

Spatio-temporal estimation of basic and effective reproduction number of COVID-19 and post-lockdown transmissibility in Bangladesh

The ongoing COVID-19 pandemic has caused unprecedented public health concern in Bangladesh. This study investigated the role of Non-Pharmaceutical Interventions on COVID-19 transmission and post-lockdown scenarios of 64 administrative districts and the country as a whole based on the spatiotemporal variations of effective reproduction number (R t) of COVID-19 incidences. The daily confirmed COVID-19 data of Bangladesh and its administrative districts from March 8, 2020, to March 10, 2021, were used to estimate R t. This study finds that the maximum value of R t reached 4.15 (3.43, 4.97, 95% CI) in late March 2020, which remained above 1 afterwards in most of the districts. Containment measures are moderately effective in reducing transmission by 24.03%. The R t was established below 1 from early December 2020 for overall Bangladesh and a gradual increase of R t above 1 has been seen from early February 2021. The basic reproduction number (R 0) in Bangladesh probably varied around 2.02 (1.33–3.28, 95% CI). This study finds a significant positive correlation (r = 0.75) between population density and COVID-19 incidence and explaining 56% variation in Bangladesh. The findings of this study are expected to support the policymakers to adopt appropriate measures for curbing the COVID-19 transmission effectively.

The Effects of Climate and Bioclimate on COVID-19 Cases in Poland

The correlations between air temperatures, relative and absolute humidity, wind, cloudiness, precipitation and number of influenza cases have been extensively studied in the past. Because, initially, COVID-19 cases were similar to influenza cases, researchers were prompted to look for similar relationships. The aim of the study is to identify the effects of changes in air temperature on the number of COVID-19 infections in Poland. The hypothesis under consideration concerns an increase in the number of COVID-19 cases as temperature decreases. The spatial heterogeneity of the relationship under study during the first year and a half of the COVID-19 pandemic in Polish counties is thus revealed.

Disparities in case frequency and mortality of coronavirus disease 2019 (COVID-19) among various states in the United States

OBJECTIVE

To utilize publicly reported, state-level data to identify factors associated with the frequency of cases, tests, and mortality in the USA.

MATERIALS AND METHODS

Retrospective study using publicly reported data collected included the number of COVID-19 cases, tests and mortality from March 14th through April 30th. Publicly available state-level data was collected which included: demographics comorbidities, state characteristics and environmental factors. Univariate and multivariate regression analyses were performed to identify the significantly associated factors with percent mortality, case and testing frequency. All analyses were state-level analyses and not patient-level analyses.

RESULTS

A total of 1,090,500 COVID-19 cases were reported during the study period. The calculated case and testing frequency were 3332 and 19,193 per 1,000,000 patients. There were 63,642 deaths during this period which resulted in a mortality of 5.8%. Factors including to but not limited to population density (beta coefficient 7.5, p < .01), transportation volume (beta coefficient 0.1, p < .01), tourism index (beta coefficient -0.1, p = .02) and older age (beta coefficient 0.2, p = .01) are associated with case frequency and percent mortality.

CONCLUSIONS

There were wide variations in testing and case frequencies of COVID-19 among different states in the US. States with higher population density had a higher case and testing rate. States with larger population of elderly and higher tourism had a higher mortality. Key messages There were wide variations in testing and case frequencies of COVID-19 among different states in the USA. States with higher population density had a higher case and testing rate. States with larger population of elderly and higher tourism had a higher mortality.

COVID-19 in Africa: rethinking the tools to manage future pandemics

Corona virus disease 2019 (COVID-19) remains an incurable, progressive pneumonia-like illness characterized by fever, dry cough, fatigue, and headache during its early stages. COVID-19 has ultimately resulted in mortality in at least 2 million people worldwide. Millions of people globally have already been affected by this disease, and the numbers are expected to increase, perhaps until an effective cure or vaccine is identified. Although Africa was initially purported by the World Health Organization (WHO) to be severely hit by the pandemic, Africa recorded the least number of cases during the first wave, with lowest rates of infections, compared to Asia, Europe, and the Americas. This statistic might be attributed to the low testing capacity, existing public health awareness and lessons learnt during Ebola epidemic. Nonetheless, the relatively low rate of infection should be an opportunity for Africa to be better prepared to overcome this and future epidemics. In this paper, the authors provide insights into the dynamics and transmission of the severe acute respiratory syndrome corona virus (SARS-CoV-2) during the first wave of the pandemic; possible explanations into the relatively low rates of infection recorded in Africa; with recommendations for Africa to continue to fight Covid-19; and position itself to effectively manage future pandemics.

Influences of climatic and non-climatic factors on COVID-19 outbreak: A review of existing literature

Coronavirus disease 2019 (COVID-19) has become a significant global public health issue resulting from SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). COVID-19 outbreak approaches an unprecedented challenge for human health, the economy, and societies. The transmission of the COVID-19 is influenced by many factors, including climatic, environmental, socioeconomic, and demographic. This study aimed to investigate the influences of climatic and sociodemographic determinants on COVID-19 transmission. The climatic variables considered herein were air temperature, relative humidity, wind speed, air pollution, and cumulative precipitation. Sociodemographic variables included population density, socioeconomic conditions, misinformation, and personal hygiene practices towards the pandemic. Review results indicated that lower temperatures and greater incidence of COVID-19 are reported in a more significant number of studies. Another factor linked to COVID-19 occurrence was the humidity. However, the results were varied; some research reported positive, and others reported negative relationships. In addition, poor air quality, along with strong winds, makes the virus more vulnerable to spreading, leading to a spike in COVID-19 cases. PM2.5, O3, and NO2 also showed a strong correlation with the recent epidemic. The findings on rainfall were inconsistent between studies. Among the non-climatic factors, population density, education, and income were credited as potential determinants for the coronavirus outbreak. Climatic and sociodemographic factors showed a significant correlation on the COVID-19 outbreak. Thus, our review emphasizes the critical importance of considering climatic and non-climatic factors while developing intervention measures. This study's core findings will support the decision-makers in identifying climatic and socioeconomic elements that influence the risks of future pandemics.

"What should be computed" for supporting post-pandemic recovery policymaking? A life-oriented perspective

The COVID-19 pandemic has caused various impacts on people's lives, while changes in people's lives have shown mixed effects on mitigating the spread of the SARS-CoV-2 virus. Understanding how to capture such two-way interactions is crucial, not only to control the pandemic but also to support post-pandemic urban recovery policies. As suggested by the life-oriented approach, the above interactions exist with respect to a variety of life domains, which form a complex behavior system. Through a review of the literature, this paper first points out inconsistent evidence about behavioral factors affecting the spread of COVID-19, and then argues that existing studies on the impacts of COVID-19 on people's lives have ignored behavioral co-changes in multiple life domains. Furthermore, selected uncertain trends of people's lives for the post-pandemic recovery are described. Finally, this paper concludes with a summary about "what should be computed?" in Computational Urban Science with respect to how to catch up with delays in the SDGs caused by the COVID-19 pandemic, how to address digital divides and dilemmas of e-society, how to capture behavioral co-changes during the post-pandemic recovery process, and how to better manage post-pandemic recovery policymaking processes.

The impact of weather on COVID-19 pandemic

Rising temperature levels during spring and summer are often argued to enable lifting of strict containment measures even in the absence of herd immunity. Despite broad scholarly interest in the relationship between weather and coronavirus spread, previous studies come to very mixed results. To contribute to this puzzle, the paper examines the impact of weather on the COVID-19 pandemic using a unique granular dataset of over 1.2 million daily observations covering over 3700 counties in nine countries for all seasons of 2020. Our results show that temperature and wind speed have a robust negative effect on virus spread after controlling for a range of potential confounding factors. These effects, however, are substantially larger during mealtimes, as well as in periods of high mobility and low containment, suggesting an important role for social behaviour.

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.

Role of urban planning characteristics in forming pandemic resilient cities - Case study of Covid-19 impacts on European cities within England, Germany and Italy

In recent decades, the world has witnessed a variety of emerging infectious diseases, some of which developed to pandemic world threatening outbreaks, the ongoing COVID-19 is known to be taking the lead in claiming lives around the globe and thus, urging people to trail its increasing figures. Therefore, this research aims to emphasize the role of urban planning in containing such outbreaks through running a series of analytical and statistical studies on European cities, worst inflicted region, to analyze the main urban features they share and that may be propagating the disease spread according to their population size, density, form, intracity connectivity and intercity connectivity. This study, as far as we know of, is the first practice to evaluate both the individual and combined impacts of these factors on recorded rates of infections. According to the context of this research, it is concluded that the diversity found in urban features are, to a large degree, related to cities being more vulnerable than others. Intracity connectivity through public transport is found to be the possible prime factor of this study, and is followed by population size, density, and intercity connectivity. Urban morphology seems to also contribute to such outbreak, with both radial and grid cities being associated to higher infections rates as to linear cities. Henceforth, setting priorities in post-pandemic urban planning schemes is essential for planning resilient cities that are capable to thrive and maintain functionality with lowest possible infections amid else possible diseases that are to follow in severity.

COVID-19 in Asia: Transmission factors, re-opening policies, and vaccination simulation

This work aims to provide insights on the COVID-19 pandemic in three prime aspects. First, we attempted to understand the association between the COVID-19 transmission rate, environmental factors (air pollution, weather, mobility), and socio-political parameters (Government Stringency Index, GSI). Second, we evaluated the efficiency of various strategies, including radical opening, intermittent lockdown, phase lift, and contact tracing, to exit the COVID-19 pandemic and get back to pre-pandemic conditions using a stochastic individual-based epidemiology model. Third, we used a deep learning approach and simulated the vaccination rate and the time for reaching herd immunity. The analysis was done based on the collected data from eight countries in Asia, including Iran, Turkey, India, Saudi Arabia, United Arab Emirates, the Philippines, South Korea, and Russia (as a transcontinental country). Our findings in the first part highlighted a noninfluential impact from the weather-driven parameters and short-term exposure to pollutants on the transmission rate; however, long-term exposure could potentially increase the risk of COVID-19 mortality rates (based on 1998-2017 p.m.2.5 data). Mobility was highly correlated with the COVID-19 transmission and based on our causal analysis reducing mobility could curb the COVID-19 transmission rate with a 6-day lag time (on average). Secondly, among all the tested policies for exiting the COVID-19 pandemic, the contact tracing was the most efficient if executed correctly. With a 2-day delay in tracing the virus hosts, a 60% successful host tracing, and a 70% contact reduction with the hosts, a pandemic will end in a year without overburdening a healthcare system with 6000 hospital beds capacity per million. Lastly, our vaccine simulations showed that the target date for achieving herd immunity significantly varied among the countries and could be delayed to October-november 2022 in countries like India and Iran (based on 60% immunized population and assuming no intermediate factors affecting the vaccination rate).

COVID-19 and environment: a poignant reminder of sustainability in the new normal

The nexus of COVID-19 and environment is conspicuously deep-rooted. The roles of environmental factors in the origin, transmission and spread of COVID-19 and the mutual impact of the pandemic on the global environment have been the two perspectives to view this nexus. The present paper attempts to systematically review the existing literature to understand and explore the linkages of COVID-19 with environment and proposes conceptual frameworks to underline this nexus. Our study indicates a critical role of meteorological factors, ambient air pollutants and wastewater in severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) transmission-spread dynamics. The study also focuses on the direct and indirect impacts of COVID-19 on the regional and global environment. Most of the indirect environmental effects of COVID-19 were attributed to global human confinement that resulted from the implementation of the pandemic containment measures. This worldwide anthropogenic 'pause' sent ripples to all environmental compartments and presented a unique test bed to identify anthropogenic impacts on the earth's natural systems. The review further addresses emerging sustainability challenges in the new normal and their potential solutions. The situation warrants critical attention to the environment-COVID-19 nexus and innovative sustainable practices to address the ramifications of short- and long-term environmental impacts of the COVID-19 pandemic.

Graphical abstract

Climate indicators and COVID-19 recovery: A case of Wuhan during the lockdown

The world needs to get out of the COVID-19 pandemic smoothly through a thorough socio-economic recovery. The first and the foremost step forward in this direction is the health recovery of the people infected. Our empirical study addresses this neglected point in the recent research on COVID-19 and specifically aims at exploring the impact of the environment on health recovery from COVID-19. The sample data are taken during the lockdown period in Wuhan, i.e., from 23rd January 2020 to 8th April 2020. The recently developed econometric technique of Quantile-on-Quantile regression, proposed by Shin and Zhu (2016) is employed to capture the asymmetric association between environmental factors (TEMP, HUM, PM2.5, PM10, CO, SO2, NO2, and O3) and the number of recovered patients from COVID-19. We observe significant heterogeneity in the association among variables across various quantiles. The findings suggest that TEMP, PM2.5, PM10, CO, NO2, and O3 are negatively related to the COVID-19 recovery, while HUM and SO2 show a positive association at most quantiles. The study recommends that maintaining a safe and comfortable environment for the patients may increase the chances of recovery from COVID-19. The success story of Wuhan, the initial epicenter of the novel coronavirus in China, can serve as an important case study for other countries to bring the outbreak under control. The current study could be conducive for the policymakers of those countries where the COVID-19 pandemic is still unrestrained.

The effect of population size for pathogen transmission on prediction of COVID-19 spread

Extreme public health interventions play a critical role in mitigating the local and global prevalence and pandemic potential. Here, we use population size for pathogen transmission to measure the intensity of public health interventions, which is a key characteristic variable for nowcasting and forecasting of COVID-19. By formulating a hidden Markov dynamic system and using nonlinear filtering theory, we have developed a stochastic epidemic dynamic model under public health interventions. The model parameters and states are estimated in time from internationally available public data by combining an unscented filter and an interacting multiple model filter. Moreover, we consider the computability of the population size and provide its selection criterion. With applications to COVID-19, we estimate the mean of the effective reproductive number of China and the rest of the globe except China (GEC) to be 2.4626 (95% CI: 2.4142-2.5111) and 3.0979 (95% CI: 3.0968-3.0990), respectively. The prediction results show the effectiveness of the stochastic epidemic dynamic model with nonlinear filtering. The hidden Markov dynamic system with nonlinear filtering can be used to make analysis, nowcasting and forecasting for other contagious diseases in the future since it helps to understand the mechanism of disease transmission and to estimate the population size for pathogen transmission and the number of hidden infections, which is a valid tool for decision-making by policy makers for epidemic control.

COVID-19 and environmental concerns: A rapid review

COVID-19 has slowed global economic growth and consequently impacted the environment as well. Parallelly, the environment also influences the transmission of this novel coronavirus through various factors. Every nation deals with varied population density and size; air quality and pollutants; the nature of land and water, which significantly impact the transmission of coronavirus. The WHO (Ziaeepour et al., 2008) [1] has recommended rapid reviews to provide timely evidence to the policymakers to respond to the emergency. The present study follows a rapid review along with a brief bibliometric analysis of 328 research papers, which synthesizes the evidence regarding the environmental concerns of COVID-19. The novel contribution of this rapid review is threefold. One, we take stock of the diverse findings as regards the transmission of the novel coronavirus in different types of environments for providing conclusive directions to the ongoing debate regarding the transmission of the virus. Two, our findings provide topical insights as well as methodological guidance for future researchers in the field. Three, we inform the policymakers on the efficacy of environmental measures for controlling the spread of COVID-19.

Impact of COVID-19 pandemic on socio-economic, energy-environment and transport sector globally and sustainable development goal (SDG)

The United Nation's Sustainable Development Goals (SDGs) want to have a peaceful world where human life will be in a safe, healthy, sustainable environment without any inequalities. However, the year 2020 experienced a global pandemic due to COVID-19. This COVID-19 created an adverse impact on human life, economic, environment, and energy and transport sector compared to the pre-COVID-19 scenario. These above-mentioned sectors are interrelated and thus lockdown strategy and stay at home rules to reduce the COVID-19 transmission had a drastic effect on them. With lockdown, all industry and transport sectors were closed, energy demand reduced greatly but the time shift of energy demand had a critical impact on grid and energy generation. Decreased energy demand caused a silver lining with an improved environment. However, drowned economy creating a negative impact on the human mind and financial condition, which at times led to life-ending decisions. Transport sector which faced a financial dip last year trying to coming out from the losses which are not feasible without government aid and a new customer-friendly policy. Sustainable transport and the electric vehicle should take high gear. While people are staying at home or using work from home scheme, building indoor environment must specially be taken care of as a compromised indoor environment affects and increases the risk of many diseases. Also, the energy-efficient building will play a key role to abate the enhanced building energy demand and more generation from renewable sources should be in priority. It is still too early to predict any forecast about the regain period of all those sectors but with vaccination now being introduced and implemented but still, it can be considered as an ongoing process as its final results are yet to be seen. As of now, COVID-19 still continue to grow in certain areas causing anxiety and destruction. With all these causes, effects, and restoration plans, still SDGs will be suffered in great order to attain their target by 2030 and collaborative support from all countries can only help in this time.

Indoor Air Quality Strategies for Air-Conditioning and Ventilation Systems with the Spread of the Global Coronavirus (COVID-19) Epidemic: Improvements and Recommendations

The coronavirus has come to the world and spread with great wide among the countries of the world and has resulted in numerous infections that exceeded 167,181,023 million patients and are close to 3.5 deaths by September 2021. It also brought with it panic and fear, halted many activities, and led to the decline of the global economy. It changed human behavior and forced people to change their lifestyles to avoid infection. One of the most sectors that must be taken into consideration through pandemic coronavirus (COVID-19) around the globe is the air conditioning systems. The HVAC systems depend on the air as a heat transfer medium. The air contains a group of pollutants, viruses, and bacteria, and it affects and destroys human life. The air filter plays a major role as an important component in the air conditioning systems. Thus, it requires more effort by researchers to improve its design to prevent the ultra-size of particles loaded with coronavirus (COVID-19). This paper provides insight into the design of existing combined air-conditioners on their suitability and their impact on the spread of the hybrid coronavirus epidemic and review efforts to obtain a highly efficient air filter to get rid of super-sized particles for protection against epidemic infection. In addition, important guideline recommendations have been made to limit the spread of the COVID-19 virus and to obtain indoor air quality in air-conditioned places.

Association between meteorological indicators and COVID-19 pandemic in Pakistan

This study was designed to investigate the impact of meteorological indicators (temperature, rainfall, and humidity) on total COVID-19 cases in Pakistan, its provinces, and administrative units from March 10, 2020, to August 25, 2020. The correlation analysis showed that COVID-19 cases and temperature showed a positive correlation. It implies that the increase in COVID-19 cases was reported due to an increase in the temperature in Pakistan, its provinces, and administrative units. The generalized Poisson regression showed that the rise in the expected log count of COVID-19 cases was 0.024 times for a 1 °C rise in the average temperature in Pakistan. Second, the correlation between rainfall and COVID-19 cases was negative in Pakistan. However, the regression coefficient between the expected log count of COVID-19 cases and rainfall was insignificant in Pakistan. Third, the correlation between humidity and the total COVID-19 cases was negative, which implies that the increase in humidity is beneficial to stop the transmission of COVID-19 in Pakistan, its provinces, and administrative units. The reduction in the expected log count of COVID-19 cases was 0.008 times for a 1% increase in the humidity per day in Pakistan. However, humidity and COVID-19 cases were positively correlated in Sindh province. It is required to create awareness among the general population, and the government should include the causes, symptoms, and precautions in the educational syllabus. Moreover, people should adopt the habit of hand wash, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to control the COVID-19.

A Descriptive Analysis of the Scientific Literature on Meteorological and Air Quality Factors and COVID-19

The role of meteorological and air quality factors in moderating the transmission of SARS-CoV-2 and severity of COVID-19 is a critical topic as an opportunity for targeted intervention and relevant public health messaging. Studies conducted in early 2020 suggested that temperature, humidity, ultraviolet radiation, and other meteorological factors have an influence on the transmissibility and viral dynamics of COVID-19. Previous reviews of the literature have found significant heterogeneity in associations but did not examine many factors relating to epidemiological quality of the analyses such as rigor of data collection and statistical analysis, or consideration of potential confounding factors. To provide greater insight into the current state of the literature from an epidemiological standpoint, the authors conducted a rapid descriptive analysis with a strong focus on the characterization of COVID-19 health outcomes and use of controls for confounding social and demographic variables such as population movement and age. We have found that few studies adequately considered the challenges posed by the use of governmental reporting of laboratory testing as a proxy for disease transmission, including timeliness and consistency. In addition, very few studies attempted to control for confounding factors, including timing and implementation of public health interventions and metrics of population compliance with those interventions. Ongoing research should give greater consideration to the measures used to quantify COVID-19 transmission and health outcomes as well as how to control for the confounding influences of public health measures and personal behaviors.

Assessment of variations of air pollutant concentrations during the COVID-19 lockdown and impact on urban air quality in South Asia

Quantifying the variations of atmospheric aerosols and trace gas concentrations with the impact of lockdown due to the Coronavirus disease of 2019 (COVID-19) pandemic is crucial in understanding urban air quality. For this purpose, we utilized the multi-instrumental approach of satellite remote sensing and reanalysis model data to examine the spatial and temporal patterns of major air pollutants during December 2019-June 2020 in South Asia. The lockdown has to lead to a considerable decrease in aerosol optical thickness (AOT) over South China (-18.92%) and Indo-Gangetic Plain (IGP; -24.29%) compared to its ordinary level for a couple of weeks. Noticeable reductions in tropospheric NO2 are observed over the Pearl River Delta (PRD; -0.3/cm2) followed by Central China (CC) with -0.21/cm2and IGP (-0.085/cm2), and the lowest (-0.0008/cm2) in the Tibetan Plateau (TP) region. The changes observed in PM2.5 and SO2 levels (from -58.56% to - 63.64%) are attributed to the decrease in anthropogenic emissions, vehicular exhaust, and industrial activities. However, the BC concentrations are reduced by approximately halved of its ordinary levels in the IGP (-2.28 μg/m3) followed by YRD (-1.56 μg/m3), CC (-1.5 μg/m3), NCP (-1.29 μg/m3), and PRD (-0.78 μg/m3) regions. The total column O3 predominantly increased from 262.68 to 285.53DU, 323.00 to 343.00DU, and 245.00 to 265.00DU in the YRD, NCP, and IGP areas. This is mainly associated with solar radiation, meteorological factors, and an unprecedented reduction in NOx during the lockdown period.

Are population size and diverse climatic conditions the driving factors for next COVID-19 pandemic epicenter in India?

Although a nationwide lockdown was imposed in India amid COVID-19 outbreak since March 24, 2020, the COVID-19 infection is increasing day-by-day. Till June 10, 2021 India has recorded 29,182,072 COVID cases and 359,695 deaths. A number of factors help to influence COVID-19 transmission rate and prevalence. Accordingly, the present study intended to integrate the climatic parameters, namely ambient air temperature (AT) and relative humidity (H) with population mass (PM) to determine their influence for rapid transmission of COVID-19 in India. The sensibility of AT, H and PM parameters on COVID-19 transmission was investigated based on receiver operating characteristics (ROC) classification model. The results depicted that AT and H models have very low sensibility (i.e., lower area under curve value 0.26 and 0.37, respectively compared with AUC value 0.5) to induce virus transmission and discrimination between infected people and healthy ones. Contrarily, PM model is highly sensitive (AUC value is 0.912, greater than AUC value 0.5) towards COVID-19 transmission and discrimination between infected people and healthy ones and approximate population of 2.25 million must impose like social distancing, personal hygiene, etc. as strategic management policy. Therefore, it is predicted, India could be the next epicenter of COVID-19 outbreak because of its over population.

Yearly and Daily Relationship Assessment between Air Pollution and Early-Stage COVID-19 Incidence: Evidence from 231 Countries and Regions

The novel coronavirus disease 2019 (COVID-19) has caused significantly changes in worldwide environmental and socioeconomics, especially in the early stage. Previous research has found that air pollution is potentially affected by these unprecedented changes and it affects COVID-19 infections. This study aims to explore the non-linear association between yearly and daily global air pollution and the confirmed cases of COVID-19. The concentrations of tropospheric air pollution (CO, NO2, O3, and SO2) and the daily confirmed cases between 23 January 2020 and 31 May 2020 were collected at the global scale. The yearly discrepancies of air pollutions and daily air pollution are associated with total and daily confirmed cases, respectively, based on the generalized additive model. We observed that there are significant spatially and temporally non-stationary variations between air pollution and confirmed cases of COVID-19. For the yearly assessment, the number of confirmed cases is associated with the positive fluctuation of CO, O3, and SO2 discrepancies, while the increasing NO2 discrepancies leads to the significant peak of confirmed cases variation. For the daily assessment, among the selected countries, positive linear or non-linear relationships are found between CO and SO2 concentrations and the daily confirmed cases, whereas NO2 concentrations are negatively correlated with the daily confirmed cases; variations in the ascending/declining associations are identified from the relationship of the O3-confirmed cases. The findings indicate that the non-linear relationships between global air pollution and the confirmed cases of COVID-19 are varied, which implicates the needs as well as the incorporation of our findings in the risk monitoring of public health on local, regional, and global scales.

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).

Interrelationship between daily COVID-19 cases and average temperature as well as relative humidity in Germany

COVID-19 pandemic continues to obstruct social lives and the world economy other than questioning the healthcare capacity of many countries. Weather components recently came to notice as the northern hemisphere was hit by escalated incidence in winter. This study investigated the association between COVID-19 cases and two components, average temperature and relative humidity, in the 16 states of Germany. Three main approaches were carried out in this study, namely temporal correlation, spatial auto-correlation, and clustering-integrated panel regression. It is claimed that the daily COVID-19 cases correlate negatively with the average temperature and positively with the average relative humidity. To extract the spatial auto-correlation, both global Moran's [Formula: see text] and global Geary's [Formula: see text] were used whereby no significant difference in the results was observed. It is evident that randomness overwhelms the spatial pattern in all the states for most of the observations, except in recent observations where either local clusters or dispersion occurred. This is further supported by Moran's scatter plot, where states' dynamics to and fro cold and hot spots are identified, rendering a traveling-related early warning system. A random-effects model was used in the sense of case-weather regression including incidence clustering. Our task is to perceive which ranges of the incidence that are well predicted by the existing weather components rather than seeing which ranges of the weather components predicting the incidence. The proposed clustering-integrated model associated with optimal barriers articulates the data well whereby weather components outperform lag incidence cases in the prediction. Practical implications based on marginal effects follow posterior to model diagnostics.

Causal graph analysis of COVID-19 observational data in German districts reveals effects of determining factors on reported case numbers

Several determinants are suspected to be causal drivers for new cases of COVID-19 infection. Correcting for possible confounders, we estimated the effects of the most prominent determining factors on reported case numbers. To this end, we used a directed acyclic graph (DAG) as a graphical representation of the hypothesized causal effects of the determinants on new reported cases of COVID-19. Based on this, we computed valid adjustment sets of the possible confounding factors. We collected data for Germany from publicly available sources (e.g. Robert Koch Institute, Germany's National Meteorological Service, Google) for 401 German districts over the period of 15 February to 8 July 2020, and estimated total causal effects based on our DAG analysis by negative binomial regression. Our analysis revealed favorable effects of increasing temperature, increased public mobility for essential shopping (grocery and pharmacy) or within residential areas, and awareness measured by COVID-19 burden, all of them reducing the outcome of newly reported COVID-19 cases. Conversely, we saw adverse effects leading to an increase in new COVID-19 cases for public mobility in retail and recreational areas or workplaces, awareness measured by searches for "corona" in Google, higher rainfall, and some socio-demographic factors. Non-pharmaceutical interventions were found to be effective in reducing case numbers. This comprehensive causal graph analysis of a variety of determinants affecting COVID-19 progression gives strong evidence for the driving forces of mobility, public awareness, and temperature, whose implications need to be taken into account for future decisions regarding pandemic management.

The Role of Social Media in the Advent of COVID-19 Pandemic: Crisis Management, Mental Health Challenges and Implications

BACKGROUND

This study focuses on how educating people through social media platforms can help reduce the mental health consequences of the COVID-19 to manage the global health crisis. The pandemic has posed a global mental health crisis, and correct information is indispensable to dispel uncertainty, fear, and mental stress to unify global communities in collective combat against COVID-19 disease worldwide. Mounting studies specified that manifestly endless coronavirus-related newsfeeds and death numbers considerably increased the risk of global mental health issues. Social media provided positive and negative data, and the COVID-19 has resulted in a worldwide infodemic. It has eroded public trust and impeded virus restraint, which outlived the coronavirus pandemic itself.

METHODS

The study incorporated the narrative review analysis based on the existing literature related to mental health problems using the non-pharmaceutical interventions (NPIs) approach to minimize the COVID-19 adverse consequences on global mental health. The study performed a search of the electronic databases available at PsycINFO, PubMed, and LISTA. This research incorporates the statistical data related to the COVID-19 provided by the WHO, John Hopkins University, and Pakistani Ministry of Health.

RESULTS

Pakistan reported the second-highest COVID-19 cases within South Asia, the fifth-highest number of cases in Asia after Iran, India, Russia, Saudi Arabia, and the 14th highest recorded cases, as of October 14, 2020. Pakistan effectively managed the COVID-19 pandemic in the second wave. It stands at the eighth-highest number of confirmed cases in Asia, the 3rd-highest in South Asia, and the 28th-highest number of established patients globally, as of February20, 2021.

CONCLUSION

The COVID-19 has resulted in over 108.16 million confirmed cases, deaths over 2.374 million, and a recovery of 80.16 million people worldwide, as of February 12, 2021. This study focused on exploring the COVID-19 pandemic's adverse effects on global public health and the indispensable role of social media to provide the correct information in the COVID-19 health crisis. The findings' generalizability offers helpful insight for crisis management and contributes to the scientific literature. The results might provide a stepping-stone for conduct future empirical studies by including other factors to conclude exciting developments.

A review of the impact of weather and climate variables to COVID-19: In the absence of public health measures high temperatures cannot probably mitigate outbreaks

The new severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) pandemic was first recognized at the end of 2019 and has caused one of the most serious global public health crises in the last years. In this paper, we review current literature on the effect of weather (temperature, humidity, precipitation, wind, etc.) and climate (temperature as an essential climate variable, solar radiation in the ultraviolet, sunshine duration) variables on SARS-CoV-2 and discuss their impact to the COVID-19 pandemic; the review also refers to respective effect of urban parameters and air pollution. Most studies suggest that a negative correlation exists between ambient temperature and humidity on the one hand and the number of COVID-19 cases on the other, while there have been studies which support the absence of any correlation or even a positive one. The urban environment and specifically the air ventilation rate, as well as air pollution, can probably affect, also, the transmission dynamics and the case fatality rate of COVID-19. Due to the inherent limitations in previously published studies, it remains unclear if the magnitude of the effect of temperature or humidity on COVID-19 is confounded by the public health measures implemented widely during the first pandemic wave. The effect of weather and climate variables, as suggested previously for other viruses, cannot be excluded, however, under the conditions of the first pandemic wave, it might be difficult to be uncovered. The increase in the number of cases observed during summertime in the Northern hemisphere, and especially in countries with high average ambient temperatures, demonstrates that weather and climate variables, in the absence of public health interventions, cannot mitigate the resurgence of COVID-19 outbreaks.

A review of the impact of weather and climate variables to COVID-19: In the absence of public health measures high temperatures cannot probably mitigate outbreaks

The new severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) pandemic was first recognized at the end of 2019 and has caused one of the most serious global public health crises in the last years. In this paper, we review current literature on the effect of weather (temperature, humidity, precipitation, wind, etc.) and climate (temperature as an essential climate variable, solar radiation in the ultraviolet, sunshine duration) variables on SARS-CoV-2 and discuss their impact to the COVID-19 pandemic; the review also refers to respective effect of urban parameters and air pollution. Most studies suggest that a negative correlation exists between ambient temperature and humidity on the one hand and the number of COVID-19 cases on the other, while there have been studies which support the absence of any correlation or even a positive one. The urban environment and specifically the air ventilation rate, as well as air pollution, can probably affect, also, the transmission dynamics and the case fatality rate of COVID-19. Due to the inherent limitations in previously published studies, it remains unclear if the magnitude of the effect of temperature or humidity on COVID-19 is confounded by the public health measures implemented widely during the first pandemic wave. The effect of weather and climate variables, as suggested previously for other viruses, cannot be excluded, however, under the conditions of the first pandemic wave, it might be difficult to be uncovered. The increase in the number of cases observed during summertime in the Northern hemisphere, and especially in countries with high average ambient temperatures, demonstrates that weather and climate variables, in the absence of public health interventions, cannot mitigate the resurgence of COVID-19 outbreaks.

Structural equation modeling to shed light on the controversial role of climate on the spread of SARS-CoV-2

Climate seems to influence the spread of SARS-CoV-2, but the findings of the studies performed so far are conflicting. To overcome these issues, we performed a global scale study considering 134,871 virologic-climatic-demographic data (209 countries, first 16 weeks of the pandemic). To analyze the relation among COVID-19, population density, and climate, a theoretical path diagram was hypothesized and tested using structural equation modeling (SEM), a powerful statistical technique for the evaluation of causal assumptions. The results of the analysis showed that both climate and population density significantly influence the spread of COVID-19 (p < 0.001 and p < 0.01, respectively). Overall, climate outweighs population density (path coefficients: climate vs. incidence = 0.18, climate vs. prevalence = 0.11, population density vs. incidence = 0.04, population density vs. prevalence = 0.05). Among the climatic factors, irradiation plays the most relevant role, with a factor-loading of - 0.77, followed by temperature (- 0.56), humidity (0.52), precipitation (0.44), and pressure (0.073); for all p < 0.001. In conclusion, this study demonstrates that climatic factors significantly influence the spread of SARS-CoV-2. However, demographic factors, together with other determinants, can affect the transmission, and their influence may overcome the protective effect of climate, where favourable.