AI-Driven Systems for Predicting Zoonotic Disease Outbreaks in Rural Livestock Communities: A Questionnaire-Based Descriptive Study

Abstract

This study seeks to understand the readiness, perceptions, and resource capacities of livestock owners and veterinary personnel toward the use of AI-powered systems to anticipate zoonotic disease outbreaks in rural livestock communities. It involved the distribution of a structured questionnaire to 200 individuals in the rural livestock community, with a descriptive statistical approach (frequency, percentage) being employed to summarize the data. Notably, 72% of respondents understand the value of AI systems, but only 24% think the community has the requisite supportive infrastructure. Response distributions for the various domains (awareness, infrastructure, willingness, constraints) are summarized in the tables and accompanying narratives of interpretation. These chapters relate to the literature on the use of AI in zoonotic disease surveillance, and the absence of literature on the gaps of trust, integration, and capacity for evidence-based surveillance. AI systems for surveillance in resource-poor settings will require significant training, infrastructure development, and revised policies. Other major stakeholder recommendations include deployment in phases, training, and development of regulatory policies.