Selected article for: "data drive and decision making"

Author: Philip J. Turk; Shih-Hsiung Chou; Marc A. Kowalkowski; Pooja P. Palmer; Jennifer S. Priem; Melanie D. Spencer; Yhenneko J. Taylor; Andrew D. McWilliams
Title: Modeling COVID-19 latent prevalence to assess a public health intervention at a state and regional scale
  • Document date: 2020_4_18
  • ID: j5o8it22_3
    Snippet: Because the COVID-19 landscape evolves rapidly due to the confluence of locally relevant factors, timely data to drive decision making around containment, treatment, and resource planning is critical. Forecasting models are used to generate early warnings to identify how a pandemic might evolve. During the early stages of the COVID-19 pandemic, forecasting was frequently applied to predict national and international infection transmission trends .....
    Document: Because the COVID-19 landscape evolves rapidly due to the confluence of locally relevant factors, timely data to drive decision making around containment, treatment, and resource planning is critical. Forecasting models are used to generate early warnings to identify how a pandemic might evolve. During the early stages of the COVID-19 pandemic, forecasting was frequently applied to predict national and international infection transmission trends [13, 14] . Local communities and health systems turned to these national and international models for their own planning; however, the generalizability of such models to the local situation is limited and ignores important community-level population characteristics and transmission dynamics [3, 15, 16, 17] . An objective of this study was to understand how spatial differences impact model results and their interpretation.

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