ARCHIVES
Original Article
Utilization of Climate Data Analytics in Healthcare Decision-Making Processes in Migori County, Kenya
Jared Murundu1
Fidelis Mukudi2
1 Department of Computer Science and Information Technology, Co-operative University of Kenya, Kenya. 2Department of Mathematical Sciences, Co-operative University of Kenya, Nairobi, Kenya.
Published Online: September-December 2025
Pages: 67-71
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403013
References
1. Ansah, E. W., Amoadu, M., Obeng, P., & Sarfo, J. O. (2024). Health systems response to climate change adaptation: A scoping review of global evidence. BMC Public Health, 24(1), 2015. https://doi.org/10.1186/s12889-024-19459-w
2. Ayanlade, A., Sergi, C. M., Sakdapolrak, P., Ayanlade, O. S., Di Carlo, P., Babatimehin, O. I., Weldemariam, L. F., & Jegede, M. O. (2022). Climate change engenders a better Early Warning System development across Sub-Saharan Africa: The malaria case. Resources, Environment and Sustainability, 10, 100080. https://doi.org/10.1016/j.resenv.2022.100080
3. Bogaert, P., Verschuuren, M., Van Oyen, H., & van Oers, H. (2021). Identifying common enablers and barriers in European health information systems. Health Policy, 125(12), 1517–1526. https://doi.org/10.1016/j.healthpol.2021.09.006
4. Chandra, G., & Mukherjee, D. (2022). Chapter 35—Effect of climate change on mosquito population and changing pattern of some diseases transmitted by them. In R. C. Sobti (Ed.), Advances in Animal Experimentation and Modeling (pp. 455–460). Academic Press. https://doi.org/10.1016/B978-0-323-90583-1.00030-1
5. Davis, A. P. (2025). Enhancing Resilience in the Healthcare Supply Chain: Lessons from IV Fluid Shortages in the Wake of Natural Disasters. Institute for Homeland Security. https://hdl.handle.net/20.500.11875/5091
6. Fang, Z., Tao, H., Mondal, S. K., Kundzewicz, Z., Zhai, J., & Jiang, T. (2025). Health Risks of Chronic Respiratory Diseases from Dry-Hot Extremes: A Global Study from 2001 to 2020 (SSRN Scholarly Paper No. 5296810). Social Science Research Network. https://doi.org/10.2139/ssrn.5296810
7. Karijo, E. K., Otieno, G. O., & Mogere, S. (2021). Determinants of Data Use for Decision Making in Health Facilities in Kitui County, Kenya. Quest Journal of Management and Social Sciences, 3(1), 63–75. https://doi.org/10.3126/qjmss.v3i1.37593
8. Kiptum, A., Mwangi, E., Otieno, G., Njogu, A., Kilavi, M., Mwai, Z., MacLeod, D., Neal, J., Hawker, L., O’Shea, T., Saado, H., Visman, E., Majani, B., & Todd, M. C. (2025). Advancing operational flood forecasting, early warning and risk management with new emerging science: Gaps, opportunities and barriers in Kenya. Journal of Flood Risk Management, 18(1), e12884. https://doi.org/10.1111/jfr3.12884
9. Lee, Y. W., Choi, J. W., & Shin, E.-H. (2021). Machine learning model for predicting malaria using clinical information. Computers in Biology and Medicine, 129, 104151. https://doi.org/10.1016/j.compbiomed.2020.104151
10. Longobardo, M., & SCHIATTARELLA, M. (2025). Humanitarian supply chains: An analysis of sc responses to natural disasters and large disruption events. https://www.politesi.polimi.it/handle/10589/239614
11. Lyu, Y., Xu, Q., & Liu, J. (2024). Exploring the medical decision-making patterns and influencing factors among the general Chinese public: A binary logistic regression analysis. BMC Public Health, 24(1), 887. https://doi.org/10.1186/s12889-024-18338-8
12. Mafwele, B. J., & Lee, J. W. (2022). Relationships between transmission of malaria in Africa and climate factors. Scientific Reports, 12(1), 14392. https://doi.org/10.1038/s41598-022-18782-9
13. Murthy, S., & Adhikari, N. K. (2013). Global Health Care of the Critically Ill in Low-Resource Settings. Annals of the American Thoracic Society, 10(5), 509–513. https://doi.org/10.1513/AnnalsATS.201307-246OT
14. Neta, G., Pan, W., Ebi, K., Buss, D. F., Castranio, T., Lowe, R., Ryan, S. J., Stewart-Ibarra, A. M., Hapairai, L. K., Sehgal, M., Wimberly, M. C., Rollock, L., Lichtveld, M., & Balbus, J. (2022). Advancing climate change health adaptation through implementation science. The Lancet Planetary Health, 6(11), e909–e918. https://doi.org/10.1016/S2542-5196(22)00199-1
15. Njoka, P. M. (2015). Factors influencing utilization of routine health data in evidence based decision making in Hiv/Aids services by public health facilities in Nakuru county [Thesis, University of Nairobi]. http://erepository.uonbi.ac.ke/handle/11295/90875
16. Njoroge, P. K. (2022). Regression Models in Malaria Cases Prediction Using Climatic Data [Thesis, University of Nairobi]. http://erepository.uonbi.ac.ke/handle/11295/167501
17. Olela, S., Makokha, G. L., & Obiero, K. (2024). Spatiotemporal Relationship between Variability in Selected Climate Parameters and Malaria Transmission Trends in Different Altitudes of Lower Lake Victoria Basin (SSRN Scholarly Paper No. 4704091). Social Science Research Network. https://papers.ssrn.com/abstract=4704091
18. Patz, J. A., Frumkin, H., Holloway, T., Vimont, D. J., & Haines, A. (2014). Climate Change: Challenges and Opportunities for Global Health. JAMA, 312(15), 1565–1580. https://doi.org/10.1001/jama.2014.13186
2. Ayanlade, A., Sergi, C. M., Sakdapolrak, P., Ayanlade, O. S., Di Carlo, P., Babatimehin, O. I., Weldemariam, L. F., & Jegede, M. O. (2022). Climate change engenders a better Early Warning System development across Sub-Saharan Africa: The malaria case. Resources, Environment and Sustainability, 10, 100080. https://doi.org/10.1016/j.resenv.2022.100080
3. Bogaert, P., Verschuuren, M., Van Oyen, H., & van Oers, H. (2021). Identifying common enablers and barriers in European health information systems. Health Policy, 125(12), 1517–1526. https://doi.org/10.1016/j.healthpol.2021.09.006
4. Chandra, G., & Mukherjee, D. (2022). Chapter 35—Effect of climate change on mosquito population and changing pattern of some diseases transmitted by them. In R. C. Sobti (Ed.), Advances in Animal Experimentation and Modeling (pp. 455–460). Academic Press. https://doi.org/10.1016/B978-0-323-90583-1.00030-1
5. Davis, A. P. (2025). Enhancing Resilience in the Healthcare Supply Chain: Lessons from IV Fluid Shortages in the Wake of Natural Disasters. Institute for Homeland Security. https://hdl.handle.net/20.500.11875/5091
6. Fang, Z., Tao, H., Mondal, S. K., Kundzewicz, Z., Zhai, J., & Jiang, T. (2025). Health Risks of Chronic Respiratory Diseases from Dry-Hot Extremes: A Global Study from 2001 to 2020 (SSRN Scholarly Paper No. 5296810). Social Science Research Network. https://doi.org/10.2139/ssrn.5296810
7. Karijo, E. K., Otieno, G. O., & Mogere, S. (2021). Determinants of Data Use for Decision Making in Health Facilities in Kitui County, Kenya. Quest Journal of Management and Social Sciences, 3(1), 63–75. https://doi.org/10.3126/qjmss.v3i1.37593
8. Kiptum, A., Mwangi, E., Otieno, G., Njogu, A., Kilavi, M., Mwai, Z., MacLeod, D., Neal, J., Hawker, L., O’Shea, T., Saado, H., Visman, E., Majani, B., & Todd, M. C. (2025). Advancing operational flood forecasting, early warning and risk management with new emerging science: Gaps, opportunities and barriers in Kenya. Journal of Flood Risk Management, 18(1), e12884. https://doi.org/10.1111/jfr3.12884
9. Lee, Y. W., Choi, J. W., & Shin, E.-H. (2021). Machine learning model for predicting malaria using clinical information. Computers in Biology and Medicine, 129, 104151. https://doi.org/10.1016/j.compbiomed.2020.104151
10. Longobardo, M., & SCHIATTARELLA, M. (2025). Humanitarian supply chains: An analysis of sc responses to natural disasters and large disruption events. https://www.politesi.polimi.it/handle/10589/239614
11. Lyu, Y., Xu, Q., & Liu, J. (2024). Exploring the medical decision-making patterns and influencing factors among the general Chinese public: A binary logistic regression analysis. BMC Public Health, 24(1), 887. https://doi.org/10.1186/s12889-024-18338-8
12. Mafwele, B. J., & Lee, J. W. (2022). Relationships between transmission of malaria in Africa and climate factors. Scientific Reports, 12(1), 14392. https://doi.org/10.1038/s41598-022-18782-9
13. Murthy, S., & Adhikari, N. K. (2013). Global Health Care of the Critically Ill in Low-Resource Settings. Annals of the American Thoracic Society, 10(5), 509–513. https://doi.org/10.1513/AnnalsATS.201307-246OT
14. Neta, G., Pan, W., Ebi, K., Buss, D. F., Castranio, T., Lowe, R., Ryan, S. J., Stewart-Ibarra, A. M., Hapairai, L. K., Sehgal, M., Wimberly, M. C., Rollock, L., Lichtveld, M., & Balbus, J. (2022). Advancing climate change health adaptation through implementation science. The Lancet Planetary Health, 6(11), e909–e918. https://doi.org/10.1016/S2542-5196(22)00199-1
15. Njoka, P. M. (2015). Factors influencing utilization of routine health data in evidence based decision making in Hiv/Aids services by public health facilities in Nakuru county [Thesis, University of Nairobi]. http://erepository.uonbi.ac.ke/handle/11295/90875
16. Njoroge, P. K. (2022). Regression Models in Malaria Cases Prediction Using Climatic Data [Thesis, University of Nairobi]. http://erepository.uonbi.ac.ke/handle/11295/167501
17. Olela, S., Makokha, G. L., & Obiero, K. (2024). Spatiotemporal Relationship between Variability in Selected Climate Parameters and Malaria Transmission Trends in Different Altitudes of Lower Lake Victoria Basin (SSRN Scholarly Paper No. 4704091). Social Science Research Network. https://papers.ssrn.com/abstract=4704091
18. Patz, J. A., Frumkin, H., Holloway, T., Vimont, D. J., & Haines, A. (2014). Climate Change: Challenges and Opportunities for Global Health. JAMA, 312(15), 1565–1580. https://doi.org/10.1001/jama.2014.13186
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