Development of a Mobile App for Real-Time Disease Surveillance in Livestock

Thesis Abstract

The rapid emergence and spread of contagious diseases among livestock present significant challenges to animal health management, economic stability, and public health, especially in regions with limited access to real-time disease monitoring systems. This study aims to develop and evaluate a mobile application designed to facilitate real-time disease surveillance in livestock populations, thereby enhancing early detection, timely response, and data-driven decision-making. The specific objectives include designing a user-friendly mobile app tailored for veterinary practitioners and livestock owners, implementing an integrated data collection and alert system, and assessing the app's usability, accuracy, and impact on disease reporting efficacy. Employing a mixed-methods research design, the study first utilizes a qualitative approach to inform app development through focus group discussions and interviews with 30 veterinary experts, livestock farmers, and informatics specialists. Subsequently, a quantitative evaluative phase involves a quasi-experimental design with a sample of 200 livestock farms randomly allocated into intervention and control groups. Data collection instruments comprise structured questionnaires, app usage logs, and disease reporting records. The validity and reliability of the survey instruments are established through Cronbach’s alpha (exceeding 0.85) and pilot testing, while thematic analysis is applied to qualitative data, and descriptive statistics alongside logistic regression analysis are employed for quantitative data. The analytical framework integrates the Technology Acceptance Model (TAM) and the Health Belief Model (HBM) to interpret factors influencing adoption and effective usage of the app. It is anticipated that the mobile app will significantly improve the timeliness and completeness of disease reporting, with an expected 40% increase in early detection of outbreaks within the intervention group compared to controls. The app is also projected to demonstrate high usability scores (above 4.0 on a 5-point Likert scale) and positive acceptance driven by perceived ease of use and perceived usefulness as per TAM. Regression analyses are expected to identify key predictors of app adoption, including technological literacy and perceived severity of livestock diseases. Qualitative insights are anticipated to reveal user perceptions, barriers to use, and contextual factors influencing implementation. This research contributes to existing knowledge by integrating ICT solutions into livestock disease surveillance frameworks, thereby providing empirical evidence on the effectiveness of mobile technologies in enhancing epidemiological monitoring in low-resource settings. The development process elucidates critical design features, user engagement strategies, and data security considerations essential for scalable deployment. Methodologically, the study demonstrates a comprehensive model combining user-centered design, mixed-method evaluation, and theory-driven analysis to inform future digital health innovations in veterinary medicine. The study concludes that a well-designed, contextually adapted mobile app can significantly augment disease monitoring capabilities among livestock stakeholders, leading to improved outbreak preparedness and response strategies. It recommends that policymakers and veterinary services integrate mobile surveillance tools into national disease control programs and prioritize training programs to boost adoption. Future research should explore longitudinal impacts of such systems, scalability across diverse geographic and socio-economic contexts, and integration with existing veterinary information systems to optimize the surveillance network's overall responsiveness and resilience.