We disentangle the efficacy of individual non-pharmaceutical interventions (NPIs), including digital contact tracing (DCT), with a novel behaviour-driven agent-based model (ABM) to inform ongoing pandemic preparedness efforts. 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 the restrictions of NPIs. We model viral load transfer between agents from contact duration, distance, and the infected agent's infectiousness level. We examine the effects of DCT adoption, adherence, and compliance, both individually and combined with other NPIs, on key pandemic indicators, thus providing novel insight into infection dynamics. DCT combined with other NPIs reduces the total infections up to 52% for realistic behaviour, whereas DCT alone yielded a 43% reduction. Surprisingly however, some NPI combinations do not improve pandemic parameters. 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 inform future pandemic control efforts and prioritisation in pandemic preparedness.

The authors have declared no competing interest.

The authors received no specific funding for this research.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

[1] D. V. Tomori et al., "Individual social contact data and population mobility data as early markers of SARS-CoV-2 transmission dynamics during the first wave in Germany-an analysis based on the COVIMOD study," BMC Medicine, vol. 19, no. 1, Art. no. 1, Oct. 2021, doi: 10.1186/s12916-021-02139-6. [2] Y. Vaizman, K. Ellis, and G. Lanckriet, "Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches," IEEE Pervasive Computing, vol. 16, no. 4, pp. 62-74, Oct. 2017, doi: 10.1109/MPRV.2017.3971131.

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