Indonesia's position as one of the high burden countries for the infectious disease, tuberculosis (TB), has caused TB to be a major health problem in Indonesia. As means to control the number of TB cases, it becomes important for the government to identify factors associated with it. Commonly, multiple linear regression models are used to evaluate the linear relationship between the identified factors and the number of TB cases. Unfortunately, this model does not have the ability to expose the spatial variation in the data. Therefore, this study proposes to implement a spatial model: a model that takes the geographical location in the model. This research examined two types of geographically weighted models (GWM): geographically weighted regression (GWR) and mixed geographically weighted regression (MGWR). These spatial models assign weights to observations based on its' geographical location. These two models were constructed to evaluate the relationship between the prevalence of TB in regency/city in Java in 2017 and the factors associated with it: population size, success rate of TB treatment, percentage of toddlers receiving BCG vaccine, percentage of HIV patient, percentage of household with adequate sanitation, percentage of poor people and the number of public health centre per one hundred thousand people. Akaike's Information Criterion (AIC) and adjusted R2 were used to assess the model performances. We found that the GWR model fits the data better than MGWR, as it has a smaller AIC value (1558.67) and a higher adjusted R2 (0.754). It is also found that BCG vaccine is important to reduce the prevalence of TB, as the percentage of toddlers receiving BCG vaccine is negatively associated with it. Among the examined areas, Jakarta is the area with the highest association between the percentage of toddlers receiving BCG vaccine and the prevalence of TB.
|Journal||Journal of Physics: Conference Series|
|Publication status||Published - 7 Jan 2021|
|Event||10th International Conference and Workshop on High Dimensional Data Analysis, ICW-HDDA 2020 - Sanur-Bali, Indonesia|
Duration: 12 Oct 2020 → 15 Oct 2020