Modernizing public health surveillance for global health security leveraging AI

TL;DR

This paper proposes a new AI-powered system to improve global health surveillance by integrating human, animal, and environmental data in real time.

Contribution

The novel AI-OneHIS infrastructure is introduced to modernize traditional public health surveillance with cloud-based AI and One Health principles.

Findings

AI-OneHIS enables faster data capture and interoperability of fragmented health systems.

The system supports precise decision-making while preserving data sovereignty and routine operations.

Implementation on a governance-collaboration-informatics framework can improve responses to public health emergencies.

Abstract

An electronic public health surveillance (e-PHS) embracing One Health and participatory approaches will collect and analyze data at the human-animal-environment interface to enhance real-time information for the prevention and control of public health emergencies (PHE) such as infectious disease outbreaks. Yet full implementation is suboptimal worldwide. Leveraging the capabilities of emerging digital technologies legally and ethically, we described the scope, added benefits, and applicability of a novel cloud-based, artificial intelligence-enabled One Health Integrated Disease Surveillance and Response health information system (AI-OneHIS) data infrastructure for modernizing the existing traditional PHS models. This multifaceted innovation will ensure faster data capture, seamless interoperability of fragmented HIS, and precise decision support, while preserving their structures, functionalities, and capabilities for routine operations and data sovereignty. This should enable the prevention, timely detection, and effective response to PHE for improved health outcomes if implemented with fidelity on a strong governance-collaboration-informatics-analytics framework.

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