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Zoonosis refers to the transmission of infectious diseases from animal tohuman. The increasing number of zoonosis incidence makes the great losses tolives, including humans and animals, and also the impact in social economic. Itmotivates development of a system that can predict the future number ofzoonosis occurrences in human. This paper analyses and presents the use ofSeasonal Autoregressive Integrated Moving Average (SARIMA) method fordeveloping a forecasting model that able to support and provide predictionnumber of zoonosis human incidence. The dataset for model development wascollected on a time series data of human tuberculosis occurrences in UnitedStates which comprises of fourteen years of monthly data obtained from a studypublished by Centers for Disease Control and Prevention (CDC). Several trialmodels of SARIMA were compared to obtain the most appropriate model. Then,diagnostic tests were used to determine model validity. The result showed thatthe SARIMA(9,0,14)(12,1,24)12 is the fittest model. While in the measure ofaccuracy, the selected model achieved 0.062 of Theils U value. It implied thatthe model was highly accurate and a close fit. It was also indicated thecapability of final model to closely represent and made prediction based on thetuberculosis historical dataset.

Prediction of Zoonosis Incidence in Human using Seasonal Auto Regressive Integrated Moving Average (SARIMA)