Olejarz Jason, Hoffmann Till, Zapf Alex, Mugahid Douaa, Molinaro Ross, Brown Chadwick, Boltyenkov Artem, Dudykevych Taras, Gupta Ankit, Lipsitch Marc, Atun Rifat, Onnela Jukka-Pekka, Fortune Sarah, Sampath Rangarajan, Grad Yonatan H
Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
medRxiv. 2025 Feb 5:2025.02.03.25321613. doi: 10.1101/2025.02.03.25321613.
Despite much research on early detection of anomalies from surveillance data, a systematic framework for appropriately acting on these signals is lacking. We addressed this gap by formulating a hidden Markov-style model for time-series surveillance, where the system state, the observed data, and the decision rule are all binary. We incur a delayed cost, , whenever the system is abnormal and no action is taken, or an immediate cost, , with action, where . If action costs are too high, then surveillance is detrimental, and intervention should never occur. If action costs are sufficiently low, then surveillance is detrimental, and intervention should always occur. Only when action costs are intermediate and surveillance costs are sufficiently low is surveillance beneficial. Our equations provide a framework for assessing which approach may apply under a range of scenarios and, if surveillance is warranted, facilitate methodical classification of intervention strategies. Our model thus offers a conceptual basis for designing real-world public health surveillance systems.
尽管针对从监测数据中早期发现异常情况开展了大量研究，但仍缺乏一个针对这些信号采取适当行动的系统框架。我们通过为时间序列监测制定一个隐马尔可夫式模型来解决这一差距，其中系统状态、观测数据和决策规则均为二元的。每当系统异常且未采取行动时，我们会产生一个延迟成本(d)，或者采取行动时会产生一个即时成本(a)，其中(a \lt d)。如果行动成本过高，那么监测是有害的，且绝不应该进行干预。如果行动成本足够低，那么监测是有害的，且应该始终进行干预。只有当行动成本处于中间水平且监测成本足够低时，监测才是有益的。我们的方程式提供了一个框架，用于评估在一系列情况下哪种方法可能适用，并且如果有必要进行监测，则有助于对干预策略进行系统分类。因此，我们的模型为设计现实世界的公共卫生监测系统提供了概念基础。
