COVID-19 spatialization by empirical Bayesian model in São Paulo, Brazil.

The new Acute Respiratory Syndrome, COVID-19, has affected the health and the economy worldwide. Therefore, scientists have been looking for ways to understand this disease. In this context, the main objective of this study was the spatialization of COVID-19, thinking in distinguishing areas with high transmissibility yet, verifying if these areas were associated with the elderly population occurrence. The work was delineated, supposing that spatialization could support the decision-making to combat the outbreak and that the same method could be used for spatialization and prevent other diseases. The study area was a municipality near Sao Paulo Metropolis, one of Brazil's main disease epicenters. Using official data and an empirical Bayesian model, we spatialized people infected by region, including older people, obtaining reasonable adjustment. The results showed a weak correlation between regions infected and older adults. Thus, we define a robust model that can support the definition of actions aiming to control the COVID-19 spread.

Effective medical center finding during COVID-19 pandemic using a spatial DSS centered on ontology engineering.

The global spread of the coronavirus has generated one of the most critical circumstances forcing healthcare systems to deal with it everywhere in the world. The complexity of crisis management, particularly in Iran, the unfamiliarity of the disease, and a lack of expertise, provided the foundation for researchers and implementers to propose innovative solutions. One of the most important obstacles in COVID-19 crisis management is the lack of information and the need for immediate and real-time data on the situation and appropriate solutions. Such complex problems fall into the category of semi-structured problems. In this respect, decision support systems use people's mental resources with computer capabilities to improve the quality of decisions. In synergetic situations, for instance, healthcare domains cooperating with spatial solutions, coming to a decision needs logical reasoning and high-level analysis. Therefore, it is necessary to add rich semantics to different classes of involved data, find their relationships, and conceptualize the knowledge domain. For the COVID-19 case in this study, ontologies allow for querying over such established relationships to find related medical solutions based on description logic. Bringing such capabilities to a spatial decision support system (SDSS) can help with better control of the COVID-19 pandemic. Ontology-based SDSS solution has been developed in this study due to the complexity of information related to coronavirus and its geospatial aspect in the city of Tehran. According to the results, ontology can rationalize different classes and properties about the user's clinical information, various medical centers, and users' priority. Then, based on the user's requests in a web-based SDSS, the system focuses on the inference made, advises the users on choosing the most related medical center, and navigates the user on a map. The ontology's capacity for reasoning, overcoming knowledge gaps, and combining geographic and descriptive criteria to choose a medical center all contributed to promising outcomes and the satisfaction of the sample community of evaluators.

Multiscale Geographically Weighted Regression in the Investigation of Local COVID-19 Anomalies Based on Population Age Structure in Poland.

A growing number of various studies focusing on different aspects of the COVID-19 pandemic are emerging as the pandemic continues. Three variables that are most commonly used to describe the course of the COVID-19 pandemic worldwide are the number of confirmed SARS-CoV-2 cases, the number of confirmed COVID-19 deaths, and the number of COVID-19 vaccine doses administered. In this paper, using the multiscale geographically weighted regression, an analysis of the interrelationships between the number of confirmed SARS-CoV-2 cases, the number of confirmed COVID-19 deaths, and the number of COVID-19 vaccine doses administered were conducted. Furthermore, using maps of the local R2 estimates, it was possible to visualize how the relations between the explanatory variables and the dependent variables vary across the study area. Thus, analysis of the influence of demographic factors described by the age structure and gender breakdown of the population over the course of the COVID-19 pandemic was performed. This allowed the identification of local anomalies in the course of the COVID-19 pandemic. Analyses were carried out for the area of Poland. The results obtained may be useful for local authorities in developing strategies to further counter the pandemic.

Spatial thinking and GIS: developing and assessing student competencies

Spatial thinking is an integral skill for geography students to develop. Whilst many spatial competencies have been identified by researchers, and the merits of GIS seemingly ubiquitous in the published literature, little work has been done into how students' spatial thinking skills can be assessed. Therefore, further investigation into the relationship between spatial thinking and performance and attainment is needed. This research investigates the impact using a geographic information system (GIS) has on students' spatial thinking skills and attempts to assess the extent using a framework. This was done through the design and implementation of two GIS-based interventions. This small-scale evaluation used qualitative methods to investigate students' and teachers' views. Student work was also analysed using the framework developed for the assessment of spatial thinking skills. The findings suggest that the use of a GIS does enhance, and in most cases improves students' spatial thinking skills, but, that spatial thinking is hard to quantify and difficult to measure progress in. Another benefit that using a GIS affords is the creation of engaging, contemporary and interactive lessons, using real data, from which students derive a lot of geographical value. [ FROM AUTHOR] Copyright of International Research in Geographical & Environmental Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Efficiency and Sustainability: The Role of Digitization in Re-Inhabiting the Existing Building Stock

Cities are complex and constantly evolving systems where changing social needs have always reshaped the built environment. Considering recent evolutionary trends in housing emergencies, amplified by the COVID-19 pandemic, and environmental sustainability goals, a rethinking of the building heritage is fundamental. This article aims to promote the conversion of buildings designed initially for nonresidential uses as a process and project strategy based on energy efficiency and a holistic and integrated vision of the circular economy. The methodological approach is based on two main phases: definition of evaluative parameters for the potential reuse of a building, and integration of the evaluation system in a BIM and GIS environment. The result is a tool for rapid automatic pre-evaluation of the potential conversion of a building into a residential space. Applying the developed methodology allows for a practical approach to the significant issue of sustainable construction, with particular attention to energy improvement and the reduction of environmental impact related to the construction of new buildings. The originality of the contribution lies in the systematization of various digital technologies to provide fundamental support for managing and transforming the varied and widespread unused real estate assets in a state of abandonment and degradation. [ FROM AUTHOR] Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States.

BACKGROUND: COVID-19 is an important public health concern due to its high morbidity, mortality and socioeconomic impact. Its burden varies by geographic location affecting some communities more than others. Identifying these disparities is important for guiding health planning and service provision. Therefore, this study investigated geographical disparities and temporal changes of the percentage of positive COVID-19 tests and COVID-19 incidence risk in North Dakota. METHODS: COVID-19 retrospective data on total number of tests and confirmed cases reported in North Dakota from March 2020 to September 2021 were obtained from the North Dakota COVID-19 Dashboard and Department of Health, respectively. Monthly incidence risks of the disease were calculated and reported as number of cases per 100,000 persons. To adjust for geographic autocorrelation and the small number problem, Spatial Empirical Bayesian (SEB) smoothing was performed using queen spatial weights. Identification of high-risk geographic clusters of percentages of positive tests and COVID-19 incidence risks were accomplished using Tango's flexible spatial scan statistic. ArcGIS was used to display and visiualize the geographic distribution of percentages of positive tests, COVID-19 incidence risks, and high-risk clusters. RESULTS: County-level percentages of positive tests and SEB incidence risks varied by geographic location ranging from 0.11% to 13.67% and 122 to 16,443 cases per 100,000 persons, respectively. Clusters of high percentages of positive tests were consistently detected in the western part of the state. High incidence risks were identified in the central and south-western parts of the state, where significant high-risk spatial clusters were reported. Additionally, two peaks (August 2020-December 2020 and August 2021-September 2021) and two non-peak periods of COVID-19 incidence risk (March 2020-July 2020 and January 2021-July 2021) were observed. CONCLUSION: Geographic disparities in COVID incidence risks exist in North Dakota with high-risk clusters being identified in the rural central and southwest parts of the state. These findings are useful for guiding intervention strategies by identifying high risk communities so that resources for disease control can be better allocated to communities in need based on empirical evidence. Future studies will investigate predictors of the identified disparities so as to guide planning, disease control and health policy.

Optimisation of COVID-19 vaccination process using GIS, machine learning, and the multi-layered transportation model

COVID-19 has affected the lives and well-being of billions of citizens worldwide. While nondrug interventions have been partially effective in containing the COVID-19 epidemic, vaccination has become the most important factor in maintaining public health and reducing deaths. In this study, a model is proposed to overcome the difficulties in organising vaccination due to heterogeneous population distribution in cities and to optimise the vaccination process considering the available resources. The results of the model are of strategic importance for the control of the COVID-19. Considering the transportation structures, population and vaccine resources in the regions, a different number of clusters is formed for each city. Each cluster consists of several districts that share health resources. A hybrid approach consisting of mathematical modelling and k-means algorithm is proposed, and it reduced the difference between vaccination times of three different vaccination clusters to about 3.5 days. The results also showed that the vaccination process can be reduced from 108 days to 44 days, which meant a 40% improvement in speed for administering vaccines. In this case study, we presented a vaccination programme in which the average antibody rate of individuals does not fall below the critical-time threshold. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

An IoT system based on open APIs and geolocation for human health data analysis

Development of applications based on open API is becoming increasingly popular today. Innovative projects using these technologies provide new opportunities for real-time human health monitoring. Such opportunities are also implemented using Internet of Things (IoT), artificial intelligence (AI) and cloud computing technologies. In the study, we developed an application based on open APIs using smart gadgets and meteorological geographic information system in the process of generating a message about the dangers to human health associated with: the presence of pollen in the air (grass pollen, birch pollen and olive pollen) indicating the level of its concentration in the air;problems with air quality, if the air quality indicator exceeds the permissible standards. The addition of such functions expands the possibilities to provide timely information about potential risks and threats and, accordingly, is an "anthropo-geo-sensor-digital"prerequisite for effective decision-making, prevailing. The implementation of this IoT system has significant methodological and technological potential that can be used to improve the efficiency of Healthcare, both in extreme conditions and in conditions of sustainable existence. First of all, this is relevant during and after the COVID-19 pandemic. The system we have developed can also be seen as one of the ways to innovate in Healthcare, in the educational process in institutions of higher education and in further scientific research on this topic. Further research in this area may be related to data processing in Healthcare systems based on machine learning, deep learning. © 2023 Copyright for this paper by its authors.

Spatial Analysis of SO 2 , PM 10 , CO, NO 2 , and O 3 Pollutants: The Case of Konya Province, Turkey

Geographical information systems are frequently used in analyses of air quality based on location and time. They are also used in the creation of pollution distribution maps to determine the parameters related to air pollutants. In this study, a spatial analysis of SO2, PM10, CO, NO2 and O3 pollutants, which cause air pollution within the borders of the municipal urban areas of Konya province, was carried out for the years 2019–2020. In this context, air pollution maps were produced using the IDW interpolation method with data obtained from the National Air Quality Monitoring Network stations, which belong to the Ministry of Environment and Urbanization, in the Konya region. The results obtained were examined with maps and graphics based on the limit values found in the Air Quality Assessment and Management Regulation published by the Ministry of Environment and Urbanization. In this context, the periods of lockdown experienced during the COVID-19 pandemic were also evaluated in terms of air pollution. From the evaluation made on the values taken from the air quality stations, it can be observed that the air pollution did not violate the national limit value much in 2019 and 2020. [ FROM AUTHOR] Copyright of Atmosphere is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Offshore Construction Progress Management by Indoor GIS Positioning: Post-COVID-19 New Normal

Travel restrictions have been imposed among countries since the outbreak of the COVID-19 pandemic. Time delays, budget issues, and poor-quality control in construction projects due to the pandemic have severely affected the construction industry. To reduce the influence of the pandemic, the paper introduces an offshore construction site progress management system with a real case study. With the integration of indoor location-based service technology and image processing method, site superintendents (architect, project director, site manager, engineer) can monitor the site progress easily and pinpoint defects for further investigation and measurement. The visualization of site images together with BIM provides a digital twin platform that can help senior management quickly review the site progress, perform quality checks, and resolve discrepancies in early phases. Positioning of workers and equipment with the adoption of digital maps is a further step in sustainable management. The proposed integration provides a new concept for construction site management during a pandemic and supports the post-COVID-19 new normal in the construction industry. © 2022 International Conference on Construction in the 21st Century. All rights reserved.

Lessons learned to improve COVID-19 response in communities with greatest socio-economic vulnerabilities.

BACKGROUND: Vulnerable communities are susceptible to and disproportionately affected by the impacts of the COVID-19 pandemic. Understanding the challenges faced, perceptions, lessons learned, and recommendations of the organizations that provide services in response to COVID-19 to vulnerable communities is critical to improving emergency response and preparedness in these communities. METHODS: This study employed GIS mapping to identify the needs and assets that exist in communities in Baltimore City, where vulnerabilities related to social determinants of health and the burden of the COVID-19 pandemic were greatest. We also conducted an online survey between September 1, 2021, and May 30, 2022, to assess the COVID-19-related services provided by local organizations, challenges faced, perceptions, lessons learned, and recommendations to inform policies, programs, and funding related to improving the COVID-19 response in underserved communities. The survey was disseminated through the online Kobo Toolbox platform to leaders and representatives of organizations in Baltimore City. RESULTS: Based on GIS mapping analysis, we identified three communities as the most vulnerable and 522 organizations involved in the COVID-19 response across Baltimore City. 247 surveys were disseminated, and 50 survey responses were received (20.24% response rate). Out of these organizations, nearly 80% provided services in response to COVID-19 to the identified vulnerable communities. Challenges experienced ranged from funding (29%), and outreach/recruitment (26%), to not having access to updated and accurate information from local officials (32%). CONCLUSIONS: This research highlights critical insights gained related to the experiences of vulnerable populations and suggests ways forward to address challenges faced during the emergency response by providing recommendations for policy and program changes. Furthermore, the findings will help better prepare vulnerable communities for public health emergencies and build more community resilience.

Spatio-temporal analysis of the COVID-19 pandemic in Türkiye: results of the controlled normalization

This study investigates the spatio-temporal structure of the pandemic in Türkiye during the normalization process. An analysis has been conducted based on spatial and space–time scan statistics of the province-based numbers of confirmed COVID-19 cases during the normalization process from February 27 to May 7, 2021. The clusters affected by regional application differences has determined. The increase in cases has been observed, and the risk classes that supported the spatial relationship have been determined. Positive spatial relationships have been observed. Moran I measurements have also directly overlapped with the developments in the timeline of the COVID-19 pandemic in Türkiye. Local Moran I analysis has shown the transition of clusters from different regions to others over time. According to the results, controlled normalization has not happened as expected and the increase in the number of cases eventually led to the start of a total lockdown. Spatial and spatio-temporal analysis may show how to respond to a potential new pandemic. Regulations that vary from region to region can be meaningless depending on the spatial interaction. Decision makers may benefit in the future from these analyses, which reveal the results of experience to control current worsening scenarios. © 2022, The Author(s), under exclusive licence to Korean Spatial Information Society.

Community-Based Surveillance and Geographic Information System‒Linked Contact Tracing in COVID-19 Case Identification, Ghana, March‒June 2020.

In response to the COVID-19 pandemic, Ghana implemented various mitigation strategies. We describe use of geographic information system (GIS)‒linked contact tracing and increased community-based surveillance (CBS) to help control spread of COVID-19 in Ghana. GIS-linked contact tracing was conducted during March 31-June 16, 2020, in 43 urban districts across 6 regions, and 1-time reverse transcription PCR testing of all persons within a 2-km radius of a confirmed case was performed. CBS was intensified in 6 rural districts during the same period. We extracted and analyzed data from Surveillance Outbreak Response Management and Analysis System and CBS registers. A total of 3,202 COVID-19 cases reported through GIS-linked contact tracing were associated with a 4-fold increase in the weekly number of reported SARS-CoV-2 infected cases. CBS identified 5.1% (8/157) of confirmed cases in 6 districts assessed. Adaptation of new methods, such as GIS-linked contact tracing and intensified CBS, improved COVID-19 case detection in Ghana.

SARS-CoV-2 concentration in wastewater consistently predicts trends in COVID-19 case counts by at least two days across multiple WWTP scales.

Wastewater surveillance of SARS-CoV-2 has proven instrumental in mitigating the spread of COVID-19 by providing an economical and equitable approach to disease surveillance. Here, we analyze the correlation of SARS-CoV-2 RNA in influents of seven wastewater plants (WWTPs) across the state of South Carolina with corresponding daily case counts to determine whether underlying characteristics of WWTPs and sewershed populations predict stronger correlations. The populations served by these WWTPs have varying social vulnerability and represent 24% of the South Carolina population. The study spanned 15 months from April 19, 2020, to July 1, 2021, which includes the administration of the first COVID-19 vaccines. SARS-CoV-2 RNA concentrations were measured by either reverse transcription quantitative PCR (RT-qPCR) or droplet digital PCR (RT-ddPCR). Although populations served and average flow rate varied across WWTPs, the strongest correlation was identified for six of the seven WWTPs when daily case counts were lagged two days after the measured SARS-CoV-2 RNA concentration in wastewater. The weakest correlation was found for WWTP 6, which had the lowest ratio of population served to average flow rate, indicating that the SARS-CoV-2 signal was too dilute for a robust correlation. Smoothing daily case counts by a 7-day moving average improved correlation strength between case counts and SARS-CoV-2 RNA concentration in wastewater while dampening the effect of lag-time optimization. Correlation strength between cases and SARS-CoV-2 RNA was compared for cases determined at the ZIP-code and sewershed levels. The strength of correlations using ZIP-code-level versus sewershed-level cases were not statistically different across WWTPs. Results indicate that wastewater surveillance, even without normalization to fecal indicators, is a strong predictor of clinical cases by at least two days, especially when SARS-CoV-2 RNA is measured using RT-ddPCR. Furthermore, the ratio of population served to flow rate may be a useful metric to assess whether a WWTP is suitable for a surveillance program.

Analysis of changes in geographical factors affecting sales in commercial alleys after COVID-19 using machine learning techniques.

Social restrictions, such as social distancing and self-isolation, imposed owing to the coronavirus disease-19 (COVID-19) pandemic have resulted in a decreased demand of commodities and manufactured products. However, the factors influencing sales in commercial districts in the pre- and post-COVID-19 periods have not yet been fully understood. Thus, this study uses machine learning techniques to identify the changes in important geographical factors among both periods that have affected sales in commercial alleys. It was discovered that, in the post-COVID-19 period, the number of pharmacies, age groups of the working population, average monthly income, and number of families living in apartments priced higher than $600k in the catchment areas had relatively high importance after COVID-19 in the prediction of a high level of sales. Moreover, the percentage of deciduous forests appeared to be a important factor in the post-COVID-19 period. As the average monthly income and worker population in their 60s and numbers of pharmacies and banks increased after the pandemic, sales in commercial alleys also increased. The survival of commercial alleys has become a critical social problem in the post-COVID-19 era; therefore, this study is meaningful in that it suggests a policy direction that could contribute to the revitalization of commercial alley sales in the future and boost the local economy.

Mysterious Outbreak of Acute Neurological Syndrome in Eluru, Andhra Pradesh, India: A Post-outbreak Geo-Spatial Analysis.

BACKGROUND: An outbreak characterized by sudden-onset seizures, loss of consciousness, and complete recovery within a few hours was reported from Eluru town in Andhra Pradesh on December 6, 2020. This study was conducted to assess the environmental correlates of the outbreak using geo-mapping tools. METHODS: A post-outbreak survey was conducted among affected cases in January-February, 2021. A house-to-house survey tool collected information on demographics, clinical profile, and environmental and psychological aspects (Impact of Event Scale). Geo-mapping and news media content analyses were done using QGIS and Atlas.ti software, respectively. RESULTS: A total of 394 cases were studied. The median (interquartile range [IQR]) age of the participants was 27 (17-39) years and comprised mostly male students. There was no clustering of cases within 48 hours of illness onset in the spatial analysis. Loss of consciousness was the first (50.7%) and the most common symptom. All cases were taken to a health facility and were discharged after a median duration of 48 minutes. COVID-19-related and environmental practices were not associated with the clinical manifestations. Awareness about pesticides was low. The outbreak reportedly had a psychological impact on 24.4% of the participants. The most common co-occurring themes in the news media analysis were water contamination and pesticides. CONCLUSION: The geo-spatial analysis did not find case clustering or points of convergence during the incubation period. The geo-locations did not distribute around water bodies or suspected landmarks although news media projected water contamination and pesticides as probable causes of the outbreak.

Evaluating the efficiency of relief centers in disaster and epidemic conditions using multi-criteria decision-making methods and GIS: A case study.

Disaster response refers to any action taken and performed by disaster team managers after and during a disaster. According to the prevalence of the coronavirus and the unpredictability of the behavior of this virus, the capacities of hospitals and medical centers have been overshadowed by this epidemic. Governments have set up temporary rehabilitation centers to control the epidemic, make better use of resources, and quarantine COVID-19 patients. The Tehran (Iran) Disaster Management Organization has designated centers to house the injured and displaced during natural disasters such as floods and earthquakes. In this study, the efficiency and sustainability of the evaluation criteria of selected disaster management centers were evaluated in three scenarios: disaster conditions (natural disasters), epidemic conditions, and disaster-epidemic situations. Firstly, the research criteria were classified by experts using the fuzzy Delphi method and weighted using the triangular fuzzy aggregation method. In addition, the criteria are evaluated as information layers in the Geographic Information System (GIS) and the relief locations determined by the disaster management are evaluated against the research criteria. By forming a decision matrix, the alternatives in all three scenarios were prioritized using the PROMETHEE Method and evaluated in terms of efficiency. As a results, the main ways criterion shown with an impact factor of 13% among the evaluation criteria of centers in disaster situations. Additionally, the security criterion with an impact factor of 22% among the evaluation criteria of centers in epidemic conditions achieved the most important criteria in the PROMETHEE ranking.

Bayesian Network Integration with GIS for the Analysis of Areas Vulnerable to the Outbreak of COVID-19 in Bangkok, Thailand

The COVID-19 pandemic prompted a search for a new method of preventing the spread of this virus. This study established a model of the areas in Bangkok which were vulnerable to the COVID-19 pandemic by using a combination of the Bayesian network (BN) and the geographic information system (GIS). The model was developed using a data-driven approach and was evaluated with 10-fold cross validation and ROC analysis. The results demonstrated that the proposed method effectively predicted the vulnerability of disease outbreak. The most vulnerable areas to the pandemic were around the center and in the west of Bangkok, while the areas of low vulnerability were found in the north and east of the city. Population density and the aerosol index were highly influential factors in the outbreaks, affirmed by sensitivity analysis. Furthermore, the model used to conduct a scenario analysis resulted in the identification of vulnerability management strategies. © Geoinformatics International.