Data-Driven Model for Animal Surveillance: Case of Pastoral Communities in Kajiado County, Kenya
Abstract
In Kenya, the livestock sector experiences an unexpected outbreak of contagious diseases that leads to animal loss and decreases cattle productivity. This impacts the livelihoods of pastoral communities, mainly the youth and women, who depend on this sector as their primary income source through selling live cattle, extra meat, and milk. Traditional methods used to predict the occurrence of contagious diseases are no longer accurate due to unpredictable weather patterns caused by climate change and associated risks. This calls for accurate, timely, and location-specific advisories on priority livestock diseases to prevent losses incurred by farmers. The objective of this study was to test cognified distributed technology in handling data-driven models to generate data evidence that can be used to predict the next chances of disease re-occurrences. The study used the constructive research approach to develop a custom-made surveillance and reporting prototype that leverage high-performance computing resources and real-time weather forecast data from remote satellites. The prototype showed a tandem between the number of infections reported and the predicted chances of occurrence generated by the model. When the incoming data from different types, locations, and magnitudes are well formatted and compared with the historical data pattern, the computing resources can perform pattern and matching analysis to determine the next chances of disease occurrence. The technology can guide agricultural stakeholders, including policymakers, on early response mechanisms and vaccination prioritization.
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