A decision support framework for a zoonosis prediction system: case study of Salmonellosis

Abstract: The rising number of zoonosis epidemics and the potential threat to humans highlight the need to apply a stringent system to prevent a zoonosis outbreak. Zoonosis is any infectious diseases that can be transmitted from
animals to humans. This paper analyses and presents the development of a decision support system (DSS) that is able to support and provide prediction on the number of zoonosis human incidence. The DSS framework consists of three components: database management subsystem, model management subsystem, and user interface. A set of 168 monthly data from 1993–2006 was used to develop the database management subsystem. Data collection was collected from the number of human Salmonellosis occurrences in the USA published by
Centers for Disease Control and Prevention (CDC). Six forecasting methods were applied in the model management subsystem. Finally, what-if (sensitivity) analysis was chosen to construct user interface subsystem. The result
determined neural network as the most appropriate method. While, sensitivity analysis result for neural network indicated large fluctuation caused by the change of data input when added by new data.