Modeling, Inference, and Prediction in Mobility-Based Compartmental Models for Epidemiology

TL;DR

This paper introduces a mobility-based compartmental model for epidemiology that accounts for individual heterogeneity, improving prediction accuracy of pandemic size and enabling mobility inference from infection data.

Contribution

It presents a novel model incorporating mobility distributions into disease dynamics and develops machine learning methods to infer mobility from data.

Findings

Mobility-based model predicts smaller pandemic sizes than classical models.

The model effectively addresses overestimation issues in epidemic predictions.

A machine learning approach successfully infers mobility distributions from real-world data.

Abstract

Classical compartmental models in epidemiology often assume a homogeneous population for simplicity, which neglects the inherent heterogeneity among individuals. This assumption frequently leads to inaccurate predictions when applied to real-world data. For example, evidence has shown that classical models overestimate the final pandemic size in the H1N1-2009 and COVID-19 outbreaks. To address this issue, we introduce individual mobility as a key factor in disease transmission and control. We characterize disease dynamics using mobility distribution functions for each compartment and propose a mobility-based compartmental model that incorporates population heterogeneity. Our results demonstrate that, for the same basic reproduction number, our mobility-based model predicts a smaller final pandemic size compared to the classical models, effectively addressing the common overestimation problem. Additionally, we infer mobility distributions from the time series of the infected population. We provide sufficient conditions for uniquely identifying the mobility distribution from a dataset and propose a machine-learning-based approach to learn mobility from both synthesized and real-world data.