Reconstructing missing information in epidemic spreading on contact networks can be essential in prevention and containment strategies. For instance, identifying and warning infective but asymptomatic individuals (e.g., manual contact tracing) helped contain outbreaks in the COVID-19 pandemic. The number of possible epidemic cascades typically grows exponentially with the number of individuals involved. The challenge posed by inference problems in the epidemics processes originates from the difficulty of identifying the almost negligible subset of those compatible with the evidence (for instance, medical tests). Here we present a new generative neural networks framework that can sample the most probable infection cascades compatible with observations. Moreover, the framework can infer the parameters governing the spreading of infections. The proposed method obtains better or comparable results with existing methods on the patient zero problem, risk assessment, and inference of infectious parameters in synthetic and real case scenarios like spreading infections in workplaces and hospitals.
翻译:在接触网络传播的流行病中重新构建缺失的信息,对于预防和遏制战略可能至关重要,例如,识别和警告感染性但无症状的个人(如人工接触追踪)有助于遏制COVID-19流行病的爆发;可能的流行病级联数量随着参与人数的增加,通常会成倍增长;流行病过程中的推论问题所构成的挑战源于难以确定与证据相匹配的几乎微不足道的子群(例如医学测试);这里我们提出了一个新的基因神经网络框架,可以对与观察相容的最可能感染的级联进行抽样;此外,该框架可以推断传染病传播的参数;拟议的方法与现有关于病人零问题、风险评估和传染参数在合成和真实情况下的推断,例如工作场所和医院的传染病传播,取得更好或可比的结果。