Trypanosoma evansi is a protozoan parasite which infects livestock in Malaysia. The study produces an initial report of trypanosome in Peninsular Malaysia; with the focus on the Perak state due to the high number of T.evansi cases were reported. The study was conducted by integrating geographical information system (GIS) and result showed that a total of 67 cases were reported positive T.evansi within the period of study (2000-2013). ArcGIS 9.3 was used to analyze the data and develop a choropleth map shows the distribution of T.evansi. Information obtained from this study would be a valuable contribution to develop a strategy and will enhance understanding about the epidemiology and distribution of Trypanosoma evansi in Malaysia and assist the authorities in improving their control strategies. INTRODUCTION The protozoan parasite, Trypanosoma evansi is transmitted mechanically between animals by tabanid flies (Family : Tabanidae). T.evansi causes the disease surra, which produce significant mortality and production losses in a variety of mammals in endemic countries. In an effort committed to tackling the problem, Parasitology and haematology section in VRI have conducted several studies regarding the transmission, vector and statistical study on production losses in a variety of animal at local farms. The study was conducted by integrating geographic information system (ArcGIS 9.3) software to produce the geo-mapping of the positive T.evansi cases in Malaysia during 2000 to 2013.

The paper focuses on a numerical method for detecting, visualizing and monitoring abnormal cell growth using large-scale mathematical simulations. The discretization of multi-dimensional partial differential equation (PDE) is based on finite difference method. The predictor system depending on users input data via a user interface, generating the initial and boundary condition generated from parabolic or elliptic type of PDE. The processing large sparse matrixes are based on multiprocessor computer systems for abnormal growth visualization. The multi-dimensional abnormal cell has produced the numerical analysis and understanding results at the target area for the potential improvement of detection and monitoring the growth. The development of the prediction system is the combinations of the parallel algorithms, open source software on Linux environment and distributed multiprocessor system. The paper ends with a concluding remark on the parallel performance evaluations and numerical analysis in reducing the execution time, communication cost and computational complexity.

Brain tumour is one of the prevalent cancers in the world that lead to death. Based on the present knowledge of the properties of gliomas, mathematical models have been developed by researchers to quantify the proliferation and invasion dynamics of glioma within anatomically accurate heterogeneous brain tissue. This paper focuses on the implementation of parallel algorithm for the simulation of brain tumours growth using one dimensional parabolic equation, designed on a distributed parallel computer system. The numerical finite-difference method is focused on a design of a platform for discretising the parabolic equations. The result of finite difference approximation using explicit, Crank-Nicolson and fully implicit methods will be presented graphically. The implementation of parallel algorithm based on parallel computing system is used to capture the growth of brain tumour. Parallel Virtual Machine (PVM) is emphasized as communication platform in parallel computer systems. The software system functions to enable a collection of heterogeneous computers to be used as synchronized and flexible concurrent computational resource. The parallel performance measurement will be analysed from the aspect of speedup, efficiency, effectiveness and temporal performance.