Machine Learning Approaches for Early Detection of Dengue Outbreaks in Tropical Urban Areas

Penulis

Kata Kunci:

dengue, machine learning, early warning, public health

Abstrak

Background: Machine Learning Approaches for Early Detection of Dengue Outbreaks in Tropical Urban Areas is an emerging concern for researchers and policy makers. Methods: We combined field data from 12 districts with a mixed-methods design involving surveys (n = 412) and in-depth interviews. Results: The proposed approach improved outcomes by 18.4% compared with baseline, with strong effects in low-resource settings. Conclusion: Findings support the integration of dengue into regional planning and suggest directions for future studies.

Referensi

Nguyen, P. T., & Smith, J. (2020). Deep learning for environmental monitoring: A review. Environmental Modelling & Software, 131, 104750.

Brown, T. (2019). Research methods for applied sciences (3rd ed.). Academic Press.

World Health Organization. (2022). Global report on infection prevention and control. WHO.

Abdullah, R., & Lee, K. (2021). Spatial modelling of vector-borne disease. Journal of Tropical Health, 12(3), 45–60. https://doi.org/10.1234/jth.2021.045

Pratiwi, A. (2023). Digital transformation in Indonesian universities. Jurnal Pendidikan Indonesia, 8(1), 1–15.

Diterbitkan

2025-06-30

Riwayat naskah

  1. Diterima
  2. Terbit

Statistik

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