Disease parameter table.

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.

多种传染病的传播依赖于人群间的近距离接触（proximity contacts）。对近距离接触动态进行建模，有助于判断某次暴发是否会引发流行疫情。随着商用移动设备的普及，近距离接触数据的采集难度大幅降低，但电池容量与相关成本，会在接触检测的观测频率与扫描时长之间形成权衡。观测频率的合理选择，应当取决于特定病原体及其伴随疾病的特征。我们对五项接触网络研究的原始数据进行了降采样处理：每项研究均以每5分钟一次的频率记录参与者间的接触情况，且观测时长均达四周及以上。这些研究总计纳入284名参与者，且各自呈现出不同的社区结构。我们发现，对于采用高分辨率近距离接触数据的流行病学模型而言，用于采集近距离数据的观测方式与观测频率，均会对模拟结果产生影响。该影响会受到人群特征以及病原体传染性的制约。通过对比两种观测方式的表现，我们发现：在多数场景下，每半小时开展一次、单次持续一分钟的蓝牙探测，所采集的近距离接触数据可支持基于智能体的传播模型（agent-based transmission models），对罹患率做出合理估算；但当需要对个体感染风险进行建模，或是针对高传染性病原体开展研究时，更频繁的蓝牙探测则更为可取。本研究结果为制定兼具效率与实效性的数据采集指南提供了实证依据。