34  Lab

In this lab, you can work to simulate a network-based epidemic model with demography (births and deaths).

34.1 Setup

Once you are ready, start out by clearing your R object environment, to make sure that you do not have any objects lingering from the tutorial. This can be accomplished with:

rm(list = ls())

34.2 Lab Steps

In the Module 8 Demography Tutorial, we first used a homogeneous and relatively low mortality rate (0.001). Let’s explore what happens to the model and network structure with further changes to the mortality rate and birth (arrival) rate.

• Assume a slightly growing population. Instead of modeling the a.rate as the same value as the death rates, use a slightly higher value (e.g., 0.0015) but keep the death rates at 0.001. Do you need to adjust the dissolution_coefs parameterization here? What happens to the overall population structure (population size) and network structure (edges and meandeg post-simulation diagnostics)?

• Use the original arrival/birth rate, but assume a higher overall death rates with disease-related mortality: ds.rate = 0.0012 and di.rate = 0.0025. Figure out how to parameterize the d.rate argument in dissolution_coefs with some trial and error: first start by taking the simple average, and then experiment with higher values that better keep the network diagnostics in check.