COVID-19: Analytics Of Contagion On Inhomogeneous Random Social Networks

Motivated by the need for novel robust approaches to modelling the Covid-19 epidemic, this paper treats a population of $N$ individuals as an inhomogeneous random social network (IRSN). The nodes of the network represent different types of individuals and the edges represent significant social relationships. An epidemic is pictured as a contagion process that changes daily, triggered on day $0$ by a seed infection introduced into the population. Individuals' social behaviour and health status are assumed to be random, with probability distributions that vary with their type. First a formulation and analysis is given for the basic SI ("susceptible-infective") network contagion model, which focusses on the cumulative number of people that have been infected. The main result is an analytical formula valid in the large $N$ limit for the state of the system on day $t$ in terms of the initial conditions. The formula involves only one-dimensional integration. Next, more realistic SIR and SEIR network models, including "removed" (R) and "exposed" (E) classes, are formulated. These models also lead to analytical formulas that generalize the results for the SI network model. The framework can be easily adapted for analysis of different kinds of public health interventions, including vaccination, social distancing and quarantine. The formulas can be implemented numerically by an algorithm that efficiently incorporates the fast Fourier transform. Finally a number of open questions and avenues of investigation are suggested, such as the framework's relation to ordinary differential equation SIR models and agent based contagion models that are more commonly used in real world epidemic modelling.