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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Chimeric forecasting: combining probabilistic predictions from computational models and human judgment.

Thomas McAndrew1, Allison Codi2, Juan Cambeiro3,4

  • 1College of Health, Lehigh University, Bethlehem, PA, USA. mcandrew@lehigh.edu.

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|November 11, 2022
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Summary
This summary is machine-generated.

Combining human judgment with computational models, a chimeric ensemble improved infectious disease forecasts for cases and deaths. This novel approach enhances public health decision-making for predicting disease spread.

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Public Health

Background:

  • Accurate forecasting of infectious agent trajectories is crucial for public health.
  • Traditional forecasting relies on computational models, but human judgment offers unique insights.
  • Integrating diverse data sources can improve predictive accuracy.

Purpose of the Study:

  • To introduce and evaluate a chimeric ensemble combining computational and human judgment forecasts for infectious diseases.
  • To assess the performance of this novel ensemble against purely computational models.

Main Methods:

  • A chimeric ensemble was created by merging human judgment forecasts from generalist crowds with computational model forecasts.
  • Forecasts were generated monthly from January to June 2021 for US national incident cases and deaths.
  • Predictions were made for two- and three-week horizons, mirroring COVID-19 Forecast Hub criteria.

Main Results:

  • The chimeric ensemble demonstrated improved predictions for incident cases compared to computational-only ensembles.
  • Performance for predicting incident deaths was comparable between the chimeric ensemble and computational-only ensembles.
  • The study highlights the value of integrating human expertise into infectious disease modeling.

Conclusions:

  • A chimeric ensemble is a flexible and supportive tool for public health.
  • This hybrid approach shows promising results for predicting the trajectory of infectious agents.
  • Combining computational power with human intuition offers a robust strategy for epidemiological forecasting.