Time series analysis via mechanistic models

TL;DR

This paper introduces a flexible framework for time series analysis using mechanistic models, enabling inference on complex stochastic dynamical systems with applications in epidemiology.

Contribution

It develops a novel class of implicit Markov models with plug-and-play inference, advancing the analysis of biological and epidemiological time series data.

Findings

Applied to measles transmission dynamics.

Analyzed cholera incidence with interacting strains.

Demonstrated effectiveness of the framework in biological systems.

Abstract

The purpose of time series analysis via mechanistic models is to reconcile the known or hypothesized structure of a dynamical system with observations collected over time. We develop a framework for constructing nonlinear mechanistic models and carrying out inference. Our framework permits the consideration of implicit dynamic models, meaning statistical models for stochastic dynamical systems which are specified by a simulation algorithm to generate sample paths. Inference procedures that operate on implicit models are said to have the plug-and-play property. Our work builds on recently developed plug-and-play inference methodology for partially observed Markov models. We introduce a class of implicitly specified Markov chains with stochastic transition rates, and we demonstrate its applicability to open problems in statistical inference for biological systems. As one example, these models are shown to give a fresh perspective on measles transmission dynamics. As a second example, we present a mechanistic analysis of cholera incidence data, involving interaction between two competing strains of the pathogen Vibrio cholerae.