The increasing intersection of human activity and the Congo Basin’s forest perimeter has significantly accelerated the risk of zoonotic pathogen spillover. In Uganda, this environmental encroachment has already led to the identification of several viruses with global security implications, including Zika, Marburg, and Yellow Fever. Most critically, the region remains a hotspot for filoviruses, specifically the Sudan and Bundibugyo viruses. Because these deadly pathogens originate in wildlife populations before jumping to human hosts, traditional siloed health models are no longer sufficient. Establishing a robust early warning system for outbreaks of Ebola virus disease is a primary necessity, requiring a holistic approach that integrates human and animal health monitoring to detect threats at the source of spillover before they reach urban centers.

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While the Congo Basin represents a high-risk zone for emerging infectious diseases, traditional epidemiological research in the region is frequently stymied by resource limitations and fragmented infrastructure. However, the ubiquitous nature of mobile technology presents a unique opportunity to bypass these physical barriers. By leveraging the high prevalence of basic cellular devices, health authorities can establish a modern surveillance platform that operates in real-time, even in the most remote or underserved communities. This digital transition shifts the paradigm of disease monitoring from reactive clinical reporting to proactive, community-based data acquisition, creating a digital frontline for identifying hemorrhagic fever symptoms at the village level.

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The central objective of this research initiative is to develop and deploy a next-generation mHealth surveillance system designed to capture acute febrile illness (AFI) in humans alongside morbidity and mortality events in wildlife. Unlike static health surveys, this platform enables the longitudinal monitoring of environmental and animal exposures that fluctuate over time, providing a dynamic map of regional health risks. By creating a tiered reporting structure, the project ensures that both trained health professionals and community members can contribute to a collective early-warning network. This is particularly vital for Ebola, where the speed of notification can mean the difference between a contained cluster and a widespread epidemic.

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The research strategy focuses on the integration and refinement of these diverse data streams to ensure operational sustainability and geographical accuracy. By consolidating information from hospital electronic records, community reports, and wildlife observations from park rangers, the system creates a comprehensive diagnostic picture of the ecosystem’s health. Advanced computational models are then applied to this aggregated data to identify statistical anomalies or "spikes" in reported symptoms. This sensitivity allows for the detection of potential Ebola outbreaks in their earliest stages, even in areas where GPS coverage is unreliable, by pinpointing the origin of a pathogen through secondary digital signatures.

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Ultimately, the project aims to deliver a fully integrated, real-time data dissemination platform that empowers local health authorities. By providing a visual dashboard of syndromic surveillance, the system facilitates a faster and more accurate response to zoonotic events. The long-term impact of this work lies in its ability to bridge the gap between rural communities and centralized health ministries, creating a scalable model for pandemic prevention that is rooted in the existing social and technological fabric of the region. Through this integrated approach, Uganda can lead the way in establishing a resilient defense against Ebola and the next generation of global viral threats.

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This work is funded by the National Institutes of Health and the World Bank Pandemic Fund.