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The challenges of modeling and forecasting the spread of COVID-19.

Andrea L BertozziElisa FrancoGeorge MohlerMartin B ShortDaniel Sledge
Published in: Proceedings of the National Academy of Sciences of the United States of America (2020)
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.
Keyphrases
  • coronavirus disease
  • public health
  • healthcare
  • sars cov
  • mental health
  • respiratory syndrome coronavirus
  • big data
  • physical activity
  • artificial intelligence