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Bayesian Conditional Autoregressive (CAR) is used in disease mapping because it is able to model relative risks by taking into account the smoothing of the estimated relative risk and entering spatial information to reduce the errors of the estimated relative risk parameters so that a more reliable relative risk model is obtained. In this study, the relative risk value of the spread of dengue fever will be calculated using Bayesian CAR with the localised model. These results were obtained using the OpenBUGS program and are illustrated in the 2016 dengue fever case data. Based on the model, mapping of dengue fever in Makassar can be identified in each district and shows that Makassar City is still very vulnerable to dengue fever.

Analysis of the relative risk of the spread of dengue fever (DF) in Makassar city, Indonesia, needs to be done to see the which areas are at high risk of DF. Bayesian Autoregressive Conditional (CAR) is used in the mapping model of this disease. This model is Able to model of relative risk by taking into account the smoothing of that relative risk and entering spatial information to reduce the error of the estimated parameters in order the reliable relative risk models is Obtained. In this study, the relative risk value of the spread of DF was Analyzed using the localized Bayesian models CAR. Under this model the geographical mapping of DF in Makassar can be identified for each sub-district and shows that Makassar is still very vulnerable to DF.

In this paper, we study an extracellular process of a biochemical system such as batch ethanol fermentation system by considering an unstructured kinetic model with four different wellknown models for the specific growth rate of the yeast cells. Then, we fit the unstructured models to the experimental data for determining the appropriate model that can capture the dynamic behaviour of the batch ethanol fermentation experimental data. The fitting procedure is proceeded by minimising a least-squared error between the model solutions and the experimental data using a direct search method. Our simulations show that the unstructured model with Aiba-type structured model for the specific growth rate of the yeast cell has the best approximating ability to describe the dynamic of the batch ethanol fermentation data.