Spatial diffusion of Omicron in Texas: Predictable or Random?

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Richard C. Jones, Ph.D.


The impacts of Covid-19 on society and the environment have now, three years from its first appearance, ballooned across the scientific literature. But our knowledge of where it originated, and why it spread as it did, has advanced much more slowly. Models of spatial diffusion at national level have been applied to past pandemics with some success, but not yet to Covid-19. One exception is a study of the spread of the original strain of Covid-19 in Texas between March and September 2020. This spread adhered to spatial diffusion theory: first hierarchical, that is into metropolitan areas in proportion to their population sizes; later contagious, that is into adjacent towns based on their proximity to metropolitan areas to which they were tributary; and finally hierarchical once more, into the most isolated towns based on their urban/ rural status. This process took six months. By contrast, the Omicron variant, in less than two months (December 2021 and January 2022), reached case levels three times those of the original Covid surges. The current study focuses on how Omicron diffused across Texas over the period November to February 2021-22. Daily case numbers for Covid-19 were available for all 254 counties from the Texas Department of State Health Services over this period, and the Omicron variant was identifiable from these data. This analysis will reveal whether the Omicron spread across the counties of Texas was predictable, partially predictable, or unpredictable, based on county population, distance to their regional metro, or the gravity concept (the ratio between the two). These insights will be of value if other such variants appear on the horizon for the US and Texas, enabling local health officials to anticipate the onset in their communities.

Keywords: covid-19, Omicron variant, spatial diffusion, gravity model, Texas

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How to Cite
JONES, Richard C.. Spatial diffusion of Omicron in Texas: Predictable or Random?. Medical Research Archives, [S.l.], v. 10, n. 10, oct. 2022. ISSN 2375-1924. Available at: <>. Date accessed: 19 june 2024. doi:
Research Articles


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