We aim to study the causes and transmission modes of infectious diseases among members of a community in the presence of hidden, asymptomatic spreaders of the pathogen.

When modeling the spread of an infection among members or nodes of a community, each node's probability of getting infected depends on its innate susceptibility and its exposure to the contagion through its neighbors.In many cases, a neighbor's influence is hidden. Such is the case with asymptomatic carriers of the disease. We develop generative models to identify the hidden influencers in both static and dynamic networks. We use the neighbors' hidden influence state to compute an accurate estimate of exposure to the contagion. We propose efficient variational inference algorithms to learn our models' parameters. We also study the causal mechanisms that lead to an elevated risk of infection.

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