Available online 28 February 2024, 100756
Author links open overlay panel, , , , AbstractForecasts of infectious agents provide public health officials advanced warning about the intensity and timing of the spread of disease. Past work has found that accuracy and calibration of forecasts is weakest when attempting to predict an epidemic peak. Forecasts from a mechanistic model would be improved if there existed accurate information about the timing and intensity of an epidemic. We presented 3,000 humans with simulated surveillance data about the number of incident hospitalizations from a current and two past seasons, and asked that they predict the peak time and intensity of the underlying epidemic. We found that in comparison to two control models, a model including human judgment produced more accurate forecasts of peak time and intensity of hospitalizations during an epidemic. Chimeric models have the potential to improve our ability to predict targets of public health interest which may in turn reduce infectious disease burden.
KeywordsForecasting
Compartmental models
Human judgment
Data availabilityAll code and data is fully available on GitHub at https://github.com/computationalUncertaintyLab/hj_guided_prediction.
© 2024 Published by Elsevier B.V.
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