Incorporating Expert Guidance in Epidemic Forecasting

Alexander Rodríguez; Bijaya Adhikari; Naren Ramakrishnan; B. Aditya Prakash

doi:10.48550/arxiv.2101.10247

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Incorporating Expert Guidance in Epidemic Forecasting

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Incorporating Expert Guidance in Epidemic Forecasting

Alexander Rodríguez, Bijaya Adhikari, Naren Ramakrishnan and B. Aditya Prakash

ArXiv.org

12/24/2020

DOI: 10.48550/arxiv.2101.10247

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https://doi.org/10.48550/arxiv.2101.10247View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Forecasting influenza like illnesses (ILI) has rapidly progressed in recent years from an art to a science with a plethora of data-driven methods. While these methods have achieved qualified success, their applicability is limited due to their inability to incorporate expert feedback and guidance systematically into the forecasting framework. We propose a new approach leveraging the Seldonian optimization framework from AI safety and demonstrate how it can be adapted to epidemic forecasting. We study two types of guidance: smoothness and regional consistency of errors, where we show that by its successful incorporation, we are able to not only bound the probability of undesirable behavior to happen, but also to reduce RMSE on test data by up to 17%.

Computer Science - Learning

Details

Title: Subtitle: Incorporating Expert Guidance in Epidemic Forecasting
Creators: Alexander Rodríguez
Bijaya Adhikari
Naren Ramakrishnan
B. Aditya Prakash
Resource Type: Preprint
Publication Details: ArXiv.org
DOI: 10.48550/arxiv.2101.10247
ISSN: 2331-8422
Language: English
Date posted: 12/24/2020
Academic Unit: Computer Science
Record Identifier: 9984411253402771

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