Application of Spatial Regression Model for Modeling Measles Case in Indonesia

Abstract

Measles is also known as morbili in Latin and measles in English. Measles, in the past is considered as something that must be experienced by every child, they assume, that measles can heal itself if it was already out, so that children with measles do not need to be treated. This study examines the case of measles and the causes of measles. The variables used in the study were cases of measles (Y), population density (X1), immunization coverage (X2), average incidence (X3), and number of deaths (X4) in Indonesia covering all provinces. The study examined the pattern of spread, then given a SEM application to identify how much influence the measles factor can affect the case of measles in Indonesia. The results of the study show that Measles Cases in Indonesia have a regional grouping pattern. The modeling results using SEM show lambda and all significant variables. The SEM model produced AIC of 462,429 which was better than the regression of the SLM model with AIC of 467,499.