Effects of Contact Network Models on Stochastic Epidemic Simulations

TL;DR

This study examines how different contact network models influence the results of stochastic epidemic simulations, highlighting the importance of preserving node degrees over clustering for accuracy.

Contribution

It provides a comparative analysis of contact network models' effects on epidemic simulation outcomes using real sensor data.

Findings

Preserving node degrees is crucial for accurate epidemic modeling.

Cluster structure preservation has less impact on simulation accuracy.

Different network models significantly alter epidemic spread predictions.

Abstract

The importance of modeling the spread of epidemics through a population has led to the development of mathematical models for infectious disease propagation. A number of empirical studies have collected and analyzed data on contacts between individuals using a variety of sensors. Typically one uses such data to fit a probabilistic model of network contacts over which a disease may propagate. In this paper, we investigate the effects of different contact network models with varying levels of complexity on the outcomes of simulated epidemics using a stochastic Susceptible-Infectious-Recovered (SIR) model. We evaluate these network models on six datasets of contacts between people in a variety of settings. Our results demonstrate that the choice of network model can have a significant effect on how closely the outcomes of an epidemic simulation on a simulated network match the outcomes on the actual network constructed from the sensor data. In particular, preserving degrees of nodes appears to be much more important than preserving cluster structure for accurate epidemic simulations.