Welcome to the latest post of the Graphs For Science substack, a cross post from the “Epidemic Modeling” series where I aim to guide you through the mathematical and conceptual details underlying epidemic modeling with a focus on what’s most relevant to the current CoVID-19 pandemic.
In this post, we explore some simple network models in order to better understand the role that degree correlations play in disease spreading. I aim to make each post self contained, but you might want to check out our previous posts to brush up on the basics of epidemic modeling:
Epidemic Modeling 101: Or why your CoVID-19 exponential fits are wrong
Epidemic Modeling 102: All CoVID-19 models are wrong, but some are useful
Epidemic Modeling 103: Adding confidence intervals and stochastic effects to your CoVID-19 Models
Epidemic Modeling 104: Impact of Seasonal effects on CoVID-19
Epidemiology 201: Network Structure, Super-spreaders and Contact Tracing
