26 Lab

In this lab, you will work to simulate a network-based epidemic model with population heterogeneity. This will use the special group attribute so that you can explore variations in both network structure and epidemic parmeterization. The specific learning objectives for this lab are to:

26.1 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:

Code

rm(list = ls())

26.2 Lab Steps

The model in Module 5 used a nodematch term with a target statistic value of 0 to parameterize a model in which none of the relations were within group.

Run that same model but change the epidemic parameters so that women (group 1) have twice the probability of infection (60% versus 30%) of males (Group 2). Inspect the group-specific prevalence and incidence plots, and calculate the raw (not standardized) cumulative incidence in these models and compare against the cumulative incidence in the tutorial.
Relax that assumption but keep everything else constant and allow for 50% of relationships to be within group and 50% to be across group. We will call this a proportional mixing model. The degree terms and target statistics may be kept unchanged, but update the nodematch target statistic accordingly. After you fit the model, diagnose it. Does the edges statistic look good?
Next run the an SIR epidemic model with the proportional mixing model, and with the two inf.prob parameters set to 0.2. Inspect the group-specific prevalence and incidence plots, and calculate the raw (not standardized) cumulative incidence in these models and compare against the cumulative incidence in the tutorial.

26.3 Lab Questions

After you have completed running the models above, please consider the following general questions:

What is the general relationship between the per-act infection probability and the epidemic outcomes (cumulative incidence) in your model with purely disortative mixing? What are a couple reasons why increasing the infection probability parameter 2-fold does not result in a similar relative increase in the cumulative incidence? What happens when mixing moves from disortative to proportional?
With the proportional mixing model, we didn’t change the degree-related target statistics. But would it be possible to do so? Conceptually, what would happen if we changed the degree(1) statistic for males (group 2) from 77.5 to some larger value (say, 100) in a proportional mixing model? Would the same balancing considerations apply as with the purely disortative mixing model? Where would those excess relations for males go?

# Lab ```{r setup, include = FALSE} library(knitr) opts_chunk$set(comment = NA) par(mar = c(3, 3, 1, 1), mgp = c(2, 1, 0)) ``` In this lab, you will work to simulate a network-based epidemic model with population heterogeneity. This will use the special `group` attribute so that you can explore variations in both network structure and epidemic parmeterization. The specific learning objectives for this lab are to: ## 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: ```{r} rm(list = ls()) ``` ## Lab Steps The model in Module 5 used a `nodematch` term with a target statistic value of 0 to parameterize a model in which none of the relations were within group. - Run that same model but change the epidemic parameters so that women (group 1) have twice the probability of infection (60% versus 30%) of males (Group 2). Inspect the group-specific prevalence and incidence plots, and calculate the raw (not standardized) cumulative incidence in these models and compare against the cumulative incidence in the tutorial. - Relax that assumption but keep everything else constant and allow for 50% of relationships to be within group and 50% to be across group. We will call this a **proportional mixing** model. The `degree` terms and target statistics may be kept unchanged, but update the `nodematch` target statistic accordingly. After you fit the model, diagnose it. Does the `edges` statistic look good? - Next run the an SIR epidemic model with the proportional mixing model, and with the two `inf.prob` parameters set to `0.2`. Inspect the group-specific prevalence and incidence plots, and calculate the raw (not standardized) cumulative incidence in these models and compare against the cumulative incidence in the tutorial. ## Lab Questions After you have completed running the models above, please consider the following general questions: 1. What is the general relationship between the per-act infection probability and the epidemic outcomes (cumulative incidence) in your model with purely disortative mixing? What are a couple reasons why increasing the infection probability parameter 2-fold does not result in a similar relative increase in the cumulative incidence? What happens when mixing moves from disortative to proportional? 2. With the proportional mixing model, we didn't change the degree-related target statistics. But would it be possible to do so? Conceptually, what would happen if we changed the `degree(1)` statistic for males (group 2) from 77.5 to some larger value (say, 100) in a proportional mixing model? Would the same balancing considerations apply as with the purely disortative mixing model? Where would those excess relations for males go?