Disease Prediction Based on Climatic Changes at Different Locations
DOI:
https://doi.org/10.15662/IJEETR.2026.0802084Keywords:
Climate change, Infectious diseases, Disease prediction, Machine learning, Vector-borne diseases, Early warning systems, Public health surveillance, Predictive modelsAbstract
This studies how climate change influences the spread of infectious diseases across different places. Changes in temperature, rainfall, humidity, and extreme weather conditions directly affect the transmission of diseases caused by insects, contaminated water, air, and animals. Instead of analyzing a single disease, this project focuses on predicting multiple diseases together, which helps in understanding the complex relationship between climate and public health. Modern techniques such as machine learning and statistical analysis are used to identify disease patterns, detect high-risk locations, and support early warning systems. Rising temperatures allow disease-carrying insects to spread into new regions, heavy rainfall increases the risk of water-borne diseases, and changing climate conditions influence the spread of respiratory illnesses. Although prediction models show strong potential, challenges remain due to limited data availability, regional climate differences, and the interaction between multiple disease types. To address these challenges, this project proposes a multi-disease prediction system that combines climate data, environmental factors, and disease records. For implementation, models such as Linear Regression, Random Forest, and Gradient Boosting are used to analyze trends and predict disease risk based on climatic changes. By integrating these models with public health data, the system can help authorities prepare better responses and reduce the impact of climate-related disease outbreaks
References
1. Kaur, G., Ghoshal, S., Marbate, R., Malviya, N., Kaur, A., Vaisakh, S. B., Srivastava, A. K., & Singh, M.“EpiClim: Weekly District-Wise All-India Multi-Epidemics Climate-Health Dataset for Accelerated Geo Health Research”, Elsevier Preprint (arXiv), pp. 1–18, 2025.
2. [Indhumathi, K., & Sathesh Kumar, K. “A Review on Prediction of Seasonal Diseases Based on Climate Change Using Big Data”, Materials Today: Proceedings, Vol. 37, pp. 2648–2652, 2021.
3. Van de Vuurst, P., & Escobar, L. E.“Climate Change and Infectious Disease: A Review of Evidence and Research Trends”, Infectious Diseases of Poverty, Vol. 12, Article 51, 2023.
4. Rees, E. E., Ng, V., Gachon, P., Mawudeku, A., McKenney, D., Pedlar, J., Yemshanov, D., Parmely, J., & Knox, J.“Risk Assessment Strategies for Early Detection and Prediction of Infectious Disease Outbreaks Associated with Climate Change”, Canada Communicable Disease Report (CCDR), Vol. 45(5), pp. 119–126, 2019.
5. Makkar, G.“Real-Time Disease Forecasting Using Climatic Factors: Supervised Analytical Methodology”, IEEE Conference Proceedings, pp. 1–5, 2018.
6. E. Kamana, J. Zhao, and D. Bai, “Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study,” vol. 12, no. 6, 2022.
7. Van de Vuurst and Escobar , “Climate change and infectious disease: a review of evidence and research trends”, 2023.
8. Nelson, S., & Jenkins, A., et al. “Predicting Climate-Sensitive Water-Related Disease Trends Based on Health, Seasonality and Weather Data in Fiji”, The Journal of Climate Change and Health, 2022.
9. Yenew, C., Bayeh, G. M., Gebeyehu, A. A., et al. “Scoping Review on Assessing Climate-Sensitive Health Risks”, BMC Public Health, Vol. 25, 2025.
10. Zhang, T., & Liu, J.“Impact of Climate Change on Infectious Disease Transmission: A CiteSpace- and VOSviewer-Based Visualization of Enteric, Respiratory and Vector-Borne Infectious Disease Studies”, Frontiers in Climate, Vol. 7, Article 1703904, pp. 1–24, 2025.
11. F. Gargano, R. Brunetti, M. Buonanno, et al., “Temporal analysis of climate change impact on the spread and prevalence of vector-borne diseases,” Microorganisms, vol. 13, no. 2, pp. 1–18, Feb. 2025, doi: 10.3390/microorganisms13020449.
12. S. A. Inam, M. A. Hossain, and R. J. Rocklöv, “Artificial intelligence and machine learning approaches for predicting climate-sensitive infectious diseases: A systematic review,” BMC Public Health, vol. 24, no. 1, pp. 1–20, 2024, doi: 10.1186/s12889-024-17965-4.
13. R. K. D. Agyarko, D. Kithinji, and K. B. Nsarhaza, “Climate change and the rise of emerging and re-emerging infectious diseases in Africa: A comprehensive review,” International Journal of Environmental Research and Public Health, vol. 22, no. 6, pp. 1–19, 2025, doi: 10.3390/ijerph22060903.
14. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
15. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
16. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
17. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
18. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
19. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
20. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
21. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
22. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
23. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
24. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
25. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
26. F. Pennisi, G. Lo Giudice, and A. C. Ricciardi, “Artificial intelligence for forecasting mosquito-borne viral diseases under climate change: A systematic review,” Machine Learning and Knowledge Extraction, vol. 8, no. 1, pp. 1–22, 2026, doi: 10.3390/make8010015.
27. S. Haque, J. J. Rocklöv, and R. J. Lowe, “Climate-driven infectious disease early warning systems: A systematic review of methods, challenges, and future directions,” Environment International, vol. 184, pp. 108503, 2024, doi: 10.1016/j.envint.2024.108503.
28. Socrates, S., Shanmugapriya, M., Murugeshwari, B., & Angalaeswari, S. (2024). Efficient Design for Implantable Device Constant Current Induction Doubly Fed Generating Incorporating Grid Connectivity. In Intelligent Solutions for Sustainable Power Grids (pp. 382-392). IGI Global Scientific Publishing.
29. Sugumar, R. (2026). Performance Optimization Frameworks for Financial Web Platforms with Real-Time Transaction Processing. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(2), 600-611.
30. Anbazhagan, K. (2025). AI Driven Zero Trust Security Model for Enterprise Data Protection and Intelligent Infrastructure Management. International Journal of Technology, Management and Humanities, 11(03), 101-107.
31. Prabha, P. S., & Rengarajan, A. (2025). ENHANCING CLOUD RESOURCE ALLOCATION WITH VISION TRANSFORMER, DEEP REINFORCEMENT LEARNING, AND IMPROVED SHRIKE OPTIMIZATION ALGORITHM. Corrosion Management ISSN: 1355-5243, 35(2), 233-245.
32. Vimal, V. R., & Banerjee, J. S. (2025). Integrating PSO, GA, and ACO for Optimized ECG Feature Selection and Classification of Cardiac Disorders. SGS-Engineering & Sciences, 1(5).
33. Anbazhagan, K. (2025). Next-Generation Enterprise Cloud AI for Healthcare: Secure CNN Pipelines and Privacy Controls. International Journal of Future Innovative Science and Technology (IJFIST), 8(6), 15980.
