Evaluation of Vulnerability Status of the Infection Risk to COVID-19 Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): A Case Study of Addis Ababa City, Ethiopia

Identifying spatiotemporal trends of SARS-CoV-2 RNA in wastewater: from the perspective of upstream and downstream wastewater-based epidemiology (WBE)

Recently, many efforts have been made to address the rapid spread of newly identified COVID-19 virus variants. Wastewater-based epidemiology (WBE) is considered a potential early warning tool for identifying the rapid spread of this virus. This study investigated the occurrence of SARS-CoV-2 in eight wastewater treatment plants (WWTPs) and their sewerage systems which serve most of the population in Taoyuan City, Taiwan. Across the entire study period, the wastewater viral concentrations were correlated with the number of COVID-19 cases in each WWTP (Spearman's r = 0.23-0.76). In addition, it is confirmed that several treatment technologies could effectively eliminate the virus RNA from WWTP influent (> 90%). On the other hand, further results revealed that an inverse distance weighted (IDW) interpolation and hotspot model combined with the geographic information system (GIS) method could be applied to analyze the spatiotemporal variations of SARS-CoV-2 in wastewater from the sewer system. In addition, socio-economic factors, namely, population density, land use, and income tax were successfully identified as the potential drivers which substantially affected the onset of the COVID-19 outbreak in Taiwan. Finally, the data obtained from this study can provide a powerful tool in public health decision-making not only in response to the current epidemic situation but also to other epidemic issues in the future.

Sanitary municipal landfill site selection by integration of GIS and multi-criteria techniques for environmental sustainability in Safita area, Tartous governorate, Syria

Urban waste disposal is a problem that poses a major challenge to city planners as a result of rapid population growth and urbanization. Finding suitable sites for solid waste is one of the most important solutions developed globally to manage this problem. In this regard, a set of physical, socio-economic and technological criteria must be considered to tackle the problem. Safita area (Tartous governorate) witnessed a rapid population growth during the decade of the war in Syria due to the onrush of internal refugees, which resulted in several environmental problems, including random waste dumps. After perusing the previous literature and considering expert opinions, a map of the spatial suitability of sustainable waste sites in the Safita area was developed by integrating the multi-criteria decision- making methodology (analytic hierarchy process) with the geographic information system. Thirteen criteria, including elevation, slope, permeability, distance to faults, distance to settlement, land use/land cover, distance to drainage, distance to water supplies, distance to lakes, distance to road, distance from tourist centers, distance from archaeological centers, and distance from religious centers, were used to achieve the goal of this study. The layer maps for these criteria were developed based on various data sources, including conventional and remote sensing data. Potential landfill sites were identified and divided into five categories: unsuitable (83.28%), less suitable (8.49%), moderately suitable (4.49%), highly suitable (2.57%), and very highly suitable (0.72%). The results of this study provide reliable spatial outputs that will help in suggesting new landfill sites that maintain environmental and socio-economic sustainability in the post-war phase. Moreover, the application of the methodology of this study can be generalized to the rest of the regions in Syria within the framework of the integrated management of the problem of random landfills.

Influence of the Demographic, Social, and Environmental Factors on the COVID-19 Pandemic-Analysis of the Local Variations Using Geographically Weighted Regression

As the COVID-19 pandemic continues, an increasing number of different research studies focusing on various aspects of the pandemic are emerging. Most of the studies focus on the medical aspects of the pandemic, as well as on the impact of COVID-19 on various areas of life; less emphasis is put on analyzing the influence of socio-environmental factors on the spread of the pandemic. In this paper, using the geographically weighted regression method, the extent to which demographic, social, and environmental factors explain the number of cases of SARS-CoV-2 is explored. The research was performed for the case-study area of Poland, considering the administrative division of the country into counties. The results showed that the demographic factors best explained the number of cases of SARS-CoV-2; the social factors explained it to a medium degree; and the environmental factors explained it to the lowest degree. Urban population and the associated higher amount and intensity of human contact are the most influential factors in the development of the COVID-19 pandemic. The analysis of the factors related to the areas burdened by social problems resulting primarily from the economic exclusion revealed that poverty-burdened areas are highly vulnerable to the development of the COVID-19 pandemic. Using maps of the local R2 it was possible to visualize how the relationships between the explanatory variables (for this research-demographic, social, and environmental factors) and the dependent variable (number of cases of SARS-CoV-2) vary across the study area. Through the GWR method, counties were identified as particularly vulnerable to the pandemic because of the problem of economic exclusion. Considering that the COVID-19 pandemic is still ongoing, the results obtained may be useful for local authorities in developing strategies to counter the pandemic.