[jinpan]

Semi-related: I'm working on agent-based simulations for coronavirus, exploring our individual actions affect outcomes.

In the first simulation, I take a look at single/dual shopper households and explore how choice affects viral spread on a societal level, and then on am individual household level.

You can check them out at https://coronavirus.simrnd.com/shopping_solo/

[jschwartzi]

You're making me want to make a simulation to explore how different behaviors among the populace of a geographic area can influence the spread of infection. It'd be cool to be able to set policies in a region as a behavioral control, and to simulate processes like hearsay and news reading as a mechanism for the spread of accurate and inaccurate information. You could have mechanics like testing materials, ppe availability, laboratory capacity, patient turnaround, symptom severity, et. al. control the effectiveness of different policies and other processes.

[jinpan]

Yeah that would be incredibly useful for testing policy in a simulated environment before releasing into the wild :)

One of the hardest challenges is "How do we choose realistic parameters for how the virus spreads? Infection duration? Mortality?"

But if we can incorporate the latest research and Monte Carlo simulate with ranges for the above parameters and find policies that are robust against those parameters, I think it would be an enormously powerful policy tool. Additionally, I think it could bring transparency into the decision process, something the public lacks.

If you want to contribute to the effort I started, the simulation code is open source at https://github.com/jinpan/covid-simulations. The engine is in Rust, which is then compiled to wasm.