Indexing Inefficacy of Efforts to Stop Escalation of COVID Mortality

Abstract

Background: COVID-19 efforts were often ineffective in controlling the spread of the pandemic. Thus, identifying ineffective controls during a pandemic is vital. Method: Utilizing publicly available data on COVID deaths in the counties of US states, we created 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 when we noticed the data dispersion of the COVID deaths was 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 the current literature. Conclusions: The IPD index an innovate way to document efficacy of interventions during pandemics.

Keywords

• positive poisson distribution
• under dispersion
• bayesian analysis
• prediction
• index of infectivity