CALYPSO: Forecasting and Analyzing MRSA Infection Patterns with Community and Healthcare Transmission Dynamics

TL;DR

CALYPSO is a hybrid modeling framework that combines neural networks with epidemiological models to accurately forecast MRSA infection patterns across healthcare and community settings, aiding policy decisions.

Contribution

It introduces a novel hybrid approach that improves interpretability and accuracy in infectious disease forecasting by integrating diverse data sources with mechanistic models.

Findings

Achieves over 4.5% better forecasting accuracy than baseline models.

Identifies high-risk regions for targeted interventions.

Supports counterfactual analysis of infection control policies.

Abstract

Methicillin-resistant Staphylococcus aureus (MRSA) is a critical public health threat within hospitals as well as long-term care facilities. Better understanding of MRSA risks, evaluation of interventions and forecasting MRSA rates are important public health problems. Existing forecasting models rely on statistical or neural network approaches, which lack epidemiological interpretability, and have limited performance. Mechanistic epidemic models are difficult to calibrate and limited in incorporating diverse datasets. We present CALYPSO, a hybrid framework that integrates neural networks with mechanistic metapopulation models to capture the spread dynamics of infectious diseases (i.e., MRSA) across healthcare and community settings. Our model leverages patient-level insurance claims, commuting data, and healthcare transfer patterns to learn region- and time-specific parameters governing MRSA spread. This enables accurate, interpretable forecasts at multiple spatial resolutions (county, healthcare facility, region, state) and supports counterfactual analyses of infection control policies and outbreak risks. We also show that CALYPSO improves statewide forecasting performance by over 4.5% compared to machine learning baselines, while also identifying high-risk regions and cost-effective strategies for allocating infection prevention resources.