In this lab, you will explore how infectious diseases spread through wildlife populations using the SIR (Susceptible–Infected–Recovered) model. We will begin with a hands-on simulation of a small outbreak to visualize how infection moves through a population over time. Then, we will shift to a real-world case study: canine distemper virus (CDV) in foxes. Using an epidemic simulator, you will investigate how factors such as population density and vaccination shape disease outcomes.
Disease is an important ecological force that shapes population dynamics, species interactions, and conservation outcomes. Just like predators or competition, pathogens can limit population growth and even trigger dramatic declines. Ecologists use models to study these processes because models allow us to test how diseases might spread under different scenarios and evaluate the potential impact of interventions like vaccination.
The SIR model is one of the most widely used frameworks for studying disease dynamics. It groups individuals into three categories:
Susceptible (S) – individuals that can be infected
Infected (I) – individuals that currently have and can spread the disease
Recovered (R) – individuals that are no longer susceptible, either because they survived infection or were vaccinated
In many disease models, including the ones used in this lab, the category R is labeled as “Removed” rather than “Recovered.” This is because the model isn’t only tracking individuals who recover with immunity, but anyone who is no longer able to spread the disease, because:
They recovered and gained immunity,
They were vaccinated and became immune, or
They died from the disease and are no longer part of the susceptible or infectious pool.
In other words, “Removed” means that the individual has exited the cycle of transmission. They don’t contribute to new infections, even though the reason for removal can differ.
By tracking how individuals move among these categories, scientists can predict how fast a disease spreads, how long an outbreak lasts, and how many individuals remain uninfected.
Several strategies can reduce disease spread in both human and wildlife populations:
Quarantines – isolating infected individuals so they cannot transmit the disease further.
Contact tracing – identifying and monitoring individuals who were exposed to an infected individual to stop chains of transmission early.
Vaccination – moving individuals directly into the “Recovered” (immune) category, lowering the proportion of susceptibles and reducing outbreak potential.
Herd immunity – achieved when enough of the population is immune (through vaccination or prior infection) that disease transmission can no longer sustain itself.
A key concept that links all these strategies is the basic reproduction number (R₀), which represents the average number of new infections caused by a single infected individual in a fully susceptible population.
If R₀ > 1, the disease can spread and cause an outbreak
If R₀ < 1, the outbreak will fade out.
This concept can be illustrated with a real world example. In 1971, African swine fever spread to Cuba, threatening both food security and the economy. Because there was no vaccine and the virus spread rapidly among pigs, officials resorted to culling (burning and burying) nearly half a million pigs. This drastic step reduced the number of susceptible hosts and ultimately helped contain the outbreak. While devastating, it demonstrates the same principle modeled in the SIR framework: reducing the susceptible pool can halt transmission.
A related concept is the Herd Immunity Threshold (HIT), which describes the proportion of the population that must be immune (through vaccination or prior infection) to prevent sustained disease spread.
HIT = 1 – (1/R₀)
Example: If R₀ = 3, then HIT = 1 – 1/3 = 2/3 = 67%
In Part 1 of this lab, you will simulate an outbreak in a small sample population to build intuition about transmission, recovery, and vaccination. In Part 2, you’ll apply these ideas to a case study of CDV in wild foxes, a virus that has caused major population crashes in carnivores worldwide. You will test how contact rates (driven by population density) and vaccination strategies change the course of an epidemic and explore the concept of herd immunity.
While the models used here are simplified, they highlight fundamental principles of disease ecology. By the end of the lab, you should be able to connect disease dynamics to ecological theory, understand how density and immunity influence outbreaks, and appreciate the challenges of managing disease in wildlife populations.
We’ll start with a small population of 36 individuals to see how a disease spreads day by day.
Go to the BioInteractive Outbreak Simulator. Set the Transmission Probability to 33% and the Recovery Probability to 33%. Select an individual in the population grid to Set Initial Case, click the "Start Simulator" button and follow the instructions.
For each infectious individual in the population grid, select their icon, then “Simulate New Infections.” Select “Done” when finished with all infectious individuals.
Select one susceptible individual to vaccinate, then select “Done.”