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- Andrea L. Bertozzi
- Department of Mathematics, University of California, Los Angeles, CA 90095;
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- Elisa Franco
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA 90095;
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- George Mohler
- Department of Computer Science, Indiana University–Purdue University Indianapolis, Indianapolis, IN 46202;
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- Martin B. Short
- Department of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332;
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- Daniel Sledge
- Department of Political Science, University of Texas at Arlington, Arlington, TX 76019
説明
<jats:title>Significance</jats:title><jats:p>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 remain a challenge. Here, we present and detail three regional-scale models for forecasting and assessing the course of the pandemic. This work is intended to demonstrate the utility of parsimonious models for understanding the pandemic and to provide 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.</jats:p>
収録刊行物
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- Proceedings of the National Academy of Sciences
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Proceedings of the National Academy of Sciences 117 (29), 16732-16738, 2020-07-02
Proceedings of the National Academy of Sciences