Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, SK, Canada.
PLoS Comput Biol. 2023 Feb 27;19(2):e1010917. doi: 10.1371/journal.pcbi.1010917. eCollection 2023 Feb.
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.
许多传染病的传播都依赖于人与人之间的近距离接触。对近距离接触的动态建模有助于确定疫情是否可能引发流行病。虽然商用移动设备的出现方便了近距离接触数据的收集，但电池容量和相关成本在用于接触检测的观察频率和扫描持续时间之间存在权衡。观察频率的选择应取决于特定病原体和伴随疾病的特征。我们对来自五项接触网络研究的数据进行了下采样，每项研究都以每 5 分钟的频率测量参与者之间的接触，持续时间超过四周。这些研究共包括 284 名参与者，表现出不同的社区结构。我们发现，对于使用高分辨率近距离数据的流行病学模型，用于收集近距离数据的观察方法和观察频率都会对模拟结果产生影响。这种影响取决于人口特征以及病原体的传染性。通过比较两种观察方法的性能，我们发现，在大多数情况下，每半小时蓝牙发现一分钟可以收集到允许基于代理的传播模型对发病率进行合理估计的近距离数据，但更频繁的蓝牙发现更有利于对个体感染风险进行建模，或者用于传染性较强的病原体。我们的研究结果为制定高效有效的数据收集指南提供了经验依据。
