A DECISION SUPPORT SYSTEM FRAMEWORK FOR SEASONAL ZOONOSIS PREDICTION

The arising number of zoonosis epidemics and the potential threat to human highlight the need to apply stringent system to contend zoonosis outbreak. Zoonosis is any infectious disease that is able to be transmitted from other animals, both wild and domestic, to humans. The increasing number of zoonotic diseases coupled with the frequency of occurrences, especially lately, has made the need to study and develop a framework to predict future number of zoonosis incidence. Unfortunately, study of literatures showed most prediction models are case-specific and often based on a single forecasting technique. This research analyses and presents the application of a decision support system (DSS) that applied multi forecasting methods to support and provide prediction on the number of zoonosis human incidence. The focus of this research is to identify and to design a DSS framework on zoonosis that is able to handle two seasonal time series type, namely additive seasonal model and multiplicative seasonal model. The first dataset describes the seasonal data pattern that exhibited the constant variation, while the second dataset showed the upward/downward trend. Two case studies were selected to evaluate the proposed framework: Salmonellosis and Tuberculosis for additive time series and Tuberculosis for multiplicative time series. Data was collected from the number of human Salmonellosis and Tuberculosis incidence in the United States published by Centers for Disease Control and Prevention (CDC). These data were selected based on availability and completeness. The proposed framework consists of three components: database management subsystem, model management subsystem, and dialog generation and management subsystem. A set of 168 monthly data (1993–2006) of Salmonellosis and Tuberculosis was used for developing the database management subsystem. Six forecasting methods, including five statistical methods and one soft computing method, were applied in the model management subsystem. They were regression analysis, moving average, decomposition, Holt-Winter’s, ARIMA, and neural network. The results of each method were compared using ANOVA, while Duncan Multiple Range Test was employed to identify the compatibility of each method to the time series. Coefficient of Variation (CV) was used to determine the most appropriate method among them. In the user interface subsystem, “What If” (sensitivity) analysis was chosen to construct this component. This analysis provided the fluctuation of forecasting results which was influenced by the changes in data. The sensitivity analysis was able to determine method with the highest fluctuation based on data update. Observation of the result showed that regression analysis was the fittest method for Salmonellosis and neural network was the fittest method of Tuberculosis. Thus, it could be concluded that results difference of both cases was affected by the available data series. Finally, the design of Graphical User Interface (GUI) was presented to show the connectivity flow between all DSS components. The research resulted in the development of a DSS theoretical framework for a zoonosis prediction system. The results are also expected to serve as a guide for further research and development of DSS for other zoonosis, not only for seasonal zoonosis but also for nonseasonal zoonosis

A DECISION SUPPORT SYSTEM FRAMEWORK FOR SEASONAL ZOONOSIS PREDICTION

The arising number of zoonosis epidemics and the potential threat to human highlight the need to apply stringent system to contend zoonosis outbreak. Zoonosis is any infectious disease that is able to be transmitted from other animals, both wild and domestic, to humans. The increasing number of zoonotic diseases coupled with the frequency of occurrences, especially lately, has made the need to study and develop a framework to predict future number of zoonosis incidence. Unfortunately, study of literatures showed most prediction models are case-specific and often based on a single forecasting technique. This research analyses and presents the application of a decision support system (DSS) that applied multi forecasting methods to support and provide prediction on the number of zoonosis human incidence. The focus of this research is to identify and to design a DSS framework on zoonosis that is able to handle two seasonal time series type, namely additive seasonal model and multiplicative seasonal model. The first dataset describes the seasonal data pattern that exhibited the constant variation, while the second dataset showed the upward/downward trend. Two case studies were selected to evaluate the proposed framework: Salmonellosis and Tuberculosis for additive time series and Tuberculosis for multiplicative time series. Data was collected from the number of human Salmonellosis and Tuberculosis incidence in the United States published by Centers for Disease Control and Prevention (CDC). These data were selected based on availability and completeness. The proposed framework consists of three components: database management subsystem, model management subsystem, and dialog generation and management subsystem. A set of 168 monthly data (1993–2006) of Salmonellosis and Tuberculosis was used for developing the database management subsystem. Six forecasting methods, including five statistical methods and one soft computing method, were applied in the model management subsystem. They were regression analysis, moving average, decomposition, Holt-Winter’s, ARIMA, and neural network. The results of each method were compared using ANOVA, while Duncan Multiple Range Test was employed to identify the compatibility of each method to the time series. Coefficient of Variation (CV) was used to determine the most appropriate method among them. In the user interface subsystem, “What If” (sensitivity) analysis was chosen to construct this component. This analysis provided the fluctuation of forecasting results which was influenced by the changes in data. The sensitivity analysis was able to determine method with the highest fluctuation based on data update. Observation of the result showed that regression analysis was the fittest method for Salmonellosis and neural network was the fittest method of Tuberculosis. Thus, it could be concluded that results difference of both cases was affected by the available data series. Finally, the design of Graphical User Interface (GUI) was presented to show the connectivity flow between all DSS components. The research resulted in the development of a DSS theoretical framework for a zoonosis prediction system. The results are also expected to serve as a guide for further research and development of DSS for other zoonosis, not only for seasonal zoonosis but also for nonseasonal zoonosis