# Combining digital data streams and epidemic networks for real time outbreak detection

arXiv: https://arxiv.org/abs/2511.07163 · HTML: https://mlsanity.com/paper/2511.07163/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 72 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 7 of 7)

- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic](https://mlsanity.com/paper/2008.12238/) (2020)
- [Accurate estimation of influenza epidemics using Google search data via ARGO](https://mlsanity.com/paper/1505.00864/) (2015)
- [Tracking COVID-19 using online search](https://mlsanity.com/paper/2003.08086/) (2020)
- [Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0)](https://mlsanity.com/paper/2009.01265/) (2020)
- [Prediction Regions for Poisson and Over-Dispersed Poisson Regression Models with Applications to Forecasting Number of Deaths during the COVID-19 Pandemic](https://mlsanity.com/paper/2007.02105/) (2020)
- [Federated Epidemic Surveillance](https://mlsanity.com/paper/2307.02616/) (2023)

## Built on by (most cited first, 1 of 1)

- [SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecasting](https://mlsanity.com/paper/2605.06530/) (2026)

## Abstract (arXiv)

Responding to disease outbreaks requires close surveillance of their trajectories, but outbreak detection is hindered by the high noise in epidemic time series. Aggregating information across data sources has shown great denoising ability in other fields, but remains underexplored in epidemiology. Here, we present LRTrend, an interpretable machine learning framework to identify outbreaks in real time. LRTrend effectively aggregates diverse health and behavioral data streams within one region and learns disease-specific epidemic networks to aggregate information across regions. We reveal diverse epidemic clusters and connections across the United States that are not well explained by commonly used human mobility networks and may be informative for future public health coordination. We apply LRTrend to 2 years of COVID-19 data in 305 hospital referral regions and frequently detect regional Delta and Omicron waves within 2 weeks of the outbreak's start, when case counts are a small fraction of the wave's resulting peak.