How environmental factors affecting COVID-19 transmission? case study: Riyadh region in Saudi Arabia

In this study, I have conducted non-medical, non-clinical-care research that will enable immediate exploring of how environmental factors affect spread of COVID-19 in Kingdom of Saudi Arabia (KSA). It focusses on climatic environmental factors that affect the distribution and population size of disease vectors and the relationship(s) between each of these environmental variables that provided from National Center for Metrology and COVID-19 infected cases from Ministry of Health in KSA. I used daily environmental data, including minimum, maximum, and averages temperatures (°C), rainfall amounts (mm), wind speed (KTS/Deg) and relative humidity (%) over the Riyadh region in Saudi Arabia. Spearman's rank correlation coefficient used to analyze the data. The results showed that average temperatures, minimum temperatures, and maximum temperatures were significantly correlated with a COVID-19 epidemic, (r = 0.527;0.509;0.530 respectively). A negative correlation was found with relative humidity (r = -0.475). These findings will be used as lessons learned as well as best practices in the future to help decision makers to understand the factors controlling COVID-19′s spread in KSA. © 2022 The Author(s)

Modeling COVID-19 Infected Cases and Deaths Based on Generalized Method of Moments

This paper investigates the dynamic relationships between the number of COVID-19 infected cases and deaths in all the districts of Karnataka state, India, from July 2020 to December 2021 based on the panel Generalized Method of Moments (GMM). The panel GMM model with the first difference transformation was found suitable for studying the dynamics of the number of deaths due to COVID-19 infections over time. The one-period lag (DEATHS (-1)) has a positive and significant effect on the number of deaths (DEATH). The Wald test confirms the validity of the coefficients' significance and adds explanatory power to the model. The correlation between number of fatalities at time t positively correlated with the number of deaths in the previous period. Also, the number of infected cases positively and significantly influences the number of deaths over time. Granger pairwise causality test reveals the existence of bi-directional causality relationships between the COVID-19 infected cases and deaths.

How Environmental Factors Affecting COVID-19 Transmission? Case Study: Riyadh region in Saudi Arabia

In this study, I have conducted non-medical, non-clinical-care research that will enable immediate exploring of how environmental factors affect spread of COVID-19 in Kingdom of Saudi Arabia (KSA). It focusses on climatic environmental factors that affect the distribution and population size of disease vectors and the relationship(s) between each of these environmental variables that provided from National Center for Metrology and COVID-19 infected cases from Ministry of Health in KSA. I used daily environmental data, including minimum, maximum, and averages temperatures (°C), rainfall amounts (mm), wind speed (KTS/Deg) and relative humidity (%) over the Riyadh region in Saudi Arabia. Spearman's rank correlation coefficient used to analyze the data. The results showed that average temperatures, minimum temperatures, and maximum temperatures were significantly correlated with a COVID-19 epidemic, (r = 0.527;0.509;0.530 respectively). A negative correlation was found with relative humidity (r= -0.475). These findings will be used as lessons learned as well as best practices in the future to help decision makers to understand the factors controlling COVID-19's spread in KSA.

The effect of weather data on the spread of COVID-19 in Jordan.

This study aims to analyze the correlation between the daily confirmed COVID-19 cases in Jordan and metrological parameters including the average daily temperature (°C), maximum ambient temperature (°C), relative humidity (%), wind speed (m/s), pressure (kPa), and average daily solar radiation (W/m2). This covers the first and the second waves in Jordan. The data were obtained from both the Jordanian Ministry of health and the Jordan Metrological Department. In this work, the Spearman correlation test was used for data analysis, since the normality assumption was not fulfilled. It was found that the most effective weather parameters on the active cases of COVID-19 in the initial wave transmission was the average daily solar radiation (r = - 0.503; p = 0.000), while all other tests for other parameters failed. In the second wave of COVID-19 transmission, it was found that the most effective weather parameter on the active cases of COVID-19 was the maximum temperature (r = 0.394; p = 0.028). This was followed by wind speed (r = 0.477; p = 0.007), pressure (r = - 0.429; p = 0.016), and average daily solar radiation (r = - 0.757; p = 0.000). Furthermore, the independent variable importance of multilayer perceptron showed that wind speed has a direct relationship with active cases. Conversely, areas characterized by low values of pressure and daily solar radiation exposure have a high rate of infection. Finally, a global sensitivity analysis using Sobol analysis showed that daily solar radiation has a high rate of active cases that support the virus' survival in both wave transmissions.

COVID-19: Geospatial Analysis of the Pandemic—A Case Study of Bihar State, India, Using Data Derived from Remote Sensing Satellites and COVID-19 National Geoportal

The state of Bihar has a sizeable population which is spread all over the nation especially the skilled and non-skilled labourers. This populace contributes towards human resource as service providers for nation building in various sectors across the length and breadth of India. The declaration by the World Health Organization (WHO) of the spread of COVID-19 virus as a pandemic brought a nationwide lockdown from 23rd of March 2020 to curb its spread. These daily wage workers were stranded far and wide without any resources. The transport communication was also withdrawn initially against the spread. Now, as soon as the conditions became conducive for the migrant labours to return to the native state of Bihar, there was a fear for disaster due to these returning migrant labour force as they probably could become a vector for the spread of COVID-19 pandemic in the state. The state government in consonance with the Central Government formed strict protocols to be adhered to, for these returning migrants. The present paper statistically analyses the spread of this pandemic once the migrant populace began returning to their home state. It investigates whether the influx of so many humans from various parts of the country would become the hub of the spread of the virus causing infectious hot spots or not. Simultaneously, as many researchers were trying to correlate the presence of atmospheric nitrogen dioxide (NO2) with the spread of COVID-19 virus, the paper tried to relate the amount of NO2 present over the study area on the day the maximum number of cases were reported in the study area. Evaluation of atmospheric nitrogen dioxide (NO2) used for the present paper was derived from satellite data. Time series analysis of this NO2 data was done. This enabled us to identify the peak day and the day when the NO2 levels were minimum. Incidentally, the number of COVID-19 cases reported synchronized with the NO2 levels in the atmosphere. Spatial auto-correlation was performed using Moran’s I test on the above two days. The values so obtained indicated that there were no hot spots identified, and the virus was found to be spread in a dispersed manner. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Effect of climatology parameters on air pollution during COVID-19 pandemic in Jordan.

This study aims to explore the real-time impact of the COVID-19 pandemic on measured air pollution in the three largest cities of Jordan (Amman, Irbid and Zarqa). It is hypothesized that a sharp decrease in the emitted amounts of particulate matter (PM10), CO, NO2 and SO2 during COVID-19 pandemic will be obtained, this corresponds with the reduced traffic due to mandated business closures. To achieve this exploration a paired sample t-test is used to compare the concentration of these four pollutants in the three cities over the period from 15 March to 30 June during the years from 2016 to 2020. It is found that there is a significant difference between the emitted concentrations mean values of CO, PM10, SO2 and NO2 during the period of study. This was indicated by the values of p for each species, which was less than 5 % for all these pollutants. The maximum reduction in SO2 and NO2 concentration during the lockdown period was in Zarqa. Irbid city witnessed the highest percentage reduction in CO and PM10. Furthermore, the correlation test, independent variable importance of multilayer perceptron and global sensitivity analysis using Sobol analysis showed that metrological data (Humidity, wind speed, average temperature and pressure) have a direct relationship with concentrations of CO, PM10, SO2 and NO2 in Amman, Irbid and Zarqa before and after COVID-19 pandemic.

Climate effects on the COVID-19 outbreak: a comparative analysis between the UAE and Switzerland.

The main aim of the present study is to disclose the similarities or differences of the climate effects on the COVID-19 outbreak in two countries, which have different climatic conditions. Using the correlation modeling, the results revealed that some climatic factors, such as the ULR, temperature, and CH4 in the UAE and aerosol index and NO2 in Switzerland have positive lagged correlations with the outburst of COVID-19 by intensifying role within - 9, - 7, and - 2 days. The mitigating role was also observed for ozone/solar radiation and temperature/long-wave radiation in the UAE and Switzerland, respectively. The initial hypotheses of the research have confirmed the correlations between new cases of COVID-19 and ULR and aerosol indices in the UAE and Switzerland. However, the main finding revealed that the climate effects on the COVID-19 outbreak show different roles in the different countries, locating in dissimilar climatic zones. Accordingly, the COVID-19 can be intensified by increases of the ULR and temperature in an arid region, while it can be exactly mitigated by increases of these factors in a temperate area. This finding may be useful for future researches for identifying the essential influencing factors for the mitigating COVID-19 outbreak.