The missing links: Evaluating contact tracing with incomplete data in large metropolitan areas during an epidemic

TL;DR

This study uses agent-based modeling to evaluate how different types of incomplete contact tracing data impact epidemic control in large cities, revealing that missing infected individuals has a more severe effect than missing contact links.

Contribution

It introduces a detailed simulation framework analyzing the effects of infector-omission and contact-omission scenarios on epidemic dynamics in metropolitan areas.

Findings

Infector-omission causes more abrupt epidemic changes.

Lower-population cities are more resilient to information loss.

Transmission network diameter increases with data omission.

Abstract

Contact tracing (CT) plays a pivotal role in controlling early epidemic spread, particularly when a novel infectious disease emerges. However, the quantitative impact of missing information -- such as untraced cases or unnotified contacts -- on the effectiveness of CT remains insufficiently understood. Using a stochastic agent-based model with sociodemographics from metropolitan areas in South Korea, we simulate how different forms of information loss affect epidemic spreading dynamics. We construct information-loss scenarios based on two types: infector-omission (IO) and contact-omission (CO), including selective (SCO) and uniform (UCO) scenarios; IO corresponds to the omission of infected individuals (nodes) from the tracing process, leading to the loss of all movement trajectories and downstream transmission links originating from them, whereas CO corresponds to the omission of specific contact events (edges), in which infected individuals are identified but some of their transmission links fail to be detected or notified. The sensitivity of epidemic dynamics to increasing omission rates differs markedly between the two types: IO scenarios exhibit substantially stronger and more abrupt changes in transmission structure and epidemic outcomes, whereas CO scenarios produce more gradual effects. In both scenarios, the magnitude of these effects varies across cities, with a lower-population city (Busan) showing greater tolerance to information loss than the largest city (Seoul), underscoring the importance of regional tailoring in CT strategies. Both IO and CO scenarios also lead to an increase in the transmission network diameter as information loss grows, indicating that a small network diameter reflects effective contact tracing that limits the depth of transmission chains.