Adaptive Sequential Surveillance with Network and Temporal Dependence
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AI 根据所提供的论文材料归类：摘要明确设定随机干预的因果目标，并处理个体网络依赖下的自适应检测分配。
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具有网络与时间依赖的自适应序贯监测
- 研究问题
- 面对潜在感染状态、个体间网络依赖与时间依赖，如何在检测资源约束下动态分配检测，并评估下一时点干预的因果目标？
- 方法与思路
- 作者提出在线超级学习器，借助一组工作模型在个体、时间或两者之间学习检测策略，并给出潜在结局与观测数据之间的识别结果。
- 材料陈述的贡献
- 摘要称方法可随疫情状态调整检测策略，并在模拟大学居住环境的疫情实验中取得较好表现，同时提供潜在结局的识别结果。
- 阅读关注点
- 应核对潜在感染状态的识别条件、资源约束下目标函数的定义、网络与时间依赖如何进入学习，以及模拟策略比较是否公平。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要未列出识别和学习保证所需条件，模拟优势不能直接推广到所有疫情环境。
原文摘要
Strategic test allocation plays a major role in the control of both emerging and existing pandemics (e.g., COVID-19, HIV). Widespread testing supports effective epidemic control by (1) reducing transmission via identifying cases, and (2) tracking outbreak dynamics to inform targeted interventions. However, infectious disease surveillance presents unique statistical challenges. For instance, the true outcome of interest - one's positive infectious status, is often a latent variable. In addition, presence of both network and temporal dependence reduces the data to a single observation. As testing entire populations regularly is neither efficient nor feasible, standard approaches to testing recommend simple rule-based testing strategies (e.g., symptom based, contact tracing), without taking into account individual risk. In this work, we study an adaptive sequential design involving n individuals over a period of τ time-steps, which allows for unspecified dependence among individuals and across time. Our causal target parameter is the mean latent outcome we would have obtained after one time-step, if, starting at time t given the observed past, we had carried out a stochastic intervention that maximizes the outcome under a resource constraint. We propose an Online Super Learner for adaptive sequential surveillance that learns the optimal choice of tests strategies over time while adapting to the current state of the outbreak. Relying on a series of working models, the proposed method learns across samples, through time, or both: based on the underlying (unknown) structure in the data. We present an identification result for the latent outcome in terms of the observed data, and demonstrate the superior performance of the proposed strategy in a simulation modeling a residential university environment during the COVID-19 pandemic.