Revealing mechanisms of infectious disease spread through empirical contact networks
Fig 1
Observed data(left panel): INoDS utilizes an observed infection time-series data to estimate statistical evidence towards a static or dynamic contact network hypothesis (or hypotheses) using a three-step procedure. Shown here is an example of two competing network hypotheses based on behaviors A and B that potentially cause infection transfer. Inferential steps (right panel): In the first step, the tool estimates per-contact transmission rate parameter β, and background transmission rate parameter ϵ which captures the components of infection propagation unexplained by the edge connections of the network hypothesis. Here, the total infected connections of the focal node i (ki) is 2. Second, to estimate the epidemiological relevance of the network hypothesis, Bayesian hypothesis testing is performed. The prior distribution shows that the null hypothesis (M = 1) assumes a uniform distribution over randomized networks generated by permuting 10%—100% of edge connections in the contact network (HA), whereas the alternate hypothesis (M = 2) is a spike-shaped distribution such that only the contact network (HA, 0% permutation) has non-zero probability. The distribution on model index shifts to M = 2 if the alternate hypothesis has higher posterior probability than the null. Third, model selection of competing network hypotheses is performed using Bayes Factor (BF). A Bayes factor above 2.44 is considered to be decisive support for one hypothesis over the other.