Projections of Cardiovascular Disease Mortality in Peninsular Malaysia Using Statistical Downscaling Based on Cluster Approach

Abstract

Projecting the mortality of cardiovascular disease in future is crucial in preparing the mitigation strategies. The purpose of this research is to estimate number of deaths of the cardiovascular disease in Peninsular Malaysia based on future temperature projections using the cluster approach. Ward s method is used to identify the number of clusters of 45 meteorological stations by calculating the shortest distance between the two coordinates of the stations. The output of global climate model (GCM) is incredibly useful for the projection of future temperature, but the large bias in the observational datasets may lead to inaccurate projection. To tackle the bias, a good fitted model for temperature series is important in order to ensure that the mean and variability of the observed series are well captured. It is important to estimate the parameters for each cluster precisely. Furthermore, a good fitted model for temperature series is also crucial in order to ensure that the mean and variability of the observations are well captured. Thus, this study proposed the appropriate statistical distribution for the temperature series to be associated in the bias correction method (BCM) using the quantile mapping (QM) technique to reduce the biases between observations and historical GCM temperature data series. Next, Ward’s method is applied to determine the optimal number of clusters for Peninsular Malaysia. The results have shown that the proposed model is able to reduce the temperature series biases between the GCM and the observations. Six clusters throughout Peninsular Malaysia have been selected based on Ward’s method. The projection number of deaths of cardiovascular disease under is estimated to increase between 2006 and 2100 in all clusters across Peninsular Malaysia, based on the temperature projections.