Approximately 60 percent of emerging infectious diseases in humans originate in animals. HIV, Ebola, SARS, MERS, and H5N1 influenza all crossed from animal reservoirs to human populations. The One Health framework, endorsed by the WHO, FAO, and UNEP jointly, recognizes that human health, animal health, and environmental health are interdependent and must be monitored together. Machine learning is now being applied to the data integration problem that One Health surveillance has always faced: combining heterogeneous datasets from human clinical systems, veterinary surveillance networks, wildlife monitoring programs, and environmental sensors into a coherent early warning system.
The November 2025 review in Biomedical Journal by Li et al. from Chang Gung Memorial Hospital and Boston Children’s Hospital documented the current state of AI integration in infection surveillance. The key finding is that integrating social media data improves influenza forecasting accuracy, while wearable technologies enable real-time monitoring of infection dynamics that traditional sentinel surveillance systems cannot capture.
What One Health Surveillance Actually Collects
Traditional infection surveillance collects data from one domain at a time. Human surveillance systems collect case reports, laboratory-confirmed diagnoses, and sentinel physician networks. Veterinary surveillance collects farm case reports, wildlife sampling data, and abattoir inspection results. Environmental surveillance collects water quality monitoring, air sampling, and climate data that affects vector ranges. These systems operate in separate institutional frameworks with different data standards, different reporting timelines, and different organizational authorities. Machine learning integration builds models that process all three data streams simultaneously, identifying correlations across the human-animal-environment interface that single-domain surveillance would miss.
The Senegal AI4MPOX-SN Initiative
The February 2026 Frontiers in Public Health paper by Faye et al. from Cheikh Anta DIOP University in Dakar documented One Health AI applied to mpox surveillance in Senegal. By late October 2025, Senegal had reported seven mpox cases, all in Dakar, following population movement along the Dakar-Thiès-Diourbel corridor. The AI4MPOX-SN initiative proposes to integrate human-animal-environment data using AI for anomaly detection and predictive modeling. The paper documents that the existing system improved reporting and geolocation, but faces challenges including underreporting in rural areas and gaps in data interoperability.
AI for Mpox in Africa
The September 2025 Journal of Virological Methods review by Olawade et al. from the University of East London documented AI applications for mpox control across Africa: machine learning for early detection, automated contact tracing through mobile data, and optimization of public health messages for specific communities. The challenges identified apply broadly: limited digital infrastructure, data quality issues in fragmented surveillance systems, and ethical concerns about privacy.
Wearables and Real-Time Infection Dynamics
Consumer wearables continuously measure resting heart rate, heart rate variability, skin temperature, and respiratory rate. These parameters change measurably during the early phase of respiratory infection, before the infected person develops symptoms or seeks clinical care. Studies using Fitbit and Apple Watch data demonstrated that elevated resting heart rate in the days before symptom onset is a statistically significant predictor of influenza-like illness. At population scale, aggregate wearable signals can detect rising infection rates faster than clinical case reporting systems.
Climate Change as a One Health Driver
Rising temperatures expand the geographic range of vector species including Aedes mosquitoes (dengue, Zika, chikungunya) and Ixodes ticks (Lyme disease, tick-borne encephalitis). Machine learning models incorporating climate variables alongside epidemiological data have shown improved predictive accuracy for vector-borne disease risk, enabling health systems to prepare mosquito control campaigns before outbreaks begin.
What Happens Next
The key performance metric is time-to-detection: how many days earlier does integrated AI surveillance detect an emerging outbreak compared to traditional single-domain systems. Whether the AI4MPOX-SN initiative and similar programs produce measurable improvements in outbreak detection speed will become clear in the next three to five years as the systems accumulate operational data.
Primary sources retrieved from PubMed: Li JH et al., “Artificial intelligence in infection surveillance,” Biomedical Journal 2025;49(2):100929 (PMID: 41205676); Faye SLB et al., Front Public Health 2026;14:1742888 (PMID: 41710307); Olawade DB et al., J Virol Methods 2025;339:115270 (PMID: 41005719).
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