Infectious disease modeling for public health practice: projections, scenarios, and uncertainty in three phases of outbreak response

  1. Andrew F. Brouwer
  2. Marisa C. Eisenberg
  3. Natalie E. Dean
  4. Harry Hochheiser
  5. Philip Huang
  6. Joseph R. Coyle
  7. Lior Rennert

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Abstract

Public health departments need evidence-backed scenario projections to support decision making in infectious disease outbreaks. However, traditional infectious disease models are often not readily deployable or responsive to the urgent questions and priorities of public health departments or health systems. Moreover, uncertainty in model outputs is not always adequately assessed or communicated, potentially undermining trust among public health practitioners and the public. To address these issues, we, the Insight Net Modeling Guidance for Public Health Working Group, used early COVID-19 data from Michigan to illustrate modeling approaches that can be used to answer urgent questions in three key phases of outbreak response: prior to local introduction, early exponential growth, and established transmission with potential interventions. In each phase, we integrate case, hospitalization, and death data and capture ranges of plausible future trajectories. These models, which produce status quo and scenario projections, are intended to inform planning and motivate action rather than forecast precise future outcomes. Importantly, this work offers guidance to focus modeling efforts and provides examples and code for how to fit and implement these models, ultimately serving as both a conceptual guide and practical toolkit to support more transparent, timely, and appropriate use of models in outbreak response.

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  1. PREreview

    This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/18530325.

    Does the introduction explain the objective of the research presented in the preprint? Yes The authors raise a significant question in public health and identify key limitations in existing infectious disease models. They then propose addressing these gaps through analysis of real-world data.
    Are the methods well-suited for this research? Highly appropriate The authors combined three different modeling approaches to better reflect real-world infectious disease dynamics. Each approach presents a clear and practical predictive framework, which was supported by examples and code.
    Are the conclusions supported by the data? Highly supported The article used recent data on COVID-19 cases, hospitalizations and deaths to build the model. The authors pointed out the limitations of the approach. They also conducted analyses under different scenarios and estimated uncertainty of the model.
    Are the data presentations, including visualizations, well-suited to represent the data? Highly appropriate and clear
    How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Very clearly The article clearly explained each stage of the model. In the discussion section, the authors emphasized the importance of collaboration with the public health departments to refine the model and adapt it to evolving conditions.
    Is the preprint likely to advance academic knowledge? Highly likely
    Would it benefit from language editing? No
    Would you recommend this preprint to others? Yes, it's of high quality
    Is it ready for attention from an editor, publisher or broader audience? Yes, as it is

    Competing interests

    The author declares that they have no competing interests.

    Use of Artificial Intelligence (AI)

    The author declares that they did not use generative AI to come up with new ideas for their review.

  3. Version published to 10.1101/2025.10.20.25338175 on medRxiv