# Amplification of the power of network hubs and degree skewness over infectious disease spread during lulls

## TL;DR

The paper shows how network hubs and degree skewness influence disease spread, especially during lulls in transmission like with COVID-19.

## Contribution

The study reveals that network hubs and degree skewness amplify disease spread during lulls but are less impactful during high transmissibility.

## Key findings

- Network hubs and high degree skewness increase peak prevalence during low to moderate transmissibility.
- The effect of hubs is amplified during lulls in disease spread.
- Highly transmissible diseases show suppressed hub effects.

## Abstract

Just as individuals’ personal social network connections shape their susceptibility to disease, the structure of larger networks in communities shapes the extent of disease spread. We examine how heterogeneity in network structure at the population level affects the spread of disease – namely, COVID-19 – considering varying levels of disease transmissibility and in-person contact rates. Using dynamic simulations that take into account network structure, social contact rates given contextual features of the community (informed by real-life data on family local family structure, schools, workplaces, and daily shopping activities), and disease infection rates, we first confirm that the presence of network hubs and high network degree skewness results in a higher level of infected peak prevalence with infectious diseases such as COVID-19 during periods of low to moderate transmissibility. However, this effect is amplified during lulls in disease spread and is suppressed during periods of greater transmissibility, rendering social network structure more significant during lulls. Moreover, in the case of already highly transmissible diseases, the role of hubs and severe degree skewness is already more continually suppressed.