Spatiotemporal analysis has been used widely to explain some geographic phenomenon, especially in an epidemiology study. Spatial and temporal autocorrelation coefficients are usually used to assess the spatial and temporal dependencies in set geographic events. However, those statistics are usually computed separately and may lead to the misleading conclusion. Analysing spatiotemporal autocorrelation would be useful to understand the geographical evolution simultaneously. Spatiotemporal autocorrelation can be used to identify the spatiotemporal clustering and outlier via local spatiotemporal autocorrelation. This paper develops a method to estimate and test the local spatiotemporal autocorrelation based on the local spatial Moran’s Index. Randomization permutation test is used to obtain the p-value which is used to construct the disease clustering. The method was applied to identify the spatiotemporal clustering and outlier detection for dengue disease data in Bandung city. Based on image analysis, this method presents the better result compare than the local spatial Moran’s Index which is done for every time separately.

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Panel data models have been applied widely in many subject areas related to economic, social, and epidemiology. In some cases (e.g. epidemiology studies), the phenomena encountered have a complex relationship structured. The risk factors such as house index, healthy behaviour index, rainfall and the other risk factors of particular infectiouse disease may have different effect on the outcome due to the heterogeneity of crossection units. The effect of the covariates on outcome could vary over individual and time units. This condition is called as a non-stationary or instability relationship problem. This problem leads to bias and inefficient of the estimators. It is important to examine the heterogeneous coefficients model for avoiding inefficient estimator. We present in detail a statistical estimation procedure of the heterogeneous coefficients for fixed effect panel data model by means of the hierarchical Bayesian estimation approach. The challenges of the Bayesian approaches are finding the joint posterior distribution and developing the algorithm for estimating the parameters of interest. We find that the joint posterior distribution of the heterogeneous coefficients fixed effect panel data model does not follow any standard known distribution form. Consequently, the analytical solution cannot be applied and simulation approach of Markov Chain Monte Carlo (MCMC) was used. We present the MCMC procedure covering the derivation of the full conditional distribution of the parameters model and present step-by-step the Gibbs sampling algorithm. The idea of this preliminary research can be applied in various fields to overcome the non-stationarity problem.

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