Abstract
Wellness depends on health and that, in turn, depends on the absence of disease. Analogous models based on physical laws have long been utilized by researchers to understand epidemic expansion in urban communities. Perhaps the most significant of this class is the gravity model in which population size is equated with planetary mass and distance between cities to that separating planets. While the model assumes homogeneity among different bodies, cities or planets, in epidemiology the likelihood of disease spread may depend on other heterogeneous, non-constant factors. The study used a public dataset of H1N1 Influenza in 2009 as the focus. A natural log regression was applied in an attempt to sort the relative importance of gravity model variables as predictors of influenza occurrence and diffusion. It was found that while the model population size serves as a general predictor of disease expansion that distance failed as an indicator of disease dynamics. Furthermore, findings from the study show that disease progression was irregular and not, as one might expect from the gravity model, consistent in space or over time. The study concludes that the gravity model may serve only as a coarse predictor of disease expansion over time. By extension, this raises similar questions about other models in which homogeneity between populations or network of populations is assumed. Key words: Gravity model, H1N1influenza, regression, spatial epidemiology.
Highlights
In February, 2020, the Wellcome Trust (2020) called for a program of universal data sharing as COVID-19 expanded from a local to a regional epidemic and became a global pandemic
Some are based on analogies to physical laws including, in a partial list, the gravity model, another based on radiation diffusion (Simini et al, 2012), and, more recently, a third based on Ohm's 1827 law of electricity (Sallah et al, 2017)
We assessed a determination of fit by transforming the gravity model to a linear form and calibrated its variables through a multivariate linear regression applying a natural logarithmic transformation To test the applicability of a multifactorial regression in this program we applied a Durbin-Watson test resulting in a very robust 1.985 result, admirably close to the accepted benchmark for larger data sets
Summary
In February, 2020, the Wellcome Trust (2020) called for a program of universal data sharing as COVID-19 expanded from a local to a regional epidemic and became a global pandemic. A series of publicly available, continuously updated ―dashboards‖ providing real-time, continuously updated data on viral incidence were developed. These included both global surveillance programs like the Johns Hopkins University dashboard (https://coronavirus.jhu.edu/map.html) as well as more dedicated, national or provincial data sites, Whatever the scale or resolution all included maps of viral incidence as well as the underlying data used in their construction. Some analogize epidemic and pandemic dynamics through reference to natural phenomena like
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