Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies

TL;DR

This paper introduces a contact graph epidemic model for COVID-19 that incorporates actual contact network structures, providing more accurate predictions of disease spread and intervention effectiveness compared to traditional models.

Contribution

The work presents a structure-aware epidemic model (CGEM) that generalizes classical models by integrating real contact networks, enabling precise analysis of intervention strategies.

Findings

Traditional models overestimate infection spread by a factor of 3.

Realistic contact networks reveal differences in epidemic curves and intervention effectiveness.

Modeling contact structures improves accuracy in predicting peaks and second waves.

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has quickly become a global public health crisis unseen in recent years. It is known that the structure of the human contact network plays an important role in the spread of transmissible diseases. In this work, we study a structure aware model of COVID-19 CGEM. This model becomes similar to the classical compartment-based models in epidemiology if we assume the contact network is a Erdos-Renyi (ER) graph, i.e. everyone comes into contact with everyone else with the same probability. In contrast, CGEM is more expressive and allows for plugging in the actual contact networks, or more realistic proxies for it. Moreover, CGEM enables more precise modelling of enforcing and releasing different non-pharmaceutical intervention (NPI) strategies. Through a set of extensive experiments, we demonstrate significant differences between the epidemic curves when assuming different underlying structures. More specifically we demonstrate that the compartment-based models are overestimating the spread of the infection by a factor of 3, and under some realistic assumptions on the compliance factor, underestimating the effectiveness of some of NPIs, mischaracterizing others (e.g. predicting a later peak), and underestimating the scale of the second peak after reopening.