# A graph-theoretic framework for integrating mobility data into mathematical epidemic models

## TL;DR

This paper introduces a new method to improve epidemic models by integrating mobility data, allowing better tracking of disease spread based on real-world movement patterns.

## Contribution

The paper proposes a novel framework that links epidemic transmission rates to mobility data, enhancing model accuracy for multi-wave outbreaks.

## Key findings

- The model successfully adapts transmission rates to population mobility patterns.
- Using Google Community Mobility Reports improves the fit of epidemic models to real-world data.
- The approach is demonstrated effectively on the first four waves of the COVID-19 pandemic.

## Abstract

Advances in modeling the spread of infectious diseases have allowed modellers to relax the homogeneous mixing assumption of traditional compartmental models. The recently introduced synthetic network model, which is an SIRS type model based on a non-linear transmission rate, effectively decouples the underlying population network structure from the epidemiological parameters of disease, and has been shown to produce superior fits to multi-wave epidemics. However, inference from case counts alone is generally problematic due to the partial unidentifiability between probability of person to person transmission and the average number of contacts per individual. An alternate source of data that can inform the network alone has the potential to improve overall modeling results. Aggregate cell phone mobility data, which record daily numbers of visits to points of interest, provide a proxy for the number of contacts that people establish during their visits. In this paper, we link the contact rate from an epidemic model to the total number of contacts formed in the population. Inferring the latter from Google Community Mobility Reports data, we develop an integrated epidemic model whose transmission adapts to population mobility. This model is illustrated on the first four waves of the COVID-19 pandemic.