ARTICLE | doi:10.20944/preprints202212.0024.v1
Subject: Computer Science And Mathematics, Probability And Statistics Keywords: Positive Poisson distribution, Under dispersion, Bayesian analysis, prediction, index of infectivity.
Online: 1 December 2022 (10:25:33 CET)
Background. COVID-19 efforts were often ineffective in controlling the spread of the pandemic. Identifying ineffective controls during a pandemic is thus vital. Method. Utilizing publicly available data on COVID deaths in the counties of US states, we create an index to capture and interpret ineffectiveness in the efforts to reduce the spread of the pandemic in US counties. This index is based on the Intervened Poisson Distribution (IPD) introduced originally by Shanmugam. Motivation for the research idea occurred while we noticed the data dispersion of the COVID deaths is smaller than the average only in some counties. Under-dispersed data is common in statistical modeling. A novel approach we adapted in this article includes the estimation of an intervention parameter estimated through iterative non-linear optimization. Results. Twenty-five counties in California, Idaho, Minnesota, Mississippi, Montana, Nebraska, North Carolina, North Dakota, Texas, and Utah were found to be ineffective in controlling for fatalities based on the expected probability distribution. A review of the policies enacted in these areas would provide insight into ineffective prevention efforts, and some of these issues are documented in current literature. Conclusion. The IPD index an innovate way to document efficacy of interventions during pandemics. The IPD may identify ineffective efforts prior to statistical models intended to evaluate efficacy of efforts.