Real-Time Disease Outbreak Prediction Models

Abstract

Infectious disease outbreaks -- from seasonal influenza to pandemic COVID-19 -- impose enormous health and economic burdens, yet early warning systems remain inadequate for timely public health intervention. Traditional surveillance relies on laboratory-confirmed case reports that lag actual disease activity by 1-3 weeks, while mechanistic epidemiological models (SIR, SEIR) require parameter estimation that is unreliable in the early outbreak phase when data are sparse. Real-time prediction integrating syndromic surveillance, digital data streams (search queries, social media, mobility data), genomic sequencing, and environmental factors could provide the 2-4 week forecast horizon needed for effective intervention. This study developed and benchmarked six outbreak prediction approaches -- classical SEIR compartmental model, autoregressive time-series (ARIMA), ensemble machine learning (gradient-boosted trees on surveillance + digital signals), recurrent neural network (LSTM on multivariate time-series), graph neural network on mobility-connected regions (GNN-Epi), and a proposed Multi-Source Outbreak Intelligence Network (MOSAIC) combining physics-informed neural ODE epidemiological dynamics, real-time digital surveillance fusion (Google Trends, Twitter/X syndromic mentions, Apple Mobility), genomic variant tracking (lineage prevalence from GISAID), and climate-epidemiology coupling via a spatio-temporal transformer -- for predicting weekly case counts, hospitalisation surges, and outbreak onset timing across four disease systems: influenza (US 2015-2024, 10 seasons), COVID-19 (EU 2020-2024), dengue (Brazil 2015-2024), and cholera (South Asia 2018-2024). MOSAIC achieved the highest prediction accuracy across all diseases: mean 2-week-ahead MAPE 12.4% (vs SEIR 28.6%, ARIMA 22.4%, ensemble ML 18.2%, LSTM 16.8%, GNN-Epi 14.6%; p < 0.001), with the greatest advantage during early outbreak phases (weeks 1-4: MAPE 14.8% vs SEIR 42.6%; p < 0.001). MOSAIC detected outbreak onset a median 2.4 weeks earlier than traditional surveillance thresholds and predicted hospitalisation surges with AUROC 0.924. The genomic module captured variant-driven wave dynamics (Delta, Omicron transitions) that purely epidemiological models missed. These results establish multi-source AI-integrated surveillance as the state-of-the-art for real-time outbreak prediction.