Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties

Abstract

As COVID-19 run rampant in high-density housing sites, it is important to use real-time data in tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research appropriately analyzed the disparities between spatial-temporal clusters, expectation maximization clustering (EM), and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to Southeast Texas analysis in Geographically Weighted Regression (GWR) modeling. The sensitive period took place in the last two quarters in 2020. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental 14 impact’s indices to perform principal component analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, age structure, natural supply, economic condition, air quality, and medical care. We established the GWR model to seek the sensitive factors. The result shows that population, hospitalization, and economic condition are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility, and satisfy for the need of emergency operations plan (EOP).

Temporal Dynamics of Socioeconomic Inequalities in COVID-19 Outcomes Over the Course of the Pandemic—A Scoping Review

“…In general, more deprived counties were at higher risk of becoming a hotspot; once a hotspot appeared, the temporal increase of cases was steeper with higher deprivation. Growing inequalities in COVID-19 incidence rates were also described in analyses concerning income [ 53 , 56 , 69 ], education [ 38 ], or employment [ 69 ].…”

Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States

“…Moreover, the presence of local multicollinearity can cause instability of the parameter estimates [ 30 ]. To date, GWR has been applied to several COVID-19 morbidity and mortality studies [ 31 , 32 , 33 , 34 , 35 ]. However, to our knowledge, GWR has never been used in the geospatial modeling of COVID-19 vaccination rates.…”

Understanding the spatial heterogeneity of Covid-19 vaccination uptake: a case study of England

“…Moreover, this relationship exhibits considerable spatial heterogeneity and local effects. Overall, the younger groups (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39) are associated with lower vaccination rates than the elderly, whilst higher car ownership or better accessibility to vaccination services lead to higher vaccination uptake rates. On the other hand, the more deprived areas are found to be related to a higher vaccination rate than the less deprived.…”

“…In addition, the local multicollinearity can lead to instability of the local parameter estimates. To our knowledge, GWR has been used in multiple research studies to describe the degree to which socioeconomic factors are associated with the COVID-19 morbidity [35][36][37][38] and mortality [39], and also COVID-19 vaccination rates [13].…”