Short-term Forecasting of Severe Outcomes for Seasonal and Epidemic Pathogens
Description
Predict severe disease outcomes - such as emergency department visits or hospital admissions - over short time horizons (1-4 weeks) to improve situational awareness for planning and decision-making at the national, state, and local level. Traditional AI/ML models (e.g. time series models) are mainly used as baselines against which to test and improve more sophisticated modeling methods.
Detailed example
Current outputs include weekly state and national hospital admissions forecasts for COVID-19 and influenza (public-facing) and weekly state and national ED visit forecasts for COVID-19 and influenza (internal to CDC at this time).
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Providing timely, accurate, and actionable forward-looking information on severe disease outcomes to government officials and the public.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Internal and publicly available hospital admissions data collected through the National Hospital Safety Network (NSHN), internal and publicly available emergency department visit data collected through the National Syndromic Surveillance Program (NSSP), internal wastewater concentration data collected through the National Wastewater Surveillance System (NWSS)