On the Effect of Missing Transmission Chain Information in Agent-Based Models: Outcomes of Superspreading Events and Workplace Transmission

TL;DR

This study evaluates how missing detailed transmission data in agent-based epidemic models affects predictions, especially in superspreading and workplace scenarios, highlighting significant differences in infection outcomes and transmission dynamics.

Contribution

The paper introduces a framework to assess the impact of simplified assumptions in ABMs on epidemic predictions, emphasizing the importance of detailed transmission data.

Findings

17.2% more infections in superspreading event with simplified data

46.0% increase in infections in certain settings due to simplifications

Differences in spatial dynamics and transmission trees are significant

Abstract

Agent-based models (ABMs) have emerged as distinguished tools for epidemic modeling due to their ability to capture detailed human contact patterns and can, thus, support decision-makers in times of outbreaks and epidemics. However, as a result of missing correspondingly resolved data transmission events are often modeled based on simplified assumptions. In this article, we present a framework to assess the impact of these simplifications on epidemic prediction outcomes, considering superspreading and workplace transmission events. We couple the VADERE microsimulation model with the large-scale MEmilio-ABM and compare the outcomes of four outbreak events after 10 days of simulation in a synthetic city district generated from German census data. In a restaurant superspreading event, where up to four households share tables, we observe 17.2 % more infections on day 10 after the outbreak. The difference increases to 46.0 % more infections when using the simplified initialization in a setting where only two households share tables. We observe similar outcomes (41.3 % vs. 9.3 % more infections) for two workplace settings with different mixing patterns between teams at work. In addition to the aggregated difference, we show differences in spatial dynamics and transmission trees obtained with complete or reduced outbreak information. We observe differences between simplified and fully detailed initializations that become more pronounced when the subnetworks in the outbreak setting are mixing less. In consequence and aside from classical calibration of models, the significant outcome differences should drive us to develop a more profound understanding of how and where simplified assumptions about transmission events are adequate.