Simulating the impact of perception bias on social contact surveys for infectious disease modelling

TL;DR

This study uses simulations of contact surveys with a synthetic network to show that perception biases can significantly distort contact data and lead to underestimating disease impact among minority groups in infectious disease models.

Contribution

It demonstrates how perception biases in contact surveys affect the accuracy of contact patterns and disease burden estimates in models.

Findings

Perception biases reduce accuracy of contact pattern data.

Biased data underestimates disease incidence in minority groups.

Survey biases can systematically affect infectious disease projections.

Abstract

Social contact patterns are a key input to many infectious disease models. Contact surveys, where participants are asked to provide information on their recent close and casual contacts with others, are one of the standard methods to measure contact patterns in a population. Surveys that require detailed sociodemographic descriptions of contacts allow for the specification of fine-grained contact rates between subpopulations in models. However, perception biases affecting a surveyed person's ability to estimate sociodemographic attributes (e.g., age, race, socioeconomic status) of others could affect contact rates derived from survey data. Here, we simulate contact surveys using a synthetic contact network of New Mexico to investigate the impact of these biases on survey accuracy and infectious disease model projections. We found that perception biases affecting the estimation of another individual's age and race substantially decreased the accuracy of the derived contact patterns. Using these biased patterns in a Susceptible-Infectious-Recovered compartmental model lead to an underestimation of cumulative incidence among older people (65+ years) and individuals identifying as races other than White. Our study shows that perception biases can impact contact patterns estimated from surveys in ways that systematically underestimate disease burden in minority populations when used in transmission models.