The spatial diffusion of covid-19 in Texas

ABSTRACT

The predominant narrative of covid-19 emphasizes its unpredictable origins, virulence, human targets, and spatial targets. Its international spread may be erratic, but at the regional level, diffusion research has been able to forecast its spread with some accuracy. This study reviews research showing how infectious disease spread over space may be predicted dependent upon a place’s size, distance from large cities and disease entry points, population density, and SES. Based on these characteristics, this analysis models the arrival of covid-19 in each of the 254 counties of Texas between March 6 (the index case) and 1 September 2020, using Texas Department of State Health Statistics data. A temporal gravity model, based solely on the population of a place divided by distance to the dominant city of its tributary region is able to explain two-thirds of the variation in arrival dates. For specific places that the model cannot predict well, the examination of their residuals suggests other variables that substantially improve predictions. This model and procedure serve local planners in the case of a resurgence of the virus or another variant of it in Texas.

KEYWORDS:

• Covid-19
• spatial diffusion
• temporal gravity model
• residual analysis
• Texas

Acknowledgements

The author would like to acknowledge the stimulus he received from working with his MA student, Jamie Gillis, given our invigorating conversations and her own thesis research on this topic. The insightful comments from the Social Science Journal Co-editor, and an SSJ reviewer, are much appreciated.