COVID-19 pandemic: environmental and social factors influencing the spread of SARS-CoV-2 in São Paulo, Brazil.

The new coronavirus SARS-CoV-2 has infected more than 14 million people worldwide so far. Brazil is currently the second leading country in number of cases of COVID-19, while São Paulo state accounts for 20% of total confirmed cases in Brazil. The aim of this study was to assess environmental and social factors influencing the spread of SARS-CoV-2 in the expanded metropolitan area of São Paulo, Brazil. Firstly, a spatial analysis was conducted to provide insights into the spread of COVID-19 within the expanded metropolitan area. Moreover, Spearman correlation test and sensitivity analysis were performed to assess social indicators and environmental conditions which possibly influence the incidence of COVID-19. Our results reveal that the spread of COVID-19 from the capital city São Paulo-its epicenter in Brazil-is directly associated with the availability of highways within the expanded metropolitan area of São Paulo. As for social aspects, COVID-19 infection rate was found to be both positively correlated with population density, and negatively correlated with social isolation rate, hence indicating that social distancing has been effective in reducing the COVID-19 transmission. Finally, COVID-19 infection rate was found to be inversely correlated with both temperature and UV radiation. Together with recent literature our study suggests that the UV radiation provided by sunlight might contribute to depletion of SARS-CoV-2 infectivity.

Data Mining Tools for Analysis of Dependence of Gas Consuption of the Gas Price for Housholds of the EU Memberd-States

The current crisis in the European natural gas industry, caused by the effects of the COVID-19 pandemic, extreme weather conditions, recent frequent accidents at gas plants around the world and speculation in the emissions market has actualized the problem of turning data into useful information and knowledge that can support decision-making. In this article, we used associative rules to identify non-obvious associative links in terms of pre-crisis empirical data (January 2013 - June 2021) between gas consumption and gas prices for EU Members. The results obtained can be useful in shaping an effective pricing policy in the European Union gas market and in regulating of household gas consumption © 2022 IEEE.

COVID-19 transmission in Africa: estimating the role of meteorological factors.

Climate variables play a critical role in COVID-19's spread. Therefore, this research aims to analyze the effect of average temperature and relative humidity on the propagation of COVID-19 in Africa's first four affected countries (South Africa, Morocco, Tunisia, and Ethiopia). As a result, policymakers should develop effective COVID-19 spread control strategies. For each country, using daily data of confirmed cases and weather variables from May 1, 2020, to April 30, 2021, generalized linear models (Poisson regression) and general linear models were estimated. According to the findings, the rising average temperature causes COVID-19 daily new cases to increase in South Africa and Ethiopia while decreasing in Morocco and Tunisia. However, in Tunisia, the relative humidity and daily new cases of COVID-19 are positively correlated, while in the other three countries, they are negatively associated.

The Relationship between Weather Conditions and COVID-19 Transmission and Deaths in Visegrád Countries

Since the end of December 2019, COVID-19 has become a worldwide issue, and its transmission and adverse effect on health and socioeconomics has become a global agenda for researchers and policymakers. Concerning this, findings argue that there is a relationship between weather conditions and COVID-19 cases and deaths. However, empirical findings on the relationship and impact of weather conditions and COVID-19 cases and deaths have been given little attention regarding Visegrád (V4) countries. Therefore, this study examines the relationship and impact of climate indicators on new COVID-19 cases and deaths for V4 countries using trend, correlation, and regression analysis. The study found that the average temperature and dew point have a negative and significant correlation with new cases and deaths of COVID-19 in V4 countries. Besides, the rise of both average temperature and dew point has a negative and significant impact on COVID-19 new cases and deaths in V4 countries. However, average humidity has a positive and significant correlation with new cases and deaths of COVID-19 in V4 countries. Moreover, the rise in humidity levels lead to an increase in new cases and deaths of COVID-19 in V4 countries.

Impacts of Air Pollution and Dampness on Occupant Respiratory Health in Unplanned Houses: A Case Study of Bandung, Indonesia

This paper presents the results from field measurements and household surveys on the severity of indoor mold risk and its impact on respiratory health in a typical unplanned neighborhood of kampungs in Bandung, Indonesia. Mold risk was investigated using fungal risk detectors (n = 102), while air pollution levels were established with total suspended particulate (TSP) and particulate matter (PM2.5) (n = 38). The self-reported prevalence of respiratory diseases was obtained using a questionnaire form (ATS-DLD-78) (n = 599). The results showed that respiratory health problems were higher in the rainy season, particularly among children. Most houses suffered from severe mold risk, primarily due to extreme humid weather conditions, especially during rainy season (97%) where water leakage was prevalent (60%). In addition, the TSP and PM2.5 concentrations exceeded the WHO standards in most kampung houses, where around 58% of the houses recorded higher outdoor mean PM2.5 concentrations than indoors. Further, the path analysis showed that allergies followed by humidity rate and smell, which were affected by window-opening duration, directly impacted children’s respiratory health. Smoking behavior and building-related health problems, due to exposure to outdoor air pollution, affected the respiratory health of those aged 15 years old and over.

A Machine Learning approach for shared bicycle demand forecasting

More than 9 million bicycles are shared worldwide through more than 3.000 Bicycle Shared Systems (BSS). Investigating possible behaviours related to the demand for these services will optimize their success. The purpose of this research is to identify the impact of weather conditions, covid and pollution on the usage of BSS. Different machine learning algorithms are studied and used to analyze the different variables. Results were consistent with the literature and theory. In what concerns the algorithms, random forest and multi-layer perceptron regressor performed better, showing a better prediction power. © 2022 IEEE Computer Society. All rights reserved.

An Application of Artificial Neural Network to Evaluate the Influence of Weather Conditions on the Variation of PM2.5-Bound Carbonaceous Compositions and Water-Soluble Ionic Species

Previous studies have determined biomass burning as a major source of air pollutants in the ambient air in Thailand. To analyse the impacts of meteorological parameters on the variation of carbonaceous aerosols and water-soluble ionic species (WSIS), numerous statistical models, including a source apportionment analysis with the assistance of principal component analysis (PCA), hierarchical cluster analysis (HCA), and artificial neural networks (ANNs), were employed in this study. A total of 191 sets of PM2.5 samples were collected from the three monitoring stations in Chiang-Mai, Bangkok, and Phuket from July 2020 to June 2021. Hotspot numbers and other meteorological parameters were obtained using NOAA-20 weather satellites coupled with the Global Land Data Assimilation System. Although PCA revealed that crop residue burning and wildfires are the two main sources of PM2.5, ANNs highlighted the importance of wet deposition as the main depletion mechanism of particulate WSIS and carbonaceous aerosols. Additionally, Mg2+ and Ca2+ were deeply connected with albedo, plausibly owing to their strong hygroscopicity as the CCNs responsible for cloud formation.

Correlation Study of Global Weather Conditions and COVID-19 Transmission: A Clustering Approach

The COVID-19 pandemic is having a drastic impact around the world. Much research has showed that weather conditions (e.g., temperature, humidity) may correlate with trans-mission and spread of the virus. However, there exist past studies with inconsistent conclusions. In this research, we analyzed a correlation between weather conditions and the COVID-19 transmission using a globally collected database. We explored the data and found that the correlation is not linear;therefore, we employed a non-linear model called Random Forest to analyze the relationship between weather conditions and the number of COVID-19 daily reported cases on a global scale, extracting only strongly correlated variables for further analysis. Subsequently, we applied K-Means clustering to identify clusters of weather conditions that influence COVID-19 dissemination. The environmental conditions that found the highest transmission of COVID-19 were-0.605 degrees Celsius, 70.63 percent relative humidity, and 17.15 km/hr wind speed, according to the results of our research. The experimental results also indicate a negative correlation between temperature and the number of new cases, while a positive relationship exists between relative humidity and wind speed. © 2022 IEEE.

Geological and geotechnical investigations for the Salang highway, Afghanistan

The present road over the Hindu Kush Mountain Range via the Salang Pass (termed the Salang Corridor) links the south of Afghanistan, including the capital Kabul, with northern Afghanistan and Central Asia. It is the only route that allows year-round north–south passage of goods and people across the mountain range. However, the present road has suffered years of neglect and is now in a state of serious disrepair. In 2012, the United States Agency for International Development completed a study examining potential alternative routes. Three routes were selected for in-depth study and detailed engineering design. In mid-2017 SMEC International Pty Ltd were appointed to carry out a feasibility study to recommend the most suitable of the three routes and to then carry out a detailed design of the chosen route. This paper outlines the geological and geotechnical studies carried out during the project with a focus on investigations for the detailed design phase, and presents the geotechnical design recommendations arising from those investigations. This paper also explains the difficulties involved in carrying out the investigations including security issues, harsh weather conditions, and maintaining effective communication between the project team both in Afghanistan and around the globe during the COVID-19 pandemic.

Modeling Surface Air Pollution with Reduced Emissions during the COVID-19 Pandemic Using CHIMERE and COSMO-ART Chemical Transport Models

The results of numerical modeling of air pollution using CHIMERE and COSMO-ART chemical transport models are presented. The modeling was performed according to the scenarios of the 50–60% reduction of emissions from anthropogenic sources in the Moscow region during the period of March–July 2020. Scenario calculations of pollutant concentrations were compared with baseline simulations using regionally adapted inventory of anthropogenic pollutant emissions to the atmosphere. The most significant decrease in the concentrations of NO2 and CO was reproduced by the models when emissions from two sectoral sources (vehicles and nonindustrial plants) were reduced. The PM10 drop was mostly influenced by the reduction of emissions from industrial combustion. With the total reduction of emissions from anthropogenic sources as compared to the baseline calculations, the pollutant concentration decreased by 44–54% for NO2, by 38–44% for CO, and by 26–39% for PM10. This generally coincides with the quantitative estimates of the pollution level drop obtained by other authors. The greatest effect of reducing pollutant emissions into the atmosphere was found during the episodes of adverse weather conditions for air purification, when the simulated and observed pollution level increases by 3–5 times as compared to the conditions of intense pollutant dispersion.

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.

The Effects of Weather Conditions on COVID-19 Forecasting, the United Arab Emirates as a Reliable Study Case

Predicting new COVID-19 cases was, and still is, of paramount importance to decision-makers in many countries. Due to its transmission nature, e.g., sneezing, coughing, and physical contact, researchers have developed prediction models that include weather features hoping to improve the forecasting models' predictions. The research did not show any conclusive evidence about the importance of including weather features in forecasting models. Thus, this paper addresses this problem by considering the United Arab Emirates (UAE) COVID-19 cases and weather conditions. Using long-short term memory (LSTM) models, a variant of artificial neural network used for forecasting, we compare a uni-variate, default forecasting model that only considers COVID-19 cases to other bi- and multi-variate models that relies on COVID-19 and weather features. The results show that including weather features in the forecasting models did not significantly improve the accuracy of the default LSTM model;the maximum increase in the coefficient of determination did not exceed 0.02. Moreover, humidity, if considered with other weather features, has a small influence on improving the prediction accuracy. © 2021 IEEE.

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.

Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19.

There are several recent publications criticizing the failure of COVID-19 forecasting models, with swinging over predictions and underpredictions, which have made it difficult for decision and policy making. Observing the failures of several COVID-19 forecasting models and the alarming spread of the virus, we seek to use some stable response for forecasting COVID-19, viz., ratios of COVID-19 cases to mortalities, rather than COVID-19 cases or fatalities. A trend of low COVID-19 cases-to-mortality ratios calls for urgent attention: the need for vaccines, for instance. Studies have shown that there are influences of weather parameters on COVID-19; and COVID-19 may have come to stay and could manifest a seasonal outbreak profile similar to other infectious respiratory diseases. In this paper, the influences of some weather, geographical, economic and demographic covariates were evaluated on COVID-19 response based on a series of Granger-causality tests. The effect of four weather parameters, viz., temperature, rainfall, solar irradiation and relative humidity, on daily COVID-19 cases-to-mortality ratios of 36 countries from 5 continents of the world were determined through regression analysis. Regression studies show that these four weather factors impact ratios of COVID-19 cases-to-mortality differently. The most impactful factor is temperature which is positively correlated with COVID-19 cases-to-mortality responses in 24 out of 36 countries. Temperature minimally affects COVID-19 cases-to-mortality ratios in the tropical countries. The most influential weather factor - temperature - was incorporated in training random forest and deep learning models for forecasting the cases-to-mortality rate of COVID-19 in clusters of countries in the world with similar weather conditions. Evaluation of trained forecasting models incorporating temperature features show better performance compared to a similar set of models trained without temperature features. This implies that COVID-19 forecasting models will predict more accurately if temperature features are factored in, especially for temperate countries.

Exposure-lag-response associations between weather conditions and ankylosing spondylitis: a time series study.

BACKGROUND: Patients with ankylosing spondylitis (AS) have reported that their pain becomes worse when the local weather changes. However, there is limited evidence verifying the short-term associations between meteorological factors and outpatient visits for patients with AS. Therefore, this study evaluates this possible association. METHODS: Meteorological data and data on daily AS outpatient visits to a general hospital in Hefei, China, from 2014 to 2019 were collected and analysed. Distributed lag nonlinear models and Poisson regression models were employed to determine the association between weather conditions and outpatient visits; the results were also stratified by gender and age. RESULTS: High relative humidity is significantly associated with all patient visits in lag 1 (RR = 1.113, 95% CI 1.021 to 1.213) and lag 7 days (RR = 1.115, 95% CI 1.014 to 1.227). A low relative risk to the nadir is observed in lag 4 days (RR = 0.920, 95% CI 0.862 to 0.983). Male and young patients (< 65 years) are more vulnerable to damp weather, and elderly people (≥ 65 years) are significantly affected by high temperatures in lag 7 days (RR = 3.004, 95% CI 1.201 to 7.510). CONCLUSIONS: Our findings suggest a potential relationship between exposure to weather conditions and increased risk of AS outpatient visits. These results can aid hospitals in preparing for and managing hospital visits by AS patients when the local weather conditions change.

Weather, mental health, and mobility during the first wave of the COVID-19 pandemic.

During the first United Kingdom wave of the COVID-19 outbreak, the first lockdown was announced on March 23, 2020, with a final easing of the restrictions on July 4, 2020. Among the most important public health costs of lockdown restrictions are the potential adverse effects on mental health and physical activity. Using data from the UK Household Longitudinal Study and Google COVID-19 Mobility Reports we find evidence of reduced park mobility during the initial period of the first UK lockdown and confirm existing evidence of worsening mental health. Linkage with weather data shows that contrary to popular belief, daily or weekly weather conditions do not exacerbate the mental health consequences of the pandemic, as we found no systematic associations during the first lockdown period; on the other hand, we find systematic links between park mobility and weather over the same period.

Climate factors and incidence of Middle East respiratory syndrome coronavirus.

BACKGROUND: Our understanding of climate factors and their links to the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) outbreaks is incomplete. This study aimed to estimate the monthly incidence of MERS-CoV cases and to investigate their correlation to climate factors. METHODS: The study used aggregated monthly MERS-CoV cases that reported to the Saudi Center for Disease Prevention and Control from the Riyadh Region between November 1, 2012 and December 31, 2018. Data on the meteorological situation throughout the study period was calculated based on Google reports on the Riyadh Region (24.7136°N, 46.6753°E). The Poisson regression was used to estimate the incidence rate ratio (IRR) and its 95% confidence intervals (CI) for each climate factor. RESULTS: A total of 712 MERS-CoV cases were included in the analysis (mean age 54.2±9.9 years), and more than half (404) (56.1%) MERS-CoV cases were diagnosed during a five-month period from April to August. The highest peak timing positioned in August 2015, followed by April 2014, June 2017, March 2015, and June 2016. High temperatures (IRR=1.054, 95% CI: 1.043-1.065) and a high ultraviolet index (IRR=1.401, 95% CI: 1.331-1.475) were correlated with a higher incidence of MERS-CoV cases. However, low relative humidity (IRR=0.956, 95% CI: 0.948-0.964) and low wind speed (IRR=0.945, 95% CI: 0.912-0.979) were correlated with a lower incidence of MERS-CoV cases. CONCLUSION: The novel coronavirus, MERS-CoV, is influenced by climate conditions with increasing incidence between April and August. High temperature, high ultraviolet index, low wind speed, and low relative humidity are contributors to increased MERS-CoV cases. The climate factors must be evaluated in hospitals and community settings and integrated into guidelines to serve as source of control measures to prevent and eliminate the risk of infection.

A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain.

The new SARS-CoV-2 coronavirus, which causes the COVID-19 disease, was reported in Wuhan, China, in December 2019. This new pathogen has spread rapidly around more than 200 countries, in which Spain has one of the world's highest mortality rates so far. Previous studies have supported an epidemiological hypothesis that weather conditions may affect the survival and spread of droplet-mediated viral diseases. However, some contradictory studies have also been reported in the same research line. In addition, many of these studies have been performed considering only meteorological factors, which can limit the reliability of the results. Herein, we report a spatio-temporal analysis for exploring the effect of daily temperature (mean, minimum and maximum) on the accumulated number of COVID-19 cases in the provinces of Spain. Non-meteorological factors such as population density, population by age, number of travellers and number of companies have also been considered for the analysis. No evidence suggesting a reduction in COVID-19 cases at warmer mean, minimum and maximum temperatures has been found. Nevertheless, these results need to be interpreted cautiously given the existing uncertainty about COVID-19 data, and should not be extrapolated to temperature ranges other than those analysed here for the early evolution period.