Selected article for: "predictive factor and regression analysis"

Author: Robertson, L. S.
Title: COVID-19 Confirmed Cases and Fatalities in 883 U.S. Counties with a Population of 50,000 or More: Predictions Based on Social, Economic, Demographic Factors and Shutdown Days
  • Cord-id: rci3283g
  • Document date: 2020_6_26
  • ID: rci3283g
    Snippet: The spread of the COVID-19 virus is highly variable among U.S. counties. Seventeen factors known or thought to be related to spread of the COVID-19 virus were studied by Poisson regression analysis of confirmed cases and deaths in 883 U.S. counties with a population of 50,000 or more as of May 31, 2020. With little exception, each factor was predictive of incidence and mortality. The regression equation can be used to identify priority locations for preventive efforts and preparation for medical
    Document: The spread of the COVID-19 virus is highly variable among U.S. counties. Seventeen factors known or thought to be related to spread of the COVID-19 virus were studied by Poisson regression analysis of confirmed cases and deaths in 883 U.S. counties with a population of 50,000 or more as of May 31, 2020. With little exception, each factor was predictive of incidence and mortality. The regression equation can be used to identify priority locations for preventive efforts and preparation for medical care caseloads when prevention is unsuccessful. Based on the correlation of cases and deaths to days since stay-at-home orders were issued, the orders reduced the cases about 48 percent and deaths about 50 percent. Focusing preventive efforts on the more vulnerable counties may be more effective and less economically damaging than statewide shutdowns.

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