Smarter Decisions for Better Health Outcomes:  An Ai-Enabled Financial Decision Intelligence  Framework for Dengue Control In Andhra  Pradesh

Madhavi  Sripathi; Kanaka Durga  Hanumanthu; Varaprasad  Goud; Anil Kumar  Akkala; Shaik  Aminabee; T Chandrasekhar  Yadav

Madhavi Sripathi Assistant Professor, K L Business School, K L deemed to be University, KLEF, Vaddeswaram, Guntur
Kanaka Durga Hanumanthu Professor, K L Business School, K L deemed to be University, Vaddeswaram, Guntur
Varaprasad Goud Assistant Professor, School of Management Studies, Chaitanya Bharathi Institute of Technology, Telangana
Anil Kumar Akkala Assistant Professor, School of Business, Aditya University, Surampalem
Shaik Aminabee Professor, Department of Pharmacology, V V Institute of Pharmaceutical Sciences, Gudlavalleru
T Chandrasekhar Yadav Associate Professor, K L Business School, K L deemed to be University, KLEF, Vaddeswaram

Keywords: Dengue control; Financial decision intelligence; Temporal graph neural networks; Bayesian hierarchical modeling; Public health financing.

Abstract

The problem of dengue fever in Andhra Pradesh, India, is an ongoing public health and financial problem that has very few effective control measures due to the current control strategies, based on reactive surveillance and past budgeting. This paper suggests an engineer assisted financial decision intelligence model based on AI to assist in proactive and economic management of dengue. The framework combines Temporal Graph Neural Networks (TGNNs) and Bayesian hierarchical spatio-temporal models to model the spread of disease in
districts in the context of uncertainty in predictions. The model allows allocating resources more efficiently by connecting epidemiological predictions with financial planning. The outcomes show that the framework identifies outbreak risks 4-6 weeks prior to the conventional practices and minimises misallocation of resources by 18-25 per cent. It, as well, enhances the cost-efficiency of the interventions, including fogging and larval control, and the significance of regionally coordinated actions. In general, the paper shows that AI-based decision support can transform dengue control and preventive measures, from a reactive response in the context of resource constraints to proactive and data driven planning.
Keywords: Dengue control; Financial decision intelligence; temporal graph neural networks; Bayesian hierarchical modelling; public health financing.

DOI: https://doi.org/10.24321/0019.5138.202617

How to cite this article:
Sripathi M, Hanumathu K D, Goud V, Akkala A K, Aminabee S, Yadav T C, Smarter Decisions for Better Health Outcomes: An Ai-Enabled Financial Decision Intelligence Framework for Dengue Control In Andhra Pradesh. J Commun Dis. 2026;58(1):126-131.

References

Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, Drake JM, Brownstein JS, Hoen AG, Sankoh

O, Myers MF. The global distribution and burden of dengue. Nature. 2013 Apr 25;496(7446):504-7

Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian data analysis. Chapman and Hall/CRC; 1995 Jun 1

Jafar SH, Hemachandran K, Nawaah D, Rodriguez RV, editors. Foundations of Artificial Intelligence in Finance:

Insights for Practitioners with Applications and Case Studies. CRC Press; 2026 Feb 4

Hanumanthu, K. D., & Kavitha, H. (2024). Machine learning and explainable AI for uncertainty-aware

financial decision-making. International Journal of Financial Engineering and Analytics, 9(3), 201–218.

Hanumanthu, K. D., Rao, S., & Prasad, M. (2024). Digital intelligence frameworks for adaptive risk analysis and

policy decision support. Journal of Intelligent Systems and Policy Modeling, 7(1), 33–49.