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A comprehensive analysis of COVID-19 transmission and mortality rates at the county level in the United States considering socio-demographics, health indicators, mobility trends and health care infrastructure attributes.
Bhowmik, Tanmoy; Tirtha, Sudipta Dey; Iraganaboina, Naveen Chandra; Eluru, Naveen.
Afiliação
  • Bhowmik T; Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, Florida, United States of America.
  • Tirtha SD; Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, Florida, United States of America.
  • Iraganaboina NC; Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, Florida, United States of America.
  • Eluru N; Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, Florida, United States of America.
PLoS One ; 16(4): e0249133, 2021.
Article em En | MEDLINE | ID: mdl-33793611
BACKGROUND: Several research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) that can contribute to the COVID-19 transmission/mortality rate. The current study effort is designed to remedy this by analyzing COVID-19 transmission/mortality rates considering a comprehensive set of factors in a unified framework. METHODS AND FINDINGS: We study two per capita dependent variables: (1) daily COVID-19 transmission rates and (2) total COVID-19 mortality rates. The first variable is modeled using a linear mixed model while the later dimension is analyzed using a linear regression approach. The model results are augmented with a sensitivity analysis to predict the impact of mobility restrictions at a county level. Several county level factors including proportion of African-Americans, income inequality, health indicators associated with Asthma, Cancer, HIV and heart disease, percentage of stay at home individuals, testing infrastructure and Intensive Care Unit capacity impact transmission and/or mortality rates. From the policy analysis, we find that enforcing a stay at home order that can ensure a 50% stay at home rate can result in a potential reduction of about 33% in daily cases. CONCLUSIONS: The model framework developed can be employed by government agencies to evaluate the influence of reduced mobility on transmission rates at a county level while accommodating for various county specific factors. Based on our policy analysis, the study findings support a county level stay at home order for regions currently experiencing a surge in transmission. The model framework can also be employed to identify vulnerable counties that need to be prioritized based on health indicators for current support and/or preferential vaccination plans (when available).
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fatores Socioeconômicos / Demografia / Atenção à Saúde / Pandemias / COVID-19 Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País como assunto: America do norte Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fatores Socioeconômicos / Demografia / Atenção à Saúde / Pandemias / COVID-19 Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País como assunto: America do norte Idioma: En Ano de publicação: 2021 Tipo de documento: Article