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Model Complexity and Accuracy: A COVID-19 Case Study

Author

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  • Colin Small

    (Operations Research and Industrial Engineering, Cockrell School of Engineering, University of Texas at Austin, Austin, Texas 78712)

  • J. Eric Bickel

    (Operations Research and Industrial Engineering, Cockrell School of Engineering, University of Texas at Austin, Austin, Texas 78712; Department of Information, Risk, and Operations Management, McCombs School of Business, University of Texas at Austin, Austin, Texas 78712)

Abstract

When creating mathematical models for forecasting and decision making, there is a tendency to include more complexity than necessary, in the belief that higher-fidelity models are more accurate than simpler ones. In this paper, we analyze the performance of models that submitted COVID-19 forecasts to the U.S. Centers for Disease Control and Prevention and evaluate them against a simple two-equation model that is specified using simple linear regression. We find that our simple model was comparable in accuracy to highly publicized models and had among the best-calibrated forecasts. This result may be surprising given the complexity of many COVID-19 models and their support by large forecasting teams. However, our result is consistent with the body of research that suggests that simple models perform very well in a variety of settings.

Suggested Citation

  • Colin Small & J. Eric Bickel, 2022. "Model Complexity and Accuracy: A COVID-19 Case Study," Decision Analysis, INFORMS, vol. 19(4), pages 354-383, December.
    • Handle: RePEc:inm:ordeca:v:19:y:2022:i:4:p:354-383
    DOI: 10.1287/deca.2022.0457
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    References listed on IDEAS

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