Narendra J. Padole,
Nitin S. Shrirao,
Manish L. Jivtode,
- Assistant Professor, Department of Computer Science and Technology, HVPM/DCPE, Amravati, Maharashtra, India
- Director, Siddhant Institute of Computer Application, Sudumbare, Pune, Maharashtra, India
- Professor, Janta mahavidyalaya, chandrapur, Maharashtra, India
Abstract
Municipal epidemic preparedness depends on accurate short-horizon forecasts at fine spatial granularity. Ward-level incidence series are typically nonstationary due to changing contact patterns, interventions, reporting delays, and heterogeneous demographic and environmental factors. This paper presents a mathematically formulated hybrid forecasting architecture designed for Amravati Municipal Corporation (AMC). The method decomposes observed incidence into (i) a mechanistic SEIR baseline that enforces epidemiological structure and (ii) a data-driven residual learned using Prophet (trend-seasonality decomposition), XGBoost (nonlinear covariate interactions), and LSTM (temporal memory). A constrained fusion estimator combines residual predictors on the probability simplex, improving stability and interpretability. We provide explicit model equations, parameter meanings, and optimization objectives, and we include experimental metrics extracted from the uploaded thesis evaluation for mathematics and discrete structures.
Keywords: Epidemic forecasting; SEIR; Hybrid modeling; Prophet; XGBoost; LSTM; Stacked fusion; Ward-level surveillance; Amravati
[This article belongs to Research & Reviews: Discrete Mathematical Structures ]
Narendra J. Padole, Nitin S. Shrirao, Manish L. Jivtode. A SEIR-Informed Stacked Fusion of Prophet, XGBoost, and LSTM for Ward-Level Epidemic Forecasting in Amravati Municipal Corporation. Research & Reviews: Discrete Mathematical Structures. 2026; 13(01):17-23.
Narendra J. Padole, Nitin S. Shrirao, Manish L. Jivtode. A SEIR-Informed Stacked Fusion of Prophet, XGBoost, and LSTM for Ward-Level Epidemic Forecasting in Amravati Municipal Corporation. Research & Reviews: Discrete Mathematical Structures. 2026; 13(01):17-23. Available from: https://journals.stmjournals.com/rrdms/article=2026/view=238924
References
- O. Kermack and A. G. McKendrick, “A contribution to the mathematical theory of epidemics,” Proc. Royal Soc. A, 1927.
- W. Hethcote, “The mathematics of infectious diseases,” SIAM Review, 2000.
- Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, 1997.
- Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” Proc. KDD, 2016.
- J. Taylor and B. Letham, “Forecasting at scale,” The American Statistician, 2018.
- M. Anderson and R. M. May, Infectious Diseases of Humans: Dynamics and Control. Oxford, U.K.: Oxford Univ. Press, 1991.
- Diekmann, J. A. P. Heesterbeek, and M. G. Roberts, “The construction of next-generation matrices for compartmental epidemic models,” J. R. Soc. Interface, 2010.
- J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 2nd ed. Melbourne, Australia: OTexts, 2018.
- H. Friedman, “Greedy function approximation: A gradient boosting machine,” Ann. Stat., 2001.
- H. Wolpert, “Stacked generalization,” Neural Networks, 1992.
- Breiman, “Stacked regressions,” Machine Learning, 1996.
- Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.
- Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, 2006.
- Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” Commun. ACM, 2008.
- Höhle, “surveillance: An R package for the monitoring of infectious diseases,” Comput. Stat., 2007.
Research & Reviews: Discrete Mathematical Structures
| Volume | 13 |
| Issue | 01 |
| Received | 07/02/2026 |
| Accepted | 26/02/2026 |
| Published | 10/03/2026 |
| Publication Time | 31 Days |
Login
PlumX Metrics