Inference on spatiotemporal dynamics for networks of biological populations

TL;DR

This paper introduces a novel likelihood-based inference algorithm for high-dimensional stochastic models in metapopulation systems, applied to COVID-19 data to improve model accuracy and inform policy.

Contribution

It develops an effective inference method for complex stochastic models and demonstrates its application to real-world epidemiological data, enhancing model fit and interpretability.

Findings

The lockdown in China was more effective than previously estimated.

The new model showed substantially improved statistical fit.

Parameter identifiability was significantly enhanced.

Abstract

Mathematical models in ecology and epidemiology must be consistent with observed data in order to generate reliable knowledge and evidence-based policy. Metapopulation systems, which consist of a network of connected sub-populations, pose technical challenges in statistical inference due to nonlinear, stochastic interactions. Numerical difficulties encountered in conducting inference can obstruct the core scientific questions concerning the link between the mathematical models and the data. Recently, an algorithm has been developed which enables effective likelihood-based inference for the high-dimensional partially observed stochastic dynamic models arising in metapopulation systems. The COVID-19 pandemic provides a situation where mathematical models and their policy implications were widely visible, and we use the new inferential technology to revisit an influential metapopulation model used to inform basic epidemiological understanding early in the pandemic. Our methods support self-critical data analysis, enabling us to identify and address model limitations, and leading to a new model with substantially improved statistical fit and parameter identifiability. Our results suggest that the lockdown initiated on January 23, 2020 in China was more effective than previously thought. We proceed to recommend statistical analysis standards for future metapopulation system modeling.