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Author Spotlight: Developing Immunocompetent Organ-on-Chip Models for Infectious Disease Research
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Infectious disease modelling to inform policy.

G C Smith, R R Kao, M Walker

    Revue Scientifique Et Technique (International Office of Epizootics)
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    Summary

    Optimizing animal disease modeling is crucial for effective decision-making. This study outlines ten steps, focusing on initial problem definition and final reporting, to enhance the relevance and understanding of modeling projects.

    Keywords:
    Decision-makingModelling processQuality assurance

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    Area of Science:

    • Veterinary epidemiology
    • Mathematical modeling
    • Decision science

    Background:

    • Modeling is increasingly vital for animal disease management and policy decisions.
    • Ensuring the optimization of the modeling process maximizes benefits for decision-makers.
    • A structured approach is needed to improve the utility and impact of animal disease models.

    Purpose of the Study:

    • To present a ten-step framework for optimizing the animal disease modeling process.
    • To enhance the relevance and understanding of modeling outputs for decision-makers.
    • To improve the overall effectiveness of animal disease modeling in informing policy.

    Main Methods:

    • Development of a ten-step protocol for animal disease modeling.
    • Categorization of steps into initialisation, modeling/quality assurance, and reporting phases.
    • Emphasis on defining the question, answer, and timescale upfront.

    Main Results:

    • A structured ten-step process is proposed to improve animal disease modeling.
    • Four steps focus on initialisation (defining scope and objectives).
    • Two steps cover the modeling process and quality assurance, followed by four reporting steps.

    Conclusions:

    • A greater emphasis on the initialisation and reporting stages of modeling projects is recommended.
    • This structured approach is expected to increase the relevance of modeling work.
    • Improved understanding of results will contribute to better-informed animal disease decision-making.