What to know
Why it Matters
Health departments and healthcare systems have data, but often lack the tools to produce real-time disease models and forecasts that can drive more informed and efficient outbreak response.
Insight Net members use ML/AI to integrate new sources of health information, fill in data gaps, and develop real-time tools. They create more accurate, efficient modeling and forecasting tools, then deliver them to decision-makers during local response.
CIDMATH
Health departments and partners in Georgia use disease forecasts to detect and respond to outbreaks like Norovirus. CIDMATH creates these tools, using ML to enhance traditional modeling approaches.
- Center for Infectious Disease Modeling & Analytics and Training Hub (CIDMATH)
- Led by: Emory University
- Website: www.cidmath.org/
CIDMATH uses ML to create better norovirus forecasts that combine conventional disease data with new sources. Their tools scrape Twitter/X for language related to the virus, which sends more than 100,000 children to the emergency department each year. Their ID Data Hub digests large, complex streams of clinical information. CIDMATH incorporates the new data sources to create more accurate models that predict how outbreaks will unfold. These forward-looking insights help teachers, parents, principals, and city officials prepare and respond to possible disease outbreaks with interventions like personal hygiene promotion, enhancing cleaning protocols, and planning for teacher illness.
MADMC
MADMC modeling tools help public health officials identify life-threatening viruses, using ML to turn large datasets into meaningful insights that drive prevention and response across the state.
- Midwest Analytics and Disease Modeling Center (MADMC)
- Led by: University of Minnesota
- Website: www.sph.umn.edu/research/centers/midwest-analytics-and-disease-modeling/
MADMC works with state partners, using natural language processing to comb through electronic health record data and analyze trends in fetal parvovirus B19 testing across the state. The tool assesses clinical notes, symptoms, and other data to model disease patterns. In 2025, it identifies a sharp increase in positive cases, which can be life-threatening for pregnant mothers. The Minnesota Department of Health and pediatricians are using this information to support increased testing, early diagnosis, and prevention with the goal of improving maternal and infant health. Read more in the (MMWR) article – notes from the field.
C-CORE
Public health and healthcare systems in California work together to turn data into actionable insights for outbreak response, thanks to C-CORE's ML/AI integrated disease forecasts and models.
- California Center for Outbreak Response (C-CORE)
- Led by: Kaiser Permanente of Southern California (KPSC)
- Website: www.insightnet.us/centers/c-core/
C-CORE focuses on improving the efficiency and accuracy of forecasting and modeling tools for respiratory viruses, monkeypox, and dengue using various techniques, including AI/machine learning. C-CORE applies these innovative strategies to the KPSC health system, with more than 4.7 million members, to identify gaps in disease testing and detection, test and scale up data collection, and develop new tools for better outbreak warning and preparedness.