Exploring Memory Effects: Sparse Identification in Vector-Borne Diseases

TL;DR

This paper introduces a data-driven framework extending SINDy to identify nonlinear transmission dynamics with memory effects in vector-borne diseases, improving prediction accuracy from limited data and aiding public health decisions.

Contribution

It develops a novel extension of SINDy for systems with distributed memory, enabling discovery of transmission mechanisms directly from time series data without predefined assumptions.

Findings

Successfully applied to SFTS case data using only incidence and temperature.

Enhanced predictive performance when coupled with mechanistic models.

Identified key behavioral and memory parameters influencing disease spread.

Abstract

Predicting the human burden of vector-borne diseases from limited surveillance data remains a major challenge, particularly in the presence of nonlinear transmission dynamics and delayed effects arising from vector ecology and human behavior. We develop a data-driven framework based on an extension of Sparse Identification of Nonlinear Dynamics (SINDy) to systems with distributed memory, enabling discovery of transmission mechanisms directly from time series data. Using severe fever with thrombocytopenia syndrome (SFTS) as a case study, we show that this approach can uncover key features of tick-borne disease dynamics using only human incidence and local temperature data, without imposing predefined assumptions on human case reporting. We further demonstrate that predictive performance is substantially enhanced when the data-driven model is coupled with mechanistic representations of tick-host transmission pathways informed by empirical studies. The framework supports systematic sensitivity analysis of memory kernels and behavioral parameters, identifying those most influential for prediction accuracy. Although the approach prioritizes predictive accuracy over mechanistic transparency, it yields sparse, interpretable integral representations suitable for epidemiological forecasting. This hybrid methodology provides a scalable strategy for forecasting vector-borne disease risk and informing public health decision-making under data limitations.