Issue |
E3S Web Conf.
Volume 501, 2024
International Conference on Computer Science Electronics and Information (ICCSEI 2023)
|
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Article Number | 02005 | |
Number of page(s) | 7 | |
Section | Information Technology for Sustainability | |
DOI | https://doi.org/10.1051/e3sconf/202450102005 | |
Published online | 18 March 2024 |
An application of spatial analysis and GIS in Tuberculosis (TB) cases in Central Luzon, Philippines
1 Mathematics and Statistics Department, De La Salle University-Dasmarinas, City of Dasmarinas 4114 Cavite, Philippines
2 Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila 2 Manila, Manila 1000, Philippines
* Corresponding author: nanacion@up.edu.ph
According to WHO (2022), tuberculosis (TB) is the primary cause of ill health and the leading cause of death globally. It is estimated that approximately a quarter of the world’s population has been infected. With 372,367 cases of tuberculosis in 2022, the Philippines is among the top eight countries that accounted for more than 66% of all TB cases worldwide. Region III, along with NCR and Region IV-A, is one of the regions in the Philippines with the highest incidence of TB. This paper utilized the geographical information system (GIS) for easier visualization, and Getis-Ord Analysis, a type of spatial analysis tool for quick interpretations to provide an evidence-based framework for TB response. The spatial analysis was conducted to (1) determine if there are clusters of TB cases in the region across various periods and (2) determine if there are hot spots of TB cases in the most recent TB data covering 2019, 2020, and 2021. The results indicate that only the 2019 TB cases exhibit significant non-random clusters. It is recommended that further investigation be conducted to determine if the spatial clustering in 2019 is associated directly or indirectly with the El Niño event that occurred that year. On the other hand, the non-significance of the results for the years 2020 and 2021 may be attributed to the underreporting due to the implemented health protocols implemented to minimize the spread of COVID-19 which affected the accuracy of the reported cases. The results of the paper may be used for optimal resource allocation in addressing the spread of the disease.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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