Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes

TL;DR

This paper explores how incorporating contact network features as covariates in clustered randomized studies can improve the efficiency of estimating contagion effects, demonstrated through simulations and real COVID-19 data.

Contribution

It introduces a method using augmented GEE with contact network covariates to enhance estimation efficiency in contagion studies, accounting for network structure.

Findings

Network covariate adjustment reduces variance of estimates.

Method improves power and reduces bias in contagion effect estimation.

Application to COVID-19 data shows practical utility.

Abstract

In a randomized study, leveraging covariates related to the outcome (e.g. disease status) may produce less variable estimates of the effect of exposure. For contagion processes operating on a contact network, transmission can only occur through ties that connect affected and unaffected individuals; the outcome of such a process is known to depend intimately on the structure of the network. In this paper, we investigate the use of contact network features as efficiency covariates in exposure effect estimation. Using augmented generalized estimating equations (GEE), we estimate how gains in efficiency depend on the network structure and spread of the contagious agent or behavior. We apply this approach to simulated randomized trials using a stochastic compartmental contagion model on a collection of model-based contact networks and compare the bias, power, and variance of the estimated exposure effects using an assortment of network covariate adjustment strategies. We also demonstrate the use of network-augmented GEEs on a clustered randomized trial evaluating the effects of wastewater monitoring on COVID-19 cases in residential buildings at the the University of California San Diego.