Data-driven stochastic reconstruction of dengue epidemic dynamics: state-dependent stochasticity and temporal organization

Dengue epidemics exhibit strong stochastic variability, temporal memory and complex temporal organization that are difficult to capture using conventional deterministic epidemic models. However, the dependence of epidemic fluctuations on the underlying dynamical state and their influence on reconstruction across temporal scales remain insufficiently understood. In this work, we develop a data-driven stochastic framework to investigate dengue epidemic dynamics using daily infection data from Imphal West District, Manipur, India, for 2023-2025. We use Singular Spectrum Analysis (SSA) to separate deterministic and stochastic epidemic components. Logarithmic regression and autocorrelation analyses reveal empirically state-dependent noise amplitudes and persistent temporal correlations, supporting a phenomenological description involving multiplicative, temporally structures stochasticity. Polynomial and logistic-type drift functions are subsequently fitted to the deterministic components and integrated to reconstruct the epidemic incidence trajectories. Although polynomial drift provides high in-sample agreement with the deterministic dynamics, complexity-entropy analysis reveals that the reconstruction depends on the epidemic year and the embedding delay. A paired circular moving-block bootstrap further demonstrates that observed minimizing delays exhibit different levels of stability across the various reconstructions and years. Finally, we conducted a noise-amplitude sensitivity analysis, and it reveals pronounced changes in the optimal embedding delay and minimum Euclidean distance in the complexity-entropy plane. The proposed framework provides a data-driven approach to characterizing structured stochastic fluctuations and assessing the temporal organization of epidemic dynamics.