Machine Learning Approach to Predict the Dengue Cases Based on Climate Factors

  • Muhammad Nasir Department of Epidemiology, College of Public Health, University of College London
  • Shobiechah Aldillah Wulandhari Malaria Consortium, Thailand
  • Dhihram Tenrisau Health Data Science, London School of Health and Tropical Medicine
  • Muhammad Haris Ibrahim Department of Health Informatics, School of Information and School of Public Health, University of Michigan
  • Ajeng Rahastri Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada
  • Nilna Sa’adatar Rohmah Field Epidemiology Training Program, Universitas Gadjah Mada, Indonesia
  • Asik Surya Arboviroses Teamwork, Indonesia’s Ministry of Health
  • Burhanuddin Thohir Arboviroses Teamwork, Indonesia’s Ministry of Health
  • Desfalina Aryani Arboviroses Teamwork, Indonesia’s Ministry of Health
  • Muhammad Firdaus Kasim Nuffield Department of Medicine, University of Oxford
Keywords: Artificial Intelligence;, Dengue;, Forecasting;, Weather;, Disease prediction


Dengue is a global health issue threatening public health, particularly in developing countries. Effective disease surveillance is critical to anticipate impending outbreaks and implement appropriate control responses. However, delays in dengue case reporting are frequent due to human resource shortfalls. Improved outbreak predictive capacity also requires additional input on vector presence and abundance, which is currently not captured in the surveillance platform. Thus, we developed a prototype AI application, “Dengue Forecasting", that leverages machine learning methods in filing the dengue case report and incorporates dengue vector and climatic parameters. This application simplifies the recording of dengue cases, vector abundance (Angka Bebas Jentik/ABJ), and selected climatic variables (sun exposure, temperature, humidity, wind speed, and precipitation) in Bandung City. The relevant data were extracted from Indonesia’s Ministry of Health and the Meteorological, Climatological, and Geophysical Agency. The entire process, from developing the model to deployment, was conducted under R programming language version 4.2.2 using packages (caret, The linear regression model demonstrated the highest precision (RMSE= 268.32 and MAE= 164.1) in predicting the dengue cases and outbreaks. We also applied this to the application deployment. “Dengue Forecasting” has the potential to assist policymakers at the district level, complementing Dengue EWARS, in anticipating and mitigating dengue outbreaks, especially in Bandung City.

Author Biographies

Muhammad Nasir, Department of Epidemiology, College of Public Health, University of College London (The space provided cannot detect the orchid number) 

Afiliasi 2: Faculty of Medicine, Tadulako University, Palu, Indonesia 

Afiliasi 3: Public Health Literature Club, Indonesia.

Shobiechah Aldillah Wulandhari, Malaria Consortium, Thailand

Afilisasi 2: Public Health Literature Club, Indonesia.

ORCID: 0000-0003-4712-6041

Dhihram Tenrisau, Health Data Science, London School of Health and Tropical Medicine

Afiliasi 2: Public Health Literature Club, Indonesia.

ORCID: 0000-0003-2621-1108

Ajeng Rahastri, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada

Afilisasi 2: Public Health Literature Club, Indonesia.

ORCID: 0000-0001-5704-2736

Nilna Sa’adatar Rohmah, Field Epidemiology Training Program, Universitas Gadjah Mada, Indonesia

Afiliasi 2: Public Health Literature Club, Indonesia.
ORCID: 0009-0004-5297-2343


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How to Cite
Nasir M, Aldillah Wulandhari S, Tenrisau D, Haris Ibrahim M, Rahastri A, Sa’adatar Rohmah N, Surya A, Thohir B, Aryani D, Firdaus Kasim M. Machine Learning Approach to Predict the Dengue Cases Based on Climate Factors. woh [Internet]. 2024May12 [cited 2024May23];:203-14. Available from: