Flu with Vaccine Model


This example builds on the the Flu with Behavior model (see ../flu-with-behavior) by adding an INFLUENZA_VACCINE condition that allows agents to be exposed to a vaccine that protects the agent from influenza with a probability equal to vaccine_effectiveness by setting INFLUENZA.sus = 0.

Review of code implementing the model

The code that implements the Flu with Vaccine model is contained in five .fred files:

  • main.fred

  • simpleflu.fred

  • stayhome.fred

  • vaccine.fred

  • parameters.fred


This main.fred contains four basic components. The first two will be familiar from the Simple Flu and Flu with Behavior models. The simulation block defines the dates and physical location of the simulation. A series of include statement refer to other .fred files to reference conditions, states, and variables necessary to run the simulation.

The Flu with Vaccine main.fred also directly defines a global variable.

variables {
    shared numeric flu_delay
    flu_delay = 90

In this case, it is the number of days to wait before vaccinating the first agents. The next code chunk modifies the INFLUENZA condition (which was previously defined in simpleflu.fred)

condition INFLUENZA {
    meta_start_state = ImportDelay

    state ImportDelay {
        wait(24 * flu_delay)

Specifically, it directs the meta-agent to start in the ImportDelay state and transition to Import after the delay. Looking back to simpleflu.fred, we can see that meta_start_state = Import. Calling include simpleflu.fred prior to this new condition block in the main.fred script causes the settings in the initial file to be overwritten. Now, rather than initializing in Import and immediately infecting some of the agents with INFLUENZA, the meta-agent will begin in ImportDelay and wait the parameterized delay before advancing to Import and assigning infections.


simpleflu.fred is identical to the file of the same name in the Simple Flu model. It specifies the state update rules for the INFLUENZA Condition that models agents’ status with respect to influenza infection. Specifically, each agent is in one of the following five states:

  1. Susceptible to infection with influenza

  2. Exposed to influenza

  3. Infectious and symptomatic with influenza

  4. Infectious and asymptomatic with influenza

  5. Recovered following infection with influenza and assumed to be immune

Refer to the tutorial on the Simple Flu Model for a complete explanation of the code in simpleflu.fred. For the purpose of this tutorial, consider the following code snippet which specifies the State rules for the InfectiousSymptomatic and Recovered States belonging to the INFLUENZA condition:

    state InfectiousSymptomatic {
        INFLUENZA.trans = 1
        wait(24* lognormal(5.0,1.5))


    state Recovered {
        INFLUENZA.trans = 0

Here INFLUENZA.trans = 1 and INFLUENZA.trans = 0 are action rules executed by agents entering the INFLUENZA.InfectiousSymptomatic state to change their transmissibility of influenza to 1, and agents entering the INFLUENZA.Recovered state to change their transmissibility of influenza to 0 (i.e. they are non-infectious). This change is accomplished by multiplying condition transmissiblity by the agent’s transmissibility, but in this case INFLUENZA transmissibility is already equal to 1. The wait rule wait(24* lognormal(5.0,1.5)) causes each agent that becomes infectious and symptomatic to remain so for a number of days determined by sampling from a lognormal distribution with median=5.0 and dispersion=1.5. Finally the transition rule next(Recovered) causes agents to transition, deterministically, to the Recovered state once their period of infection has elapsed. Once in Recovered, transmissibility is set to 0 and the agent waits indefinitely (wait()). The next() transition has no effect, but is required to have any following rule.


stayhome.fred is identical to the file of the same name in the Flu with Behavior model. This file defines new behavior for agents: while symptomatic with an influenza infection (specifically, while in the InfectedSymptomatic state of the INFLUENZA condition), agents will probabilistically stay at home all day rather than attending work or school.


vaccine.fred defines the variables, condition, and states (for both agents and the meta-agent) required to simulate basic vaccination behavior in our population.

The primary change to this simulation, and the entirety of this file, is to create an INFLUENZA_VACCINE condition with states that influence both this condition and the INFLUENZA condition. Agents begin in Start and advance to Considering with the probability willing_to_consider, defined in the variables block of the current file. Any agent that does not advance to Considering goes instead to the Excluded state for INFLUENZA_VACCINE and stays there permanently. These agents will not gain INFLUENZA immunity except by contracting the illness.

    state Considering {
        INFLUENZA_VACCINE.sus = 1

Agents that move to Considering have their susceptibility to INFLUENZA_VACCINE set to 1 and then wait to have the vaccine transmitted to the by the meta-agent.

    state Decide {
        INFLUENZA_VACCINE.sus = 0

    state Taker {
        wait(24 * days_until_effective)
        next(Immune) with prob(vaccine_effectiveness)

Once an agent in Considering is exposed by the meta-agent, they move to the Decide state. These agents change their susceptibility to the vaccine to zero, wait 24 hours, and then “take” the vaccine, moving to the Taker state. Here, agents wait until the vaccine becomes effective, defined in the variables block as days_until_effective, and then move to either Immune with the vaccine_effectiveness probability or Failed otherwise. Agents in the Immune state modify their INFLUENZA susceptibility to zero and can no longer be infected with that condition. The Failed state does not modify INFLUENZA susceptibility, mimicking a vaccination failure. Agents in both of these states remain there indefinitely.

    meta_start_state = ImportStart
    start_state = Start
    transmission_mode = proximity
    transmissibility = 1
    exposed_state = Decide


   state ImportStart {
        wait(24 * vaccine_delay)

    state ImportVaccine {

The remaining two states apply only to the meta-agent. At the top of the INFLUENZA_VACCINE condition, the meta-agent is started in ImportStart. We also define that the condition will be transmitted via proximity and that agents exposed to the condition will advance to the Decide state.

When the meta-agent begins in ImportStart, it waits the number of days defined by vaccine_delay (plus 1 hour) and then advances to ImportVaccine. During each time step, the meta-agent changes state before the ordinary agents, so it is common to have the meta-agent wait 1 time step at the start, so that ordinary agent have time to execute the rules in their start state. In this case, some ordinary agents end up on the Considering state at time 0, where they become susceptible to vaccination. In this ImportVaccine state, the meta-agent exposes some proportion of the Considering agents to the vaccine (moving them to Decide). This proportion is defined in the variables block as initial_vaccines, though we will alter this value in the parameters.fred file.


A parameters.fred file is also introduced in this model. This is largely a convenience for programmatically modifying the simulation parameters with a bash script. parameters.fred is the last file included in main.fred, allowing the contained values to overwrite any that were specified in earlier-imported files. The METHODS file in this directory defines two different FRED jobs by sequentially creating two different parameters.fred files and running fred_job with each of those files.

Sample Model Outputs

This model is run using the METHODS file in a FRED Local container. Running this file produces two sets of outputs that differ only in the proportion of the population that is originally vaccinated, 0% or 30%. Ideally, there would be a testing step in development to make sure that vaccines are being transmitted and imparting immunity (or reducing susceptibility) in the way that is expected. We can see that initially vaccinating 30% of the population reduces the cumulative infections substantially, providing evidence that the INFLUENZA_VACCINE condition is functioning as expected.


Cumulative cases over time


In addition to the features from previous tutorial modules, this model also includes:

  • A second, transmissible condition can alter the behavior of the original condition by directly changing susceptibility.

  • The use of ordered imports overwrites earlier parameter values.

  • An example of modifying parameters in successive simulations by rewriting parameters.fred.