Research Article
Spatial Modelling of Pulmonary TB Distribution in Indonesia Using on Environmental and Socio-economic Variables
@INPROCEEDINGS{10.4108/eai.5-10-2022.2328785, author={Helina Helmy and Muhammad Totong Kamaluddin and Iskhaq Iskandar and Suheryanto Suheryanto and Irfannuddin Irfannuddin and Novrikasari Novrikasari}, title={Spatial Modelling of Pulmonary TB Distribution in Indonesia Using on Environmental and Socio-economic Variables}, proceedings={Proceedings of the 3rd Sriwijaya International Conference on Environmental Issues, SRICOENV 2022, October 5th, 2022, Palembang, South Sumatera, Indonesia}, publisher={EAI}, proceedings_a={SRICOENV}, year={2023}, month={4}, keywords={spatial distribution pulmonary tb cases environmental socio-economic geographically weighted regression}, doi={10.4108/eai.5-10-2022.2328785} }
- Helina Helmy
Muhammad Totong Kamaluddin
Iskhaq Iskandar
Suheryanto Suheryanto
Irfannuddin Irfannuddin
Novrikasari Novrikasari
Year: 2023
Spatial Modelling of Pulmonary TB Distribution in Indonesia Using on Environmental and Socio-economic Variables
SRICOENV
EAI
DOI: 10.4108/eai.5-10-2022.2328785
Abstract
Tuberculosis (TB) is a contagious epidemic globally. Based on environmental and socio-economic data, this study aims to develop a spatial model and investigate the factors influencing the spread of pulmonary TB cases. Using the GWR method, this study analyzes influencing aspects and estimates the total of pulmonary TB cases in Indonesia involving seven variables: population density, poverty index, number of health facilities, medical personnel, rainfall, solar radiation, and road network. The results show that the model is accurate with R2 values of 0.953 and adjusted R2 values of 0.940. Spatial analysis shows that Indonesia has an average number of pulmonary TB patients with 4,856 cases. Several factors dominate the distribution of pulmonary TB cases in Indonesia: population density, poverty index, number of health facilities, medical personnel, road network, rainfall, and solar radiation. The resulting GWR model can explain the dependent variable by more than 90%. Environmental and socio-economic variables can be adopted to develop spatial models of infectious diseases at an urban level.