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Disease prediction models and operational readiness.

Courtney D Corley1, Laura L Pullum2, David M Hartley3

  • 1Pacific Northwest National Laboratory, Richland, Washington, United States of America.

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Summary

This review systematically analyzes biosurveillance models for predicting disease events involving select agents. It identifies 44 models, assessing their prediction capabilities, parameters, data sources, and verification methods for operational readiness.

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Area of Science:

  • Biosurveillance and Disease Event Prediction
  • One Health Paradigm in Epidemiology
  • Pathogen Risk Assessment and Modeling

Background:

  • Biosurveillance systems are crucial for early detection of disease events, particularly those involving select agents.
  • Predictive modeling is essential for forecasting disease outbreaks within the One Health framework.
  • Existing models vary in scope, data sources, and validation, impacting their operational readiness.

Purpose of the Study:

  • To systematically review biosurveillance models capable of forecasting disease events caused by select agents.
  • To classify identified models based on their prediction approach, spatial and ecological characteristics, and diagnostic capabilities.
  • To evaluate the verification and validation (V&V) methods applied to these models and propose readiness guidelines.

Main Methods:

  • Systematic literature search of commercial and government databases, including Google search results.
  • Inclusion criteria focused on models predicting disease events (not just transmission) involving US National Select Agent Registry pathogens.
  • Analysis of 44 selected papers, classifying models and recording parameters, data sources, and V&V methods.

Main Results:

  • Identified 44 biosurveillance models, categorized into event prediction (4), spatial (26), ecological niche (28), diagnostic/clinical (6), spread/response (9), and reviews (3).
  • Model parameters included etiology, climatic, spatial, and cultural factors; data sources ranged from remote sensing to expert opinion.
  • Verification and Validation (V&V) status varied, with a significant portion lacking reported V&V methods.

Conclusions:

  • The review highlights the diversity of biosurveillance models and their varying levels of development and validation.
  • There is a need for standardized V&V methods to enhance the operational readiness of disease prediction models.
  • Proposed operational readiness level guidelines can aid in assessing and improving the reliability of biosurveillance forecasting tools.