Using spatial analysis to study COVID 19

Compared to what we often notice, the COVID-19 pandemic proclamation and accompanying lockdown at various levels, from the local city level to the national level, have a significantly larger influence on our immediate environment. Despite the fact that data is readily available, research employing various geospatial analysis tools to assess the effects of this ongoing epidemic and enforced lockdowns are not in depth. The scientific community and practitioners, however, can profit greatly from the use of spatial analysis tools, techniques, and geographic information system (GIS) platforms. These advantages include clearer and easier-to-understand visualization, real-time tracking of confirmed and reported case numbers, contact tracing, spread direction, as well as the ability to pinpoint hotspots and stop the spread of the disease inside communities. This pandemic did not usher in the use of GIS in matters pertaining to public health. Many research employed it in the past, long before computerized GIS software was created in the middle of the 1960s. Since then, GIS has been widely used for illness pattern analysis, visualization, and detection. According to a recent analysis, out of the 869 papers that were included, 14% of the studies used GIS techniques for mapping, particularly for infectious disease mapping.

By enforcing lockdowns and doing contact tracing, various GIS software and techniques have been implemented and broadly accepted to stop the transmission. The Johns Hopkins University's web-based, near-real-time COVID dashboard is the best illustration of how GIS has been used in this pandemic.

Later, other municipal and regional governing organizations as well as the WHO took the same course. During this pandemic, the internet dashboards have been a vital source of information. Although early studies using or employing GIS methodologies were more concerned with visualizing or contact tracing, when more data started to become available, they went on to spatial analysis including social, economic, environmental, and more advanced analytical tools.

Cultural and Socioeconomic

Along with the booming clinical and public health research in response to the coronavirus pandemic, social scientists are also repurposing existing studies, methodologies, and data to comprehend how people are reacting to the pandemic and its effects on socioeconomic and cultural contexts around the world.

Numerous research using GIS and emphasizing socio-economic and cultural elements of COVID-19 have been found. GDP, demographic and household statistics, population density, and accessibility data were all employed in the research. Several scholars looked at the spatial pattern of COVID-19 emergence across various socio-cultural contexts using geospatial tools and techniques and data from the social vulnerability index that was already available. Population density diseases, along with poverty and unemployment rates, are the key variables related with a higher COVID-19 death rate, according to studies that created dot density and choropleth maps of COVID-19 cases.

Using a regression model-based spatio-temporal analysis, various research found a correlation between the spatial distribution of socioeconomic indicators and the temporal advancement of the pandemic. They found that the incidence of COVID-19 is closely correlated with per capita GIS and availability to public transportation. Another study examined the relationship between virus infection and social vulnerability using a similar analytical tool and taking into account socioeconomic data at the county level, demographic information, minority and disability status, language, and housing and transportation database. The study came to the conclusion that language and minority status are strongly related to the rise of COVID-19 instances. Principal component analysis (PCA) and hotspot analysis in ArcGIS were used by Kim and Bostwick (2020) to analyze the relationship between racial disparity and COVID-19 mortality. According to the study, African American communities in the USA had the highest rate of COVID-19-related fatalities. Regression analysis and ArcGIS were also utilized in other studies to look at the spatial patterns of the COVID-19 in connection to socioeconomic factors. Regression analyses were performed in this work to examine the hypotheses of a positive association between COVID-19 incidence and death rates and socioeconomic characteristics in the GIS. GIS was utilized to map the spatial aspects and inequalities between urban and rural areas.

In order to evaluate the viability of social distance and accessibility study of a certain age group, proximity and hotspot analysis in GIS has become a popular geospatial analysis technique. Gibson and Rush determined the separation between each dwelling's closest neighbors in order to pinpoint those that are unable to properly use social distance. Dryhurst utilized a GIS to map the mean risk perception of the COVID-19 in 10 nations and created a global risk perception index (RPI) using a linear regression model. Sarkar reclassified Bangladesh's administrative divisions using ArcGIS and multicriteria analysis-based pairwise comparison in order to account for COVID-19 vulnerability. To determine the kind and level of geographical clustering and scatter plots of socio-economic indicators, Cavalcante and Abreu used Moran's I and LISA. Similar to this, spatial connections between the density of constructed historical resources and Airbnb listings were found using the Exploratory Spatial Data Analysis (ESDA) technique. They came to the conclusion that the distribution of Airbnb listings exhibits some spatial autocorrelation based on the computation and mapping.

Surveilling the Health Population

An immense number of public health studies have been carried out from the first case in Wuhan to the worldwide pandemic to assist the policymakers in understanding how to manage the current and future public health responses. Nine papers in total were found to address public health concerns and make use of GIS. A multicriteria decision making index was created by several research to evaluate the risk and robustness of the current healthcare system. Requia built a geodatabase with information on land use, income, population, health, the number of hospital beds, and personnel at the municipal level using GIS techniques. They estimated that Brazilian municipalities will lack 17 beds. Similar to this, Jovanovi used ArcGIS-based network analysis for accessibility and resilience mapping to create a worldwide index made up of 57 indicators.

To determine whether there is any correlation between the age of orthopedic doctors and COVID-19 confirmed cases, GIS was employed. According to the study, there is a geographic correlation between the number of patients and surgeons in a state. In a related study, Ruthberg examined the possible risk of COVID-19 for otolaryngologists over the age of 60. They produced a heat map to display the potential risk zone by state using QGIS. According to the study, the riskiest states were New York, New Jersey, Massachusetts, and Michigan based on the ratio of confirmed cases to all ENT cases combined. To determine the geographic accessibility to COVID-19 specialist medical facilities in Ghana, Kuupiel used ArcGIS to compute the distance from the primary healthcare (PHC) clinic and to the closest health facility. According to the analysis, the current typical travel distance and duration to PHC are far greater than the generally acknowledged requirements.

In order to predict and estimate the number of patients and fatalities at the end of the pandemic, Ahmadi created an epidemic prediction model for spatial-temporal analysis. According to the analysis, Iran will have roughly 7900 and 4620 deaths from May 13 to June 1, 2020, respectively, after which the curve would flatten. Studies also used georeferencing for COVID-19 epidemiological analysis. All of the COVID-19 instances were first geocoded for these studies, following which they were connected to the county layers in ArcGIS by administrative codes before applying LISA. This study found that there is significant regional heterogeneity in China and that the spatial clustering is not random. An epidemiological tool based on wastewater (WBE) was suggested as a surveillance method to keep track of the COVID-19 pandemic. The SWMM modeling environment and ArcGIS were utilized in the study to execute the GIS-based hydraulic model and network analysis. The findings strongly supported the use of WBE as a quick and effective technique to monitor COVID-19, which the authors claimed might be used in conjunction with clinical testing to save billions of dollars.

Geospatial analysis of COVID-19

For these investigations, all of the COVID-19 cases were first geocoded, and then, before applying LISA, they were linked to the county layers in ArcGIS by administrative codes. According to this study, China has high regional variability and its geographical clustering is not random. As a way to monitor the COVID-19 pandemic, a wastewater-based epidemiological technique (WBE) has been proposed. The study's GIS-based hydraulic model and network analysis were implemented using the SWMM modeling environment and ArcGIS. The researchers suggested that the results might be utilized in conjunction with clinical tests to save billions of dollars by using WBE as a quick and efficient method to monitor COVID-19.

Iran, South Korea, Italy, Germany, Spain, China (Hubei), and France are the top nations with increased outbreak potential, according to a global study that used the online Rasch Modeling Algorithm to assess geographic hazards of COVID-19 transmission. Other research used GIS-based origin, destination, and frequency mapping of the public transportation system from Wuhan to examine the spatial dissemination of COVID-19. According to the study, there is a direct correlation between the rise in COVID-19 instances and the frequency of public transportation leaving Wuhan. Other research used time-series analysis and plots to illustrate the regional and temporal variance of COVID-19 cases in China. They also used spatial analysis to explain the function of case importation in transmission in cities all throughout China.

Another international investigation employed geographic information systems (GIS) to illustrate the spread of air passengers from Wuhan and the global infected traveler ratio. The spatial pattern analysis research also involved an intriguing function for geo-referencing of confirmed instances. These investigations analyzed the areas with a high risk of transmission and quickly pinpointed such areas. Similar to this, human movement data was used to study the spread and containment of COVID-19 cases using a generalized linear regression model. The spatial dispersal pattern of COVID-19 trajectory with or without the Wuhan travel ban was modelled in this study using spatial distribution mapping of travel movements from Wuhan to each province. They discovered that the COVID-19's arrival in other places was delayed by around three days due to the Wuhan shutdown.

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