This model simulates the infectious disease outbreak dynamics, such as COVID-19, related to the factors of population density, transmission rate, mortality rate, and individual mobility in two populations. It can be used to compare various infectious disease outbreak scenarios, such as outbreaks in a bigger or smaller city, grounding, different levels of transmission rate, etc.
Grades: 9-12;
The model starts with two isolated human populations in which all people are healthy but susceptible (green color) to the incoming pathogen. Once an infected case (orange color) appears in the population, it will pass the disease to one of the susceptible people nearby (within a radius of 1.5) at the defined transmission rate. The infected people are able to transmit the disease for 14 days. By the 15th day of being infected, the people either die (disappears from the model) at the defined mortality rate, or recover and become immunized (blue color).
The left and right populations can be set as different sizes using the sliders. Given they occupy the same size of the area, the larger is the population size, the higher is the population density.
The mobility of the left and right populations can be set differently to simulate the self-isolating or a rough social distancing in the two populations .
The transmission rate and mortality equally apply to the people in both populations.
Each tick in the model is regarded as a day. The model is initially set to stop on the 180th day. Change the number in "Time" if you want to run the model for a longer or shorter time.
Use the button "Watch an infected person" to follow an infected person.
Use the button "Add/Remove Divider" to separate or connect two populations.
Use the button "run one day" to run the model for only one tick. This button is useful for examining the dynamic in a controlled way and collecting day-by-day data.
THINGS TO TRY
There are so many things you can try in this model. Here are only very a few quick ideas:
Can you use the model to simulate the outbreak in a bigger city, a smaller town, a rural area? How fast the disease spread in these places?
Can you use the model to simulate the disease caused by a pathogen that is highly infectious and very deadly, moderate infectious but not very deadly, or other combinations of these two factors?
How will the disease spreading be affected when mobility is decreased, for example, conduct self-isolating, play social distancing?
When can you get a flat but longer-lasting curve? What does it mean for a community?
Before we get an effective vaccine or medicine for the pathogen, how can we minimize the number of deceased individuals in this model?
Dr. Lin Xiang creates this module at the University of Kentucky in 2019. If you mention this model in a publication, we ask that you include the citations below.
Xiang, L. (2020). Infectious Disease Outbreak-Population Comparison. Department of STEM Education, University of Kentucky, Lexington, KY.
Questions? Comments? Email us.