Spatial diffusion of COVID-19: An econometric-based approach.
Abstract
An understanding of how infected-susceptible populations interact is
critical to identify underlying causal factors and disease transmission
patterns of infectious diseases. Disease transmission patterns are
dynamic, non-linear, and spatially complex. This anisotropic
characteristic of disease spread necessitates the ideal solution to be
sensitive to the geographic context. A Spatial Diffusion Model (SDM) to
predict interaction potential and COVID-19 risk probability is developed
by adapting the Newtonian gravity model. This novel approach overcomes
the limitations of existing epidemiological studies by characterizing
the behavioral patterns of the infected population to model the
spatiotemporal transmission of disease across the geographic space. The
proposed model is robust as it couples a multicriteria behavioral
pattern to enhance predictive capability. The model shows an 83.74%
correlation with the observational COVID-19 case data. The highest risk
patterns for COVID-19 are predicted in the neighborhoods of New York
City (NYC), exhibiting clustered socioeconomic disparities along with
racial and ethnic heterogeneity. Policymakers can use these results to
identify neighborhoods at high risk for becoming hot spots; efficiently
match community resources with needs, and ensure that the most
vulnerable have access to equipment, personnel, and medical
interventions. This study emphasizes the need for improved spatial
epidemiological models including enhanced depictions of human activity
patterns and the need to integrate spatial data with advanced
mathematical models.