Predicting COVID-19 and Respiratory Illness: Results of the 2022–2023 Armed Forces Health Surveillance Division Forecasting Challenge

• Published in: MSMR (U.S. Army Center for Health Promotion and Preventive Medicine, Executive Communications Division) Vol. 31; no. 5; pp. 24 - 30
• Main Authors: Bova, Mark L., McGee, Sasha A., Elliott, Kathleen R., Ubiera, Juan I.
• Format: Journal Article
• Language: English
• Published: Armed Forces Health Surveillance Division 20-05-2024
• ISSN: 2158-0111; 2152-8217

Since 2019, the Integrated Biosurveillance Branch of the Armed Forces Health Surveillance Division has conducted an annual forecasting challenge during influenza season to predict short-term respiratory disease activity among Military Health System beneficiaries. Weekly case and encounter observed data were used to generate 1- through 4-week advanced forecasts of disease activity. To create unified combinations of model inputs for evaluation across multiple spatial resolutions, 8 individual models were used to calculate 3 ensemble models. The accuracy of forecasts compared to the observed activity for each model was evaluated by calculating a weighted interval score. Weekly 1- through 4-week ahead forecasts for each ensemble model were generally higher than observed data, especially during periods of peak activity, with peaks in forecasted activity occurring later than observed peaks. The larger the forecasting horizon, the more pronounced the gap between forecasted peak and observed peak. The results showed that several models accurately predicted COVID-19 cases and respiratory encounters with enough lead time for public health response by senior leaders.