Transmission of many communicable diseases depends on proximity contacts among humans. Modeling the dynamics of proximity contacts can help determine whether an outbreak is likely to trigger an epidemic. While the advent of commodity mobile devices has eased the collection of proximity contact data, battery capacity and associated costs impose tradeoffs between the observation frequency and scanning duration used for contact detection. The choice of observation frequency should depend on the characteristics of a particular pathogen and accompanying disease. We downsampled data from five contact network studies, each measuring participant-participant contact every 5 minutes for durations of four or more weeks. These studies included a total of 284 participants and exhibited different community structures. We found that for epidemiological models employing high-resolution proximity data, both the observation method and observation frequency configured to collect proximity data impact the simulation results. This impact is subject to the population's characteristics as well as pathogen infectiousness. By comparing the performance of two observation methods, we found that in most cases, half-hourly Bluetooth discovery for one minute can collect proximity data that allows agent-based transmission models to produce a reasonable estimation of the attack rate, but more frequent Bluetooth discovery is preferred to model individual infection risks or for highly transmissible pathogens. Our findings inform the empirical basis for guidelines to inform data collection that is both efficient and effective. Author summaryClose-proximity human contacts play a fundamental role in the spread of many diseases. While the advent of commodity mobile devices have eased the collection of contact time series, battery capacity and associated costs impose tradeoffs between the frequency and scanning duration used for contact detection and participant experience and adherence. To understand the impact of the frequency with which human contact networks are observed on the accuracy of network reconstruction and simulated attack rate, we downsampled data from five high-velocity contact network studies, each measuring participant contacts every 5 minutes over at least four weeks. Results from infection transmission models parameterized by contact networks reconstructed from successively downsampled contact information revealed that the model-predicted attack rate and the per-realization variability in predicted attack rate varies markedly by pathogen and network structure. For some pathogens across multiple studies, downsampling contact rates imparts pronounced inaccuracies in model-predicted attack rate, compared to what is predicted with highest-velocity contact data. Our findings can inform design of data collection studies that are both efficient and effective, and may aid understanding of contact networks beyond the current collection limit.
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