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The challenges of modeling and forecasting the spread of COVID-19.
Bertozzi, Andrea L; Franco, Elisa; Mohler, George; Short, Martin B; Sledge, Daniel.
Afiliación
  • Bertozzi AL; Department of Mathematics, University of California, Los Angeles, CA 90095; bertozzi@ucla.edu.
  • Franco E; Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA 90095.
  • Mohler G; Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA 90095.
  • Short MB; Department of Bioengineering, University of California, Los Angeles, CA 90095.
  • Sledge D; Department of Computer Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202.
Proc Natl Acad Sci U S A ; 117(29): 16732-16738, 2020 07 21.
Article en En | MEDLINE | ID: mdl-32616574
The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Neumonía Viral / Control de Infecciones / Infecciones por Coronavirus / Pandemias / Betacoronavirus / Modelos Teóricos Tipo de estudio: Prognostic_studies Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2020 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Neumonía Viral / Control de Infecciones / Infecciones por Coronavirus / Pandemias / Betacoronavirus / Modelos Teóricos Tipo de estudio: Prognostic_studies Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2020 Tipo del documento: Article