Spatio-temporal analysis of dengue cases in Sabah
Introduction: Dengue fever is a significant public health issue worldwide. Geographic Information System is a powerful tool in public health, allowing for the analysis and visualisation of spatial data to understand disease distribution and identify clusters of cases. Therefore, this study aims to d...
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| Format: | Article |
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Universiti Putra Malaysia, Fakulti Perubatan dan Sains Kesihatan
2023
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| author | Kunasagran, Priya Dharishini Syed Abdul Rahim, Syed Sharizman Jeffree, Mohammad Saffree Atil, Azman Hidrus, Aizuddin Mokti, Khalid Abd Rahim, Mohammad Aklil Muyou, Adora J. Mujin, Sheila Miriam Ali, Nabihah Md Taib, Norsyahida Mohd Zali, S Muhammad Izuddin Rabbani Dapari, Rahmat Azhar, Zahir Izuan Koay, Teng Khoon |
| author_facet | Kunasagran, Priya Dharishini Syed Abdul Rahim, Syed Sharizman Jeffree, Mohammad Saffree Atil, Azman Hidrus, Aizuddin Mokti, Khalid Abd Rahim, Mohammad Aklil Muyou, Adora J. Mujin, Sheila Miriam Ali, Nabihah Md Taib, Norsyahida Mohd Zali, S Muhammad Izuddin Rabbani Dapari, Rahmat Azhar, Zahir Izuan Koay, Teng Khoon |
| author_sort | Kunasagran, Priya Dharishini |
| collection | UPM |
| description | Introduction: Dengue fever is a significant public health issue worldwide. Geographic Information System is a powerful tool in public health, allowing for the analysis and visualisation of spatial data to understand disease distribution and identify clusters of cases. Therefore, this study aims to determine the spatiotemporal distribution of dengue cases in Sabah. Methods: Quantum Geospatial Information System (QGIS) and GeoDa software were used to determine the spatial distribution, pattern, and cluster analysis. Results: The spatial distribution of dengue cases shifted, with most cases concentrated on the east coast of Sabah. The distribution of dengue cases in Beluran, Tenom, Kota Marudu, Kudat, Keningau, and Papar changed from 2017 to 2020. The scatter plots of Moran’s index values were generated to analyse the spatial clustering of dengue cases in Sabah over four years: 2017 (Moran’s index = 0.271), 2018 (Moran’s index = 0.333), 2019 (Moran’s index = 0.367), and 2020 (Moran’s index = 0.294). The statistical significance of clustering was established by observing p-values below the threshold of 0.05 for all four years. Local indicators of spatial association showed the spatial autocorrelation pattern of high-high (hotspot) areas with elevated dengue incidence and low-low (cold-spot) areas with relatively lower dengue rates. Conclusion: This study has provided evidence of dengue case distribution patterns, spatial clustering, and hotspot and coldspot areas. Prioritising these clusters can improve planning and resource allocation for more efficient dengue prevention and control. |
| first_indexed | 2024-09-25T03:41:09Z |
| format | Article |
| id | upm.eprints-108933 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-09-25T03:41:09Z |
| publishDate | 2023 |
| publisher | Universiti Putra Malaysia, Fakulti Perubatan dan Sains Kesihatan |
| record_format | dspace |
| spelling | upm.eprints-1089332024-05-11T15:08:00Z http://psasir.upm.edu.my/id/eprint/108933/ Spatio-temporal analysis of dengue cases in Sabah Kunasagran, Priya Dharishini Syed Abdul Rahim, Syed Sharizman Jeffree, Mohammad Saffree Atil, Azman Hidrus, Aizuddin Mokti, Khalid Abd Rahim, Mohammad Aklil Muyou, Adora J. Mujin, Sheila Miriam Ali, Nabihah Md Taib, Norsyahida Mohd Zali, S Muhammad Izuddin Rabbani Dapari, Rahmat Azhar, Zahir Izuan Koay, Teng Khoon Introduction: Dengue fever is a significant public health issue worldwide. Geographic Information System is a powerful tool in public health, allowing for the analysis and visualisation of spatial data to understand disease distribution and identify clusters of cases. Therefore, this study aims to determine the spatiotemporal distribution of dengue cases in Sabah. Methods: Quantum Geospatial Information System (QGIS) and GeoDa software were used to determine the spatial distribution, pattern, and cluster analysis. Results: The spatial distribution of dengue cases shifted, with most cases concentrated on the east coast of Sabah. The distribution of dengue cases in Beluran, Tenom, Kota Marudu, Kudat, Keningau, and Papar changed from 2017 to 2020. The scatter plots of Moran’s index values were generated to analyse the spatial clustering of dengue cases in Sabah over four years: 2017 (Moran’s index = 0.271), 2018 (Moran’s index = 0.333), 2019 (Moran’s index = 0.367), and 2020 (Moran’s index = 0.294). The statistical significance of clustering was established by observing p-values below the threshold of 0.05 for all four years. Local indicators of spatial association showed the spatial autocorrelation pattern of high-high (hotspot) areas with elevated dengue incidence and low-low (cold-spot) areas with relatively lower dengue rates. Conclusion: This study has provided evidence of dengue case distribution patterns, spatial clustering, and hotspot and coldspot areas. Prioritising these clusters can improve planning and resource allocation for more efficient dengue prevention and control. Universiti Putra Malaysia, Fakulti Perubatan dan Sains Kesihatan 2023 Article PeerReviewed Kunasagran, Priya Dharishini and Syed Abdul Rahim, Syed Sharizman and Jeffree, Mohammad Saffree and Atil, Azman and Hidrus, Aizuddin and Mokti, Khalid and Abd Rahim, Mohammad Aklil and Muyou, Adora J. and Mujin, Sheila Miriam and Ali, Nabihah and Md Taib, Norsyahida and Mohd Zali, S Muhammad Izuddin Rabbani and Dapari, Rahmat and Azhar, Zahir Izuan and Koay, Teng Khoon (2023) Spatio-temporal analysis of dengue cases in Sabah. Malaysian Journal of Medicine and Health Sciences, 19 (suppl.20). pp. 88-94. ISSN 1675-8544 https://medic.upm.edu.my/upload/dokumen/2024020210453611_2023-0520.pdf |
| spellingShingle | Kunasagran, Priya Dharishini Syed Abdul Rahim, Syed Sharizman Jeffree, Mohammad Saffree Atil, Azman Hidrus, Aizuddin Mokti, Khalid Abd Rahim, Mohammad Aklil Muyou, Adora J. Mujin, Sheila Miriam Ali, Nabihah Md Taib, Norsyahida Mohd Zali, S Muhammad Izuddin Rabbani Dapari, Rahmat Azhar, Zahir Izuan Koay, Teng Khoon Spatio-temporal analysis of dengue cases in Sabah |
| title | Spatio-temporal analysis of dengue cases in Sabah |
| title_full | Spatio-temporal analysis of dengue cases in Sabah |
| title_fullStr | Spatio-temporal analysis of dengue cases in Sabah |
| title_full_unstemmed | Spatio-temporal analysis of dengue cases in Sabah |
| title_short | Spatio-temporal analysis of dengue cases in Sabah |
| title_sort | spatio temporal analysis of dengue cases in sabah |
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