By employing the spatial position of olive trees in the Salento subregion in Italy, we defined a network structure on top of them and hence applied and tested our models to this real life, spatial scenario.

For the case study, a brief parenthesis is needed. In the last decade, Puglia region suffered from a very strong and devastating epidemic of the bacterium Xylella fastidiosa, which infected and killed several MILLIONS of olive trees.

In the containment scenario, where epidemic and vaccination evolve simultaneously, we found that both strategies perform slightly similarly in reducing the impact of the diseases, but block vaccination exploits fewer resources.

In the preventive scenario, where the vaccination occurs BEFORE the epidemic takes place, Ring outperforms Block vaccination, reducing the size of the epidemic and, in some case, even preventing it from happening.

We derived a set of equations ruling each of the process and tested them on a set of synthetic networks, proving an excellent correspondence between theoretical and simulated results in all the scenarios considered.

In this work we defined a new set of models for vaccination strategies on networks. To put it simple: given a node, its neighbors can be vaccinated up to (Block) or exactly at (Ring) R contacts away. Vaccination can then occur before or during an epidemic spread.

📣🚨 New work out on @PLOS Computational Biology! Excited and proud to have provided the world with more maps of Puglia🕷️ Thanks for the journey, I was greatly guided @lualvarezzuzek @oriartime @ricgallotti @mtizzoni @chub_fbk https://t.co/WQ0kRH5ygs 💊main results below👇

With a bit of delay, I am deeply honored and enthusiastic to receive the Best Paper by a Junior Researcher Award 2024 by @AssoComPol. It was a great journey, with great people @elenapavan1980 @Aurora_Perego.