I would have thought the standard way to resolve some of the questions above would be to use a large agent-based model, simulating disease transmission among millions of agents and then observing how successful some testing scheme is within the model (you might be able to backtest the model against well-documented outbreaks).
I’m not sure how much you’d trust these models over your intuitions, but I’d guess they’d have quite a lot of mileage.
I’ve only skimmed these papers, but these seem promising and illustrative of the direction to me:
The best stuff looking at global-scale analysis of epidemics is probably by GLEAM. I doubt full agent-based modelling at small-scales is giving you much but massively complicating the model.
Great post—I really enjoyed reading this.
I would have thought the standard way to resolve some of the questions above would be to use a large agent-based model, simulating disease transmission among millions of agents and then observing how successful some testing scheme is within the model (you might be able to backtest the model against well-documented outbreaks).
I’m not sure how much you’d trust these models over your intuitions, but I’d guess they’d have quite a lot of mileage.
I’ve only skimmed these papers, but these seem promising and illustrative of the direction to me:
Scaling of agent-based models to evaluate transmission risks of infectious diseases
3D Agent-Based Model of Pedestrian Movements for Simulating COVID-19 Transmission in University Students
BioWar: scalable agent-based model of bioattacks
The best stuff looking at global-scale analysis of epidemics is probably by GLEAM. I doubt full agent-based modelling at small-scales is giving you much but massively complicating the model.