Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Nov 10, 2025
Date Accepted: Jul 10, 2026
Impact of Digital Contact Tracing and Other Non-Pharmaceutical Interventions on Pandemic Control: A micro-level, behaviour-driven ABM analysis
ABSTRACT
Background:
Non-pharmaceutical interventions (NPIs), including digital contact tracing (DCT), are central to controlling the spread of airborne pathogens.
Objective:
Nevertheless, the effectiveness of individual interventions and the role of personal behaviour remain insufficiently understood. Understand how NPI composition and the individual's behaviour contribute to reducing airborne pathogen transmission.
Methods:
We disentangle the efficacy of individual non-pharmaceutical interventions (NPIs), including digital contact tracing (DCT), with a novel micro-level agent-based model (ABM) that simulates individual human behaviour. Analyse the impact of individual behaviour on the effectiveness of non-pharmaceutical interventions to control airborne pathogen propagation. In particular, we want ot understand how the personal choices, measured in the dimensions of acceptance, adherence, and compliance, Our model’s Zeitgeber architecture delineates contextual characteristics, including daytime, daily routines, locations, and activities. Our method determines each agent’s current location and behaviour in a realistic environment under NPI restrictions. We model viral load transfer between agents from contact duration, distance, and the infected agent’s infectiousness level. We examine the effects of DCT-related behaviour, including adoption, adherence, and compliance, with a default intervention and with further restricting and relaxing NPIs, on key pandemic indicators.
Results:
Personal choices regarding DCT indicate that a DCT adoption (activate DCT) should be the first goal of DCT implementation campaign, followed by adherence (notify others), and compliance (follow recommendations). Among the three considered behaviour dimensions, there is no monotonic path to improving pandemic characteristics. Assuming realistic behaviour with regard to DCT, total infections reduced by 43% in the default intervention, while DCT combined with other NPIs reduced total infections up to 52%. Surprisingly, however, some restricting NPI combinations do not improve pandemic characteristics.
Conclusions:
Implementations of interventions like DCT will face challenges as pandemic characteristics do not consistently improve as the behaviour dimensions of acceptance, adherence, and compliance increase. When considering realistic behaviour, more is not always better; NPI combinations can interfere with each other to the detriment of pandemic control. Our approach offers fine-grained analysis capabilities on the effectiveness of NPI combinations that cannot be obtained in human studies due to confounding effects. Thus our approach can guide future pandemic control efforts and prioritisation for pandemic preparedness.
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