In the infectious disease group here at Predictive Science, we run simulations to predict influenza burden on a week-to-week basis in the United States. Here the overall burden of infuenza is measured using influenza hospital admissions. Thus our model seeks to forecast new hospital admissions for future weeks.
We use a Suseptible-Infectious-Recovered/Hospitalized (SIRH) model with a time-dependent reproduction number R0(t). Reproduction number can be understood as a measure of infectiousness.
Fitting the model to daily data is done via Markov Chain Monte Carlo (MCMC) process. This procedure maps the probability distribution, revealing not only the best fit, but also likely model variations.
The posterior distribution of the fit is used to make state-specific probabilistic forecasts of the daily number of confirmed influenza hospital admissions. The national forecast is constructed as the ordered sum of the state level forecasts. States with low hospitalization rates are not fitted and are forecasted with a simple statistical model. We also use a statistical model when we believe that there is large uncertainty in future trends. The daily data and forecasts are aggregated to the weekly scale.
Epidemic Week: Week of the year as defined by the Morbidity and Mortality Weekly Report (MMWR). In this setting 'epidemic week' selects the portion of the flu season to fit the model to.
Hospital Admission Data: The 'gold standard' for our forecasts is confirmed influenza admission data (field name: `previous_day_admission_influenza_confirmed`) from the COVID-19 Reported Patient Impact and Hospital Capacity by State Timeseries . This daily timeseries is provided by The U.S. Department of Health and Human Services. The data updates regularly and currently its reporting is mandatory. Here the daily forecasts and data are aggregated to the weekly scale.
Our work is supported by the CDC/CSTE (Award No.: 5 NU38OT000297) as part of a CDC coordinated Collaborative Forecasting Challenge for Weekly Confirmed Influenza Admissions During the 2021-2022 season.
For questions about this web site please contact:
Dr. M. Ben-Nun: firstname.lastname@example.org
Dr. James Turtle: email@example.com
Dr. Pete Riley: firstname.lastname@example.org