Human contact patterns are highly heterogeneous in terms of the both the number and nature of interactions. To incorporate these heterogeneities into infectious disease models one naturally represents a population as a weighted network. While there is a large literature on the spread of diseases on networks, most techniques are highly computational in nature. In this talk I will talk about an analytical framework for modeling infectious diseases as a percolation process on weighted networks based on probability generating functions.
In the context of vaccination, human contact heterogeneities become a resource: Vaccinating individuals with greater total exposure leads to a greater reduction in disease spread. We have proposed that exposure notification apps, such as COVID Alert, can be leveraged to improve vaccine uptake among high exposure individuals, thereby optimizing our limited COVID-19 vaccine supply. We demonstrate the efficiency of this proposal using our weighted percolation theory framework.
- Mathematical physics
- Scientific Series