Interactive Agent-Based Model Visualization for COVID-19 Masking

Watch this tutorial video and then play with this masksim simulator yourself — we're making an interactive version of this agent-based model available here for you to try experimenting with our new individual agent-based model, and see what different masking policies do. You'll see how much small accidents can affect the spread of infections as we've made each run completely randomized. Try many runs if you want to see the statistical tendencies under different masking policies.
day 0 total infected 0    
 percentage of population wearing masks
 M=   % masking themselves

 universal masking culture or policy
      people are masking themselves
 M=   % masking themselves per day
      people are unmasking themselves
 U=   % unmasking themselves per day
      min # masked people
      max # masked people
 mask characteristics
 T =   % mask transmission rate
 A=   % mask absorption rate
 initial population numbers & color key
 S =   susceptible
 E =   exposed
 I =   infected
 R =   recovered
 baseline SEIR model parameters
 β =   infection rate
 γ =   recovery rate = 1 / days
 σ =   exposed to infected rate
 μ =   baseline mortality rate

masksim v20200426 De Kai @dekai123