STM Journals Peer-reviewed research from Consortium e-Learning Network

Research & Reviews: Discrete Mathematical Structures Review Article

A SEIR-Informed Stacked Fusion of Prophet, XGBoost, and LSTM for Ward-Level Epidemic Forecasting in Amravati Municipal Corporation

  1. Narendra J. Padole Department of Computer Science and Technology, HVPM/DCPE, Amravati
  2. Nitin S. Shrirao Siddhant Institute of Computer Application, Sudumbare, Pune
  3. Manish L. Jivtode Janta mahavidyalaya, chandrapur

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

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