Modeling the Effects of Disinformation Spread on Multi-Commodity Networks

Abstract

Disinformation has become a more common weapon amidst growing social media platforms and users, targeting consumer behavior to affect physical infrastructure. Understanding how disinformation could attack a multi-commodity network and how to minimize its spread will help alleviate stress on systems during attacks. This research integrates an epidemiological model (SIR) with a mixed integer programming (MIP) model to minimize weighted shortage across commodities, evaluating the change over time dependent on disinformation spread β and recovery γ. It is then applied to a multi-commodity Swedish railway network carrying 14 goods over 200 nodes. Results demonstrated how different spread and recovery rates change the degree and timing of shortage.