GIS-Enhanced Bayesian Reinforcement Learning for Vector-Borne Infectious Disease Transmission

Kipngetich Gideon; Victor Muthama Musau; Margaret Wambui Kinyua

doi:doi:10.11648/j.ijdsa.20261202.12

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GIS-Enhanced Bayesian Reinforcement Learning for Vector-Borne Infectious Disease Transmission

Kipngetich Gideon^*

, Victor Muthama Musau, Margaret Wambui Kinyua

Published in International Journal of Data Science and Analysis (Volume 12, Issue 2)

Received: 12 March 2026 Accepted: 26 March 2026 Published: 10 June 2026

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Abstract

Vector-borne infectious diseases has continue to pose a significant challenge to global public health, and accurate and timely transmission prediction are crucial for effective intervention and control. The development of accurate and efficient vector-borne infectious disease predictive models has been the current trend in disease modeling. Modeling transmission, however, has assumed a discrete transmission space, which is not always ideal in the real world, and little attention has been paid to overestimation biases in predictions. The effectiveness of a predictive model is also determined by its capability to capture a significant number of data characteristics to enhance robust and accurate prediction of cases. The study proposed the application of a GIS-enhanced Bayesian Reinforcement learning model for the transmission of vector-borne infectious diseases, and model performance assessment was determined. The Bayesian quantifies the uncertainty in the parameters of models, and min-max ensemble Q-value estimation minimizes overestimation bias in the model. Simulation study was used to evaluate model performance, success rate, and interaction rate. The findings show that vector and human can avoid interaction with a success rate of 96.2% when human select combined intervention actions spray repellent, insecticide treated nets, larval management, and vaccination. Other variables such as education, wealth index, community participation, and gender empowerment significantly influence the transmission of the disease in the area. The model demonstrates a better performance in describing the transmission of the disease, therefore setting the stage for future research in predictive modeling within sub-Saharan disease-prone regions. The model can be used to determine the appropriate actions that the human should adopt to reduce human-vector interaction.

Published in	International Journal of Data Science and Analysis (Volume 12, Issue 2)
DOI	10.11648/j.ijdsa.20261202.12
Page(s)	25-36
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Geographic Information System, Bayesian, Reinforcement Learning, Infectious Disease, Transmission

References

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Gideon, K., Musau, V. M., Kinyua, M. W. (2026). GIS-Enhanced Bayesian Reinforcement Learning for Vector-Borne Infectious Disease Transmission. International Journal of Data Science and Analysis, 12(2), 25-36. https://doi.org/10.11648/j.ijdsa.20261202.12

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ACS Style

Gideon, K.; Musau, V. M.; Kinyua, M. W. GIS-Enhanced Bayesian Reinforcement Learning for Vector-Borne Infectious Disease Transmission. Int. J. Data Sci. Anal. 2026, 12(2), 25-36. doi: 10.11648/j.ijdsa.20261202.12

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AMA Style

Gideon K, Musau VM, Kinyua MW. GIS-Enhanced Bayesian Reinforcement Learning for Vector-Borne Infectious Disease Transmission. Int J Data Sci Anal. 2026;12(2):25-36. doi: 10.11648/j.ijdsa.20261202.12

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@article{10.11648/j.ijdsa.20261202.12,
  author = {Kipngetich Gideon and Victor Muthama Musau and Margaret Wambui Kinyua},
  title = {GIS-Enhanced Bayesian Reinforcement Learning for Vector-Borne Infectious Disease Transmission
},
  journal = {International Journal of Data Science and Analysis},
  volume = {12},
  number = {2},
  pages = {25-36},
  doi = {10.11648/j.ijdsa.20261202.12},
  url = {https://doi.org/10.11648/j.ijdsa.20261202.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20261202.12},
  abstract = {Vector-borne infectious diseases has continue to pose a significant challenge to global public health, and accurate and timely transmission prediction are crucial for effective intervention and control. The development of accurate and efficient vector-borne infectious disease predictive models has been the current trend in disease modeling. Modeling transmission, however, has assumed a discrete transmission space, which is not always ideal in the real world, and little attention has been paid to overestimation biases in predictions. The effectiveness of a predictive model is also determined by its capability to capture a significant number of data characteristics to enhance robust and accurate prediction of cases. The study proposed the application of a GIS-enhanced Bayesian Reinforcement learning model for the transmission of vector-borne infectious diseases, and model performance assessment was determined. The Bayesian quantifies the uncertainty in the parameters of models, and min-max ensemble Q-value estimation minimizes overestimation bias in the model. Simulation study was used to evaluate model performance, success rate, and interaction rate. The findings show that vector and human can avoid interaction with a success rate of 96.2% when human select combined intervention actions spray repellent, insecticide treated nets, larval management, and vaccination. Other variables such as education, wealth index, community participation, and gender empowerment significantly influence the transmission of the disease in the area. The model demonstrates a better performance in describing the transmission of the disease, therefore setting the stage for future research in predictive modeling within sub-Saharan disease-prone regions. The model can be used to determine the appropriate actions that the human should adopt to reduce human-vector interaction.
},
 year = {2026}
}

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TY  - JOUR
T1  - GIS-Enhanced Bayesian Reinforcement Learning for Vector-Borne Infectious Disease Transmission

AU  - Kipngetich Gideon
AU  - Victor Muthama Musau
AU  - Margaret Wambui Kinyua
Y1  - 2026/06/10
PY  - 2026
N1  - https://doi.org/10.11648/j.ijdsa.20261202.12
DO  - 10.11648/j.ijdsa.20261202.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 25
EP  - 36
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20261202.12
AB  - Vector-borne infectious diseases has continue to pose a significant challenge to global public health, and accurate and timely transmission prediction are crucial for effective intervention and control. The development of accurate and efficient vector-borne infectious disease predictive models has been the current trend in disease modeling. Modeling transmission, however, has assumed a discrete transmission space, which is not always ideal in the real world, and little attention has been paid to overestimation biases in predictions. The effectiveness of a predictive model is also determined by its capability to capture a significant number of data characteristics to enhance robust and accurate prediction of cases. The study proposed the application of a GIS-enhanced Bayesian Reinforcement learning model for the transmission of vector-borne infectious diseases, and model performance assessment was determined. The Bayesian quantifies the uncertainty in the parameters of models, and min-max ensemble Q-value estimation minimizes overestimation bias in the model. Simulation study was used to evaluate model performance, success rate, and interaction rate. The findings show that vector and human can avoid interaction with a success rate of 96.2% when human select combined intervention actions spray repellent, insecticide treated nets, larval management, and vaccination. Other variables such as education, wealth index, community participation, and gender empowerment significantly influence the transmission of the disease in the area. The model demonstrates a better performance in describing the transmission of the disease, therefore setting the stage for future research in predictive modeling within sub-Saharan disease-prone regions. The model can be used to determine the appropriate actions that the human should adopt to reduce human-vector interaction.

VL  - 12
IS  - 2
ER  -

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Kipngetich Gideon

Pure and Applied Sciences, Kirinyaga University, Kerugoya, Kenya

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http://orcid.org/0000-0003-2874-4266
Victor Muthama Musau

Pure and Applied Sciences, Kirinyaga University, Kerugoya, Kenya

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Margaret Wambui Kinyua

Mathematics, Statistics and Actuarial Sciences, Karatina University, Karatina, Kenya

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APA Style

Gideon, K., Musau, V. M., Kinyua, M. W. (2026). GIS-Enhanced Bayesian Reinforcement Learning for Vector-Borne Infectious Disease Transmission. International Journal of Data Science and Analysis, 12(2), 25-36. https://doi.org/10.11648/j.ijdsa.20261202.12

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ACS Style

Gideon, K.; Musau, V. M.; Kinyua, M. W. GIS-Enhanced Bayesian Reinforcement Learning for Vector-Borne Infectious Disease Transmission. Int. J. Data Sci. Anal. 2026, 12(2), 25-36. doi: 10.11648/j.ijdsa.20261202.12

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AMA Style

Gideon K, Musau VM, Kinyua MW. GIS-Enhanced Bayesian Reinforcement Learning for Vector-Borne Infectious Disease Transmission. Int J Data Sci Anal. 2026;12(2):25-36. doi: 10.11648/j.ijdsa.20261202.12

Copy | Download

@article{10.11648/j.ijdsa.20261202.12,
  author = {Kipngetich Gideon and Victor Muthama Musau and Margaret Wambui Kinyua},
  title = {GIS-Enhanced Bayesian Reinforcement Learning for Vector-Borne Infectious Disease Transmission
},
  journal = {International Journal of Data Science and Analysis},
  volume = {12},
  number = {2},
  pages = {25-36},
  doi = {10.11648/j.ijdsa.20261202.12},
  url = {https://doi.org/10.11648/j.ijdsa.20261202.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20261202.12},
  abstract = {Vector-borne infectious diseases has continue to pose a significant challenge to global public health, and accurate and timely transmission prediction are crucial for effective intervention and control. The development of accurate and efficient vector-borne infectious disease predictive models has been the current trend in disease modeling. Modeling transmission, however, has assumed a discrete transmission space, which is not always ideal in the real world, and little attention has been paid to overestimation biases in predictions. The effectiveness of a predictive model is also determined by its capability to capture a significant number of data characteristics to enhance robust and accurate prediction of cases. The study proposed the application of a GIS-enhanced Bayesian Reinforcement learning model for the transmission of vector-borne infectious diseases, and model performance assessment was determined. The Bayesian quantifies the uncertainty in the parameters of models, and min-max ensemble Q-value estimation minimizes overestimation bias in the model. Simulation study was used to evaluate model performance, success rate, and interaction rate. The findings show that vector and human can avoid interaction with a success rate of 96.2% when human select combined intervention actions spray repellent, insecticide treated nets, larval management, and vaccination. Other variables such as education, wealth index, community participation, and gender empowerment significantly influence the transmission of the disease in the area. The model demonstrates a better performance in describing the transmission of the disease, therefore setting the stage for future research in predictive modeling within sub-Saharan disease-prone regions. The model can be used to determine the appropriate actions that the human should adopt to reduce human-vector interaction.
},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - GIS-Enhanced Bayesian Reinforcement Learning for Vector-Borne Infectious Disease Transmission

AU  - Kipngetich Gideon
AU  - Victor Muthama Musau
AU  - Margaret Wambui Kinyua
Y1  - 2026/06/10
PY  - 2026
N1  - https://doi.org/10.11648/j.ijdsa.20261202.12
DO  - 10.11648/j.ijdsa.20261202.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 25
EP  - 36
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20261202.12
AB  - Vector-borne infectious diseases has continue to pose a significant challenge to global public health, and accurate and timely transmission prediction are crucial for effective intervention and control. The development of accurate and efficient vector-borne infectious disease predictive models has been the current trend in disease modeling. Modeling transmission, however, has assumed a discrete transmission space, which is not always ideal in the real world, and little attention has been paid to overestimation biases in predictions. The effectiveness of a predictive model is also determined by its capability to capture a significant number of data characteristics to enhance robust and accurate prediction of cases. The study proposed the application of a GIS-enhanced Bayesian Reinforcement learning model for the transmission of vector-borne infectious diseases, and model performance assessment was determined. The Bayesian quantifies the uncertainty in the parameters of models, and min-max ensemble Q-value estimation minimizes overestimation bias in the model. Simulation study was used to evaluate model performance, success rate, and interaction rate. The findings show that vector and human can avoid interaction with a success rate of 96.2% when human select combined intervention actions spray repellent, insecticide treated nets, larval management, and vaccination. Other variables such as education, wealth index, community participation, and gender empowerment significantly influence the transmission of the disease in the area. The model demonstrates a better performance in describing the transmission of the disease, therefore setting the stage for future research in predictive modeling within sub-Saharan disease-prone regions. The model can be used to determine the appropriate actions that the human should adopt to reduce human-vector interaction.

VL  - 12
IS  - 2
ER  -

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