COVID-19 Progression Timeline and Effectiveness of Response-to-Spread Interventions across the United States

Pai Liu; Payton Beeler; Rajan K Chakrabarty

State-wise Risk Assessment. Figure 6 shows an assessment of the vulnerability of each state against COVID-19 epidemics. Our assessment was done on four core factors, (a) urgency, (b) percent of uninsured local population, (c) local liquid-asset-poverty rate, and (d) age-weighted mortality rate of the infected patients. Urgency is quantitated as the date of a local epidemic peak per the SEIR model; it identifies how much time is left for the local policy makers and disease control professionals to design and implement effective interventions. The percent of uninsured population is negatively related to the overall willingness of the local resident to seek out diagnosis and quarantine as and when the early symptoms of COVID-19 arise (32). The percent of liquidasset-poverty is the fraction of local households that lack savings to sustain above poverty level for three-months (33) . The implication of lengthy societal shutdown is catastrophical to these households or individuals because of the potential disruption in their income. The age-weighted mortality rate quantifies the magnitude of consequence should every intervention on disease containment fail and the situation evolve to disrest. A summary report (4) of the COVID-19 cases by Chinese Center for Disease Control and Prevention has identified that the case-fatality rate strongly correlates with patients' age. The overall fatality rate for all confirmed cases in China is 2.3% (1023 of 44672 cases) (4); however, patients aged ≥ 80 years and 70-79 years have case fatality rate of 14.8% (208 of 1408 cases) and 8.0% (312 of 3918 cases), respectively. In our analysis, the age-weighted mortality rate for a state was calculated using age-specific fatality rate reported in Ref. (4) in conjunction with state-wise demographic composition (19) . Figure 6 (a) shows how each state is expected to be affected with regard to the percent of uninsured population and the liquid-asset-poverty rate. A positive correlation can be observed in the dataset, since the individuals who live on paycheck-by-paycheck are less likely to be enrolled in medical insurance. The states scattered in qudrant II, such as Texas, Georgia, Oklahoma, Florida, and Mississippi, are vulnerable to the impact of COVID-19 due to their high liquid asset poverty rate. Among these states, Texas is especially notable because of the sheer amount of uninsured . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.