Short-term Bayesian influenza forecasting in a hospital environment using a linear Kalman filter

Journal of Theoretical Biology 2026 Open access

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Authors: Shankar Kaleeswaran Mani, Grzegorz A. Rempala, Eben Kenah, Fernanda Lang Schumacher
DOI: 10.1016/j.jtbi.2026.112422
Cited by: 1
References: 21

Abstract

Accurately forecasting the weekly number of influenza (flu) lab tests and positive cases is important for hospitals as they work to plan staffing, manage supplies, and provide timely patient care. In this paper, we describe a practical use of a Bayesian Kalman filter to forecast weekly flu tests and positives in a hospital setting. Using data from a large hospital system in Ohio, we show that this approach, which blends real-time hospital information with historical patterns, offers a dependable way to anticipate flu-related patient volume as far as four weeks ahead.

Fields of Study & Subjects

Data-Driven Disease Surveillance COVID-19 epidemiological studies Influenza Virus Research Studies

Publication details

Journal / source
Journal of Theoretical Biology
Publisher
Elsevier BV
Volume
624
Pages
112422–112422
Publication date
26 February 2026
Language
EN