Master of Science, The Ohio State University, 2024, Biostatistics

Accurate forecasting of weekly number of influenza (flu) lab tests and positive cases is vital for hospitals to provide adequate patient care at the right time. It also helps prevent shortages or overages of staffs and supplies. In this paper we present a practical implementation of a Bayesian Kalman filter to forecast weekly flu test and positive cases in a hospital environment. By integrating real time hospital data, this framework offers a robust methodology for accurately predicting flu volume one to four weeks out with a reasonable accuracy.

Committee: Grzegorz Rempala (Advisor); Eben Kenah (Committee Member); Fernanda Schumacher (Committee Member) Subjects: Biostatistics; Health Care Management; Mathematics; Medicine; Statistics