Nowcasting Burden and Infection Trends for Seasonal and Epidemic Pathogens
Description
Improve real-time estimates of disease burden and infection trends for better 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-level estimates of the time-varying reproductive number (Rt) – a measure of epidemic trajectory and indicator of the level of effort needed to bring an epidemic under control – for COVID-19 and influenza (public-facing) and RSV (internal to CDC at this time), and weekly nowcasts of hospital admissions within the Respiratory Virus Hospitalization Surveillance Network (RESP-NET; 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 information on current and near-future disease risk and effort required for control 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 Respiratory Virus Hospitalization Surveillance Network (RESP-NET), internal and publicly available emergency department visit data collected through the National Syndromic Surveillance Program (NSSP)