Carbon dioxide (CO 2) emissions is an environmental problem which leads to Earth's greenhouse effect. Much concerns with carbon dioxide emissions centered around the growing threat of global warming and climate change. This paper, however, presents a simple model development using multiple regression with interactions for estimating carbon dioxide emissions in Malaysia and Thailand. Five indicators over the period 1971-2006, namely energy use, GDP per capita, population density, combustible renewables and waste, and CO 2 intensity are used in the analysis. Progressive model selections using forward selection, backward elimination and stepwise regression are used to remove insignificant variables, with possible interactions. Model selection techniques are compared against the performance of eight criteria model selection process. Global test, Coefficient test, Wald test and Goodnessof-fit test are carried out to ensure that the best regression model is selected for further analysis. A numerical illustration is included to enhance the understanding of the whole process in obtaining the final best model.
Abstract-Global warming due to the rapid increase in greenhouse gas emissions, mainly carbon dioxide (CO 2 ), is a worldwide issue that leads to escalating pollutions and emerging diseases. The comparative performances of multiple regression (MR) and multivariate adaptive regression splines (MARS) for statistical modelling of CO 2 emissions are analyzed in ASEAN countries over the period of 1980-2007. The regression models are fitted individually for every potential variable investigated so as to find the best-fit parametric or non-parametric model. The results show a significant difference between the performance of MR and MARS models with the inclusion of interaction terms. The MARS model is computationally feasible and has better predictive ability than the MR model in predicting CO 2 emissions. In overall, MARS can be viewed as a modification of stepwise regression that enhances the latter's performance in the regression setting.
One of the threats of the world health is the infectious diseases. This leads to the raise of concern of the policymakers and disease researchers. Vaccination program is one of the methods to prevent the vaccine-preventable diseases and hence help to eradicate the diseases. The impact of the preventive actions is related to the human behavioral changes. Fear of the diseases will increase one’s incentive in taking the preventive actions to avoid the diseases. As human behavioral changes affecting the impact of the preventive actions, the individual-based model is constructed to incorporate the behavioral changes in disease modeling. The agents in the individual-based model are allowed to move randomly and interact with each other in the environment. The interactions will cause the disease viruses as well as the fearfulness to be spread in the population. In addition, the individual-based model can have different environment setups to distinguish the urban and rural areas. The results shown in this paper are divided into two subsections, which are the justification of using uniform distribution as random number generator, and the variation of disease spread dynamics in urban and rural areas. Based on the results, the uniform distribution is found to be sufficient in generating the random numbers in this model as there is no extreme outlier reported in the experiment. We have hypothesized the individuals in urban area to have higher level of fearfulness compared to those in rural area. However, the preliminary results of the survey conducted show a disagreement with the hypothesis. Nevertheless, the data collected still show two distinct classes of behavior. Thus, the distinction does not fall into the samples taken from rural or urban areas but perhaps more on the demographic factors. Therefore, the survey has to be study again and demographic factors have to be included in the survey as we could not distinguish the level of fearfulness by areas.
Abstract-Infectious disease is one of the health issues that threatens the world population. The spread of infectious diseases can be controlled via vaccination program and self-isolation as suggested by the public health organization. The individuals' incentives to take the control measures are depend on their fearfulness on the disease. This fearfulness or feeling of fear is associated with human behavior. Hence, the objective of this study is to incorporate human behavior in disease modeling. We also aimed to study the level of fearfulness in both subpopulations of receiving complete information and receiving incomplete information and its impact to the vaccination program. In this paper, a model is developed by using individualbased modelling approach to capture the human behavioral changes during disease outbreak. The analysis of the results presented the relationship between the fearfulness and the vaccination decision of the individual. Hence, the level of fearfulness needs to be examined in order to organize an effective vaccination program.
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