# A Network Epidemic Model for Online Community Commissioning Data

## TL;DR

This paper introduces a network epidemic model combining preferential attachment and stochastic epidemic processes to analyze infection spread in online communities, highlighting its application to real data and discussing parameter identifiability issues.

## Contribution

It proposes a novel network epidemic model integrating preferential attachment with stochastic infection dynamics for online community analysis.

## Key findings

- Model fits online commissioning data effectively.
- Identifiability issues were observed in parameter estimation.
- Simulation study highlights challenges in parameter recovery.

## Abstract

A statistical model assuming a preferential attachment network, which is generated by adding nodes sequentially according to a few simple rules, usually describes real-life networks better than a model assuming, for example, a Bernoulli random graph, in which any two nodes have the same probability of being connected, does. Therefore, to study the propogation of "infection" across a social network, we propose a network epidemic model by combining a stochastic epidemic model and a preferential attachment model. A simulation study based on the subsequent Markov Chain Monte Carlo algorithm reveals an identifiability issue with the model parameters. Finally, the network epidemic model is applied to a set of online commissioning data.