Spatial Distribution Of COVID-19 Research Articles

Spatio-Temporal Modeling of COVID-19 Spread in Relation to Urban Land Uses: An Agent-Based Approach

This study aims to address the existing gaps in evidence regarding spatio-temporal modeling of COVID-19 spread, specifically focusing on the impact of different urban land uses in a geospatial information system framework. It employs an agent-based model at the individual level in Gorgan, northeast Iran, characterized by diverse spatial and demographic features. The interactions between human agents and their environment were considered by incorporating social activities based on different urban land uses. The proposed model was integrated with the susceptible–asymptomatic–symptomatic–on treatment–aggravated infection–recovered–dead epidemic model to better understand the disease transmission at the micro-level. The effect of various intervention scenarios, such as social distancing, complete and partial lockdowns, restriction of social gatherings, and vaccination was investigated. The model was evaluated in three modes of cases, deaths, and the spatial distribution of COVID-19. The results show that the disease was more concentrated in central areas with a high population density and dense urban land use. The proposed model predicted the distribution of disease cases and mortality for different age groups, achieving 72% and 71% accuracy, respectively. Additionally, the model was able to predict the spatial distribution of disease cases at the neighborhood level with 86% accuracy. Moreover, findings demonstrated that early implementation of control scenarios, such as social distancing and vaccination, can effectively reduce the transmission of COVID-19 spread and control the epidemic. In conclusion, the proposed model can serve as a valuable tool for health policymakers and urban planners. This spatio-temporal model not only advances our understanding of COVID-19 dynamics but also provides practical tools for addressing future pandemics and urban health challenges.

Spatial Planning as an Explanatory Factor in the Spatial Distribution of COVID-19 in Kampala City

Spatial Planning as an Explanatory Factor in the Spatial Distribution of COVID-19 in Kampala City

Spatial and Temporal Analysis of 2019 Novel Coronavirus (2019-Ncov) Cases in Selangor, Malaysia

The COVID-19 pandemic continues to wreak devastation on public health systems worldwide, particularly in Selangor, Malaysia, COVID-19 was reported from October 2020 to October 2021 at prevalent rate. In order to control and prevent the spread of this pandemic, which is already underway, there is need to comprehend the spatial dimension of this disease. Therefore, the purpose of this study was to describe the patterns of COVID-19 virus transmission in the state of Selangor. Methods: Using a Geographic Information System (GIS), and the Moran’s Index (MI), spatial distribution of COVID-19 across the entire mukim was mapped and spatial statistical analysis was carried out with indications of local spatial correlations. Results: The finding revealed that the clusters were concentrated in the western and southern regions (Global Moran's I = 0.468, p = 0.05, Z = 7.01) of the state of Selangor, thus, this research provides important information on the regional distribution and temporal dynamics of COVID-19. Conclusion: An assessment of COVID-19's geographic spread can help enhance health care programs and resource allocation in Malaysia, specifically Selangor where the COVID-19 is pandemic.

Socio-Economic and Demographic Factors Associated with the Spatial Distribution of COVID-19 in Africa.

Corona is a disease that affects the whole world. Countries with weak economies are specifically more vulnerable. A proper understanding of COVID-19 spreading, identifying the high-risk areas, and discovering factors influencing the spread of the disease are crucial to improving disease control. This study evaluates the geo-statistical distribution of COVID-19 to identify critical areas of Africa using spatial clustering pattern analysis. In addition, the spatial correlation between infected cases and variables such as the unemployment rate, gross domestic product (GDP), population, and vaccination rate is calculated using Geographically Weighted Regression (GWR) analysis. The hot-spot map showed a statistically significant cluster of high values in southern and northern Africa. Moreover, the outcome of the GWR analysis revealed the GDP and population had the most significant correlation with the spreading of COVID-19, with Local R2 values of (0.01-0.99) and (0-0.89), respectively.

Spatial distribution of COVID-19 in Ethiopia - geospatial analysis

Background: COVID-19 was the devastating worldwide public health problem in recent years. COVID19 disease affecting large populations in different continents of the world starting on 11 March 2020. This study will be useful to give information on geographical surveillance of COVID-19 in Ethiopia, to know regions of high or low rates of COVID 19, to give evidence as baseline data for future research on COVID-19 in Ethiopia, and to predict the prevalence of COVID-19 from the untested population in different regions of Ethiopia using the art geospatial techniques. Methods: We have used secondary data collected from March 13, 2020, to November 23/ 2021 From COVID19 – Ethiopia - Ethiopian Health Data. ArcGIS 10.3 and SaTscan software were used for spatial analysis and geographical analysis respectively. Results: A high prevalence of COVID-19 was found in Addis Ababa (67.36%) and Oromia (13.85%). Spatial autocorrelation analysis indicated that the spatial distribution of COVID-19 is nonrandom (Moran’s Index: 0.134297, P-value: 0.052384). In spatial scan statistical analysis, eight clusters were identified and the higher rates of COVID-19 were observed in the Afar (RR= 1.80, p<0.001) and Addis Ababa (RR= 50.33, p<0.001) regions. Conclusion: The spatial distribution of COVID-19 cases in Ethiopia was not random. Hot spot research revealed a significant prevalence of COVID-19 patients in the Addis Ababa, Dire Dawa, Harari, and Oromia regions. For the reduction of COVID-19 infections in Ethiopia, preventative and control activities should be extended to high-risk locations.

Spatial Distribution of COVID-19 and Comorbidities in New England

The COVID-19 pandemic created new barriers and challenges to accessing primary care services, particularly among older adults who already faced barriers related to access to care, including transportation, health literacy, and self-management support. Nova Southeastern University South Florida Geriatric Workforce Enhancement Program (NSU SFGWEP) partnered with primary care clinics to conduct wellness calls to older adult patients identified through clinic EHR. The wellness calls’ objectives were to 1) discuss COVID-19 protective measures; 2) assess wellness needs and access to care barriers; and 3) screen for telehealth support. From September 2020 to February 2021, the team (including medical students, public health students, and SFGWEP staff) contacted 200 patients via telephone and conducted a comprehensive wellness survey developed by the study team, informed by validated surveys. Among the 200 patients called, 60% (n=34) were very concerned about the Covid-19 pandemic, 33% (n=34) reported often feeling isolated from others, and 20% (n=34) expressed difficulty getting medical care. A smaller subset of patients reported concern about limited COVID-19 testing (n=1), lack of knowledge about seeing their provider via telehealth (n=7), lack of face masks (n=1), and challenges with obtaining medication refills (n=1). Following wellness calls, the NSU SFGWEP team provided education, referred to clinical resources, and, for low-income patients with access to care challenges, provided Samsung Tablets (n=50) with peer training to enable telehealth.

Spatial Distribution of COVID-19 Under the Influence of Environmental Factors: Correlation and Regression Analysis

Close monitoring of morbidity and mortality from COVID-19 has become a “supplier” of large amounts of spatially distributed information. This, in turn, has led to the creation of many models for the development of a pandemic. We used official information on the incidence and mortality of COVID-19 for 24 regions (oblasts and republics) of the Volga basin of the Russian Federation. We performed multiple regression analysis to test the significance of the impact of 19 social, economic and environmental indicators. 10 parameters turned out to be statistically significant for the number of cases, among which social indicators make a significant contribution (63.19%). For the indicator “mortality from COVID-19”, we identified 15 statistically significant factors. At the same time, we note the prevalence of socio-economic parameters (58.4%) over the environmental ones (31.2%). Among the analyzed 19 indicators, there were no such indicators with the help of which it is possible to quickly influence the incidence and mortality from COVID-19. Vaccination of the population is still a topical remedy. Nevertheless, we consider the very approach to the study of spatially distributed information to be successful. By combining the efforts of infectious disease specialists, systemologists, ecologists and other specialists, one can hope to create a workable (predictive and manageable) model for the development of COVID-19.

山东省新型冠状病毒肺炎疫情空间分异

山东省新型冠状病毒肺炎疫情空间分异

Modelling and mapping of the COVID-19 trajectory and pandemic paths at global scale: A geographer’s perspective

AbstractIn December 2019, the virus SARS-CoV-2 responsible for the COVID-19 pandemic was detected in the Chinese city of Wuhan. The virus started to spread from China and dispersed over the rest of the world. In March 2020, WHO (World Health Organization) declared COVID-19 a pandemic. The transmission path of the pandemic was accelerated by different types of transportation. With complete analysis of spatial data, population density, types of traffic networks, and their properties, the spatial distribution of COVID-19 was estimated. GIS (Geographical Information System), numerical methods, and software for network analysis were used in this research to model scenarios of virus distribution on a global scale. The analyzed data included air, railway, marine, and road traffic. In the pandemic research, numerous models of possible trajectory of viruses can be created. Many have a stochastic character. This study includes all countries in the world affected by the COVID-19 up to date. In this study, GIS methods such as buffer, interpolations, and numerical analysis were used in order to estimate and visualize ongoing COVID-19 pandemic situation. According to the availability of new data, trajectory of virus paths was estimated. On the other hand, sparsely populated areas with poorly developed and small traffic networks (and isolated island territories) tend to be less or not affected as shown by the model. This low-cost approach can be used in order to define important measures that need to be addressed and implemented in order to successfully mitigate the implications of COVID-19 not only on global, but local and regional scales as well.

Facing a Second Wave from a Regional View: Spatial Patterns of COVID-19 as a Key Determinant for Public Health and Geoprevention Plans.

Several studies on spatial patterns of COVID-19 show huge differences depending on the country or region under study, although there is some agreement that socioeconomic factors affect these phenomena. The aim of this paper is to increase the knowledge of the socio-spatial behavior of coronavirus and implementing a geospatial methodology and digital system called SITAR (Fast Action Territorial Information System, by its Spanish acronym). We analyze as a study case a region of Spain called Cantabria, geocoding a daily series of microdata coronavirus records provided by the health authorities (Government of Cantabria—Spain) with the permission of Medicines Ethics Committee from Cantabria (CEIm, June 2020). Geocoding allows us to provide a new point layer based on the microdata table that includes cases with a positive result in a COVID-19 test. Regarding general methodology, our research is based on Geographical Information Technologies using Environmental Systems Research Institute (ESRI) Technologies. This tool is a global reference for spatial COVID-19 research, probably due to the world-renowned COVID-19 dashboard implemented by the Johns Hopkins University team. In our analysis, we found that the spatial distribution of COVID-19 in urban locations presents a not random distribution with clustered patterns and density matters in the spread of the COVID-19 pandemic. As a result, large metropolitan areas or districts with a higher number of persons tightly linked together through economic, social, and commuting relationships are the most vulnerable to pandemic outbreaks, particularly in our case study. Furthermore, public health and geoprevention plans should avoid the idea of economic or territorial stigmatizations. We hold the idea that SITAR in particular and Geographic Information Technologies in general contribute to strategic spatial information and relevant results with a necessary multi-scalar perspective to control the pandemic.

Mapeamento da COVID-19 no Estado da Paraíba: Elementos para a espacialização e análise em ambiente SIG

O Brasil foi um dos primeiros países da América Latina com casos de Covid-19, academicamente, desde o início de janeiro de 2020 observamos no Portal de Periódicos da Capes o crescimento de produções sobre esse tema. Contudo, percebemos a ausência de análises sobre o Estado da Paraíba, o que nos proporcionou a curiosidade de compreendermos como esse fenômeno vem de manifestando e de que forma o uso de um Sistema de Informações Geográficas poderia contribuir para essa reflexão. Nesse sentido, o presente artigo apresenta por objetivo realizar uma análise sobre o processo de disseminação e distribuição dos casos e óbitos de Covid-19 no Estado da Paraíba através de um Sistema de Informações Geográficas. Para o alcance desse objetivo realizamos a busca de boletins epidemiológicos na Secretaria Estadual de Saúde dentre o período de 31 de março até 16 de Maio de 2020. Esses dados foram sistematizados a partir dos casos e óbitos existentes no Estado através do software QGIS, no qual geramos um total de nove mapas temáticos. Nossos resultados indicam pelo menos três tendências da pandemia no Estado: a) distribuição espacial da Covid-19 na Paraíba segue o fluxo na BR-230; b) existência de três principais núcleos de disseminação: região metropolitana de João Pessoa; Campina Grande e as cidades de Sousa e Patos; c) padrão elevado de contaminação entre profissionais da saúde. Tais resultados nos apresenta outra demanda, a necessidade de entendermos como numa escala intra-urbana a pandemia vem se manifestando, especialmente nos três principal polos de disseminação do Estado.