COVID_MODEL
The COVID_MODEL belongs to a family of models called “SIR models,” where the letters stand for Susceptible (meaning that you are uninfected but susceptible to the disease), Infected (meaning that you are both infected and contagious), and Recovered (meaning that you have either recovered from the illness and are neither susceptible nor contagious, or that you have otherwise been removed from the population). In this model, people (sometimes called “agents”) are situated on an abstract two-dimensional space. Anytime a susceptible individual is connected to an infected individual, they become infected with some probability (VIRUS-SPREAD-CHANCE). An infected individual then recovers with a probability dictated by the disease’s recovery rate. The population looks like this. There are 250 agents. The blue ones are susceptible, the red ones are infected, and the grey ones are recovered.
Press on the NetLogo icon to open Web Covid_Model
The model is not specific to COVID-19, and ignores aspects like incubation periods. You should think about this as a generic model of disease transmission. The model parameter I want to focus on is one related to the agents’ connections, that you can control with CONNECTIONS slider. This slider controls the numbers of contacts for each individual, that is to say the movement restrictions policy.
Using the sliders, choose the POPULATION and the CONNECTIONS (number of contacts coming out of each individual).
The INITIAL-INFECTED slider determines how many of the individuals will start the simulation infected with the virus.
Then press SETUP to create the network. Press GO to run the model. The model will stop running once the virus has completely died out.
The VIRUS-SPREAD-CHANCE, VIRUS-CHECK-FREQUENCY sliders (discussed in "How it Works" above) can be adjusted before pressing GO, or while the model is running.
The NETWORK STATUS plot shows the number of individuals in each state (S, I, R) over time.
At the end of the run, after the virus has died out, some individuals are still susceptible, while others have become immune. What is the ratio of the number of immune individuals to the number of susceptible ones? How is this affected by changing the CONNECTIONS of the network?
## THINGS TO TRY
Set CONNECTIONS to minimum value (about 6), this corresponds to restriction policy to movement. How long does it take? How many infected individuals can we observe?
In alternative, in absence of movement restrictions (CONNECTIONS > 40) , how does the infected plot evolves?
Whether agents have any restrictions can dramatically affect the dynamics of disease transmission. I started with a population of 250 uninfected individuals and infected three of them at random. I then tracked the number of agents who were infected over time. In the following graph there are the results from simulations with narrowly (blue) and widely (red) individuals, defined as above as agents with a number of connections of either 6 or 40 links, respectively.
The graphic shows how the restrictions policy “flat the curve” of infefected individuals (Blue shape). Instead, when individuals have a lot of connections (No Restriction policy), the disease spreads very fast, and lots of them are infected at the same time (Red shape).