Epidemic preparedness depends on our ability to predict the trajectory of an epidemic and the human behavior that drives spread in the event of an outbreak. Changes to behavior during an outbreak limit the reliability of syndromic surveillance using large-scale data sources, such as online social media or search behavior, which could otherwise supplement healthcare-based outbreak-prediction methods. Here, we measure behavior change reflected in mobile-phone call-detail records (CDRs), a source of passively collected real-time behavioral information, using an anonymously linked dataset of cell-phone users and their date of influenza-like illness diagnosis during the 2009 H1N1v pandemic. We demonstrate that mobile-phone use during illness differs measurably from routine behavior: Diagnosed individuals exhibit less movement than normal (1.1 to 1.4 fewer unique tower locations; P<3.2×10−3), on average, in the 2 to 4 d around diagnosis and place fewer calls (2.3 to 3.3 fewer calls; P<5.6×10−4) while spending longer on the phone (41- to 66-s average increase; P<4.6×10−10) than usual on the day following diagnosis. The results suggest that anonymously linked CDRs and health data may be sufficiently granular to augment epidemic surveillance efforts and that infectious disease-modeling efforts lacking explicit behavior-change mechanisms need to be revisited.

流行病防范取决于我们预测流行病轨迹的能力以及在爆发时推动传播的人类行为。爆发期间行为的变化限制了使用大规模数据源（例如在线社交媒体或搜索行为）进行症状监测的可靠性，否则这些数据源可以补充基于医疗保健的爆发预测方法。在这里，我们使用手机用户的匿名链接数据集及其在期间诊断出流感样疾病的日期来测量手机通话详细记录 (CDR) 中反映的行为变化，CDR 是被动收集的实时行为信息的来源。 2009 年 H1N1v 大流行。我们证明了在疾病期间使用手机与常规行为有显着差异：被诊断的个体表现出比正常人更少的运动（1.1 比 1.磷<3.2×10-3)，平均而言，在诊断前后的 2 到 4 天，拨打更少的电话（减少 2.3 到 3.3 次电话；磷<5.6×10-4）同时在手机上花费更长的时间（平均增加 41 到 66 秒；磷<4.6×10-10) 在诊断后的第二天比往常。结果表明，匿名关联的 CDR 和健康数据可能足够细化以增强流行病监测工作，并且需要重新审视缺乏明确的行为改变机制的传染病建模工作。