Home page

Objectives

The aim of this tutorial is to run scenario analyses with an individual-based model in R that accounts for location specific mixing.

In this session, you will

  1. Run an IBM with household, school/work and community mixing.
  2. Discuss the impact of transmission parameters and features

The best thing to do is to read each section and type (or copy and paste) the R commands (in grey boxes) into your own R session to check that you understand the code. Hands-on questions are indicated in red

Modelling concepts

The individual-based model will make use of a virtual population in which each individual is part of a household, school/work (based on age), and the general community. Each day (= time step), individuals can interact at home, school/work and in the community. We specify for each location a contact probability or average number of contacts per day.

Set working directory

The first step when creating a new R file is to specify a working directory. You need to tell the R session where you want it to work. To do this you use the command to “set the working directory” or setwd().

You can label this working directory as ‘home’, and have other locations stored for plotting or for getting data. This location should be an existing folder on your computer. For example

home <- "~/Documents/Modelling_intro/" ## on a OS X
home <- "C:\\Documents\\Modelling_intro\\" ## on windows
setwd(home)

Load model code

If the curl package is not installed yet, you can do so by

install.packages(curl) # install the curl library (to import data)

Please load the IBM functions we prepared on the GitHub repository. Using your skills from the previous tutorial(s), you should be able to code this model yourselves.

# load package 'curl'
library('curl')

# download R file with IBM function
curl_download("https://github.com/lwillem/modelling-intro/raw/master/lib/ibm_functions.R", destfile = "ibm_functions.R")

# load the IBM functions
source('ibm_functions.R')

Base case scenario

First, let’s run the model with the default parameters: 

# you can run the location-specific IBM with default parameters (see console)
run_ibm_location()

## [1] "MODEL PARAMETERS"
## [1] "pop_size: 2000"
## [1] "num_days: 50"
## [1] "num_infected_seeds: 3"
## [1] "vaccine_coverage: 0"
## [1] "rng_seed: 28"
## [1] "num_days_infected: 7"
## [1] "transmission_prob: 0.1"
## [1] "num_contacts_community_day: 4"
## [1] "contact_prob_household: 1"
## [1] "contact_prob_school: 0.5"
## [1] "contact_prob_workplace: 0.3"
## [1] "num_schools: 2"
## [1] "target_school_ages: 3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18"
## [1] "num_workplaces: 150"
## [1] "bool_show_demographics: TRUE"
## [1] "-------------"
## [1] "MODEL RESULTS"
## [1] "total incidence: 100%"
## [1] "Peak prevalence: 60%"
## [1] "Peak day:        12"
## [1] "Total run time:  3s"

What can you learn from these graphs?

Change some parameters

# increase num_contacts_community_day to 6
run_ibm_location(num_contacts_community_day = 6)

# set num_schools to 5
run_ibm_location(num_school = 6)

# set transmission_prob to 0.15 (and switch off the demographic plots)
run_ibm_location(transmission_prob = 0.15,
                 bool_show_demographics = FALSE)

Scenarios

Now, what happens if you…

  • change the population size to 100 or 2000?
  • start with more infected individuals?
  • add more schools
  • decrease the number of workplaces
  • allow only household contacts?
  • allow only community contacts?
  • allow only school contacts?
  • allow only household and school contacts?
  • allow only household and workplace contacts?
  • … etc.

(TIP: the run_ibm_location function has an option to add_baseline = TRUE to add model results with the default parameters. However, this increases the run time since the model is executed twice.)