Temporal Heterogeneities Increase the Prevalence of Epidemics on Evolving Networks

TL;DR

This paper demonstrates that temporal heterogeneities in contact patterns significantly influence epidemic dynamics, leading to earlier, larger, and faster outbreaks, and highlights the importance of accounting for these heterogeneities in epidemic modeling.

Contribution

It introduces a stochastic model for temporal networks with heterogeneous contact patterns and analyzes their impact on epidemic spread, emphasizing the need to consider temporal heterogeneities in R0 estimation.

Findings

Heterogeneous contact patterns cause earlier and larger epidemics in SIR models.

Epidemics spread faster initially in SI and SIS models with heterogeneous contact patterns.

Heterogeneous contact patterns lead to higher prevalence compared to homogeneous scenarios with same average inter-event times.

Abstract

Empirical studies suggest that contact patterns follow heterogeneous inter-event times, meaning that intervals of high activity are followed by periods of inactivity. Combined with birth and death of individuals, these temporal constraints affect the spread of infections in a non-trivial way and are dependent on the particular contact dynamics. We propose a stochastic model to generate temporal networks where vertices make instantaneous contacts following heterogeneous inter-event times, and leave and enter the system at fixed rates. We study how these temporal properties affect the prevalence of an infection and estimate R0, the number of secondary infections, by modeling simulated infections (SIR, SI and SIS) co-evolving with the network structure. We find that heterogeneous contact patterns cause earlier and larger epidemics on the SIR model in comparison to homogeneous scenarios. In case of SI and SIS, the epidemics is faster in the early stages (up to 90% of prevalence) followed by a slowdown in the asymptotic limit in case of heterogeneous patterns. In the presence of birth and death, heterogeneous patterns always cause higher prevalence in comparison to homogeneous scenarios with same average inter-event times. Our results suggest that R0 may be underestimated if temporal heterogeneities are not taken into account in the modeling of epidemics.