What question did this study set out to answer?

This research aims to develop a Bayesian forecasting method to predict weekly influenza cases and lab tests in a hospital environment.

February 28, 2026Open Access

Short-Term Bayesian Influenza Forecasting in a Hospital Environment Using a Linear Kalman Filter

Key Points

This research aims to develop a Bayesian forecasting method to predict weekly influenza cases and lab tests in a hospital environment.
Applied a Bayesian Kalman filter for forecasting flu cases and tests.
Utilized real-time data from a large hospital system in Ohio.
Compared historical patterns with current data for accurate predictions.
Forecasts patient volume related to influenza four weeks in advance.
Demonstrated reliability in anticipating weekly lab tests and positive cases.

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.

Short-Term Bayesian Influenza Forecasting in a Hospital Environment Using a Linear Kalman Filter

Key Points

Abstract

Cite This Study