Epidemiology - Spreading III

With the hit of new pandemic threats, scientific frameworks are needed to understand the unfolding of the epidemic. The use of mobile apps that are able to trace contacts is of utmost importance in order to control new infected cases and contain further propagation.

Here we present a theoretical approach [1] that uses both percolation and message--passing techniques to quantify the role of automated contact tracing in mitigating an epidemic wave.

Infectious diseases, much like the current one of COVID-19, have had a huge economic and societal impact. The ability to model the transmission characteristics of an infection is critical to minimize its impact. In fact, predicting how fast an infection is spreading could be a major factor in deciding lockdown decisions, as well as the severity and strictness of the applied mitigation measures. Even though modeling epidemics is a well studied subject, most simple models do not include quarantine measures, such as those imposed in the recent pandemic.

We devise a systematic method for risk estimation of coinfective spreading dynamics, based on k-means clustering. By classifying the outbreaks into high and low risk incidents, we define the mean outbreak size and the outbreak probability.

We study the relations between importance measures of nodes in temporal network epidemiology and reachability (the expected number of nodes that could be reached by time-respecting paths at a random time). We find that this intuitive predictor of epidemic importance occasionally fail, and does so in peculiar patterns. We discuss the network-structural reasons for this.