Category: Epidemiology

Summary: Testing whether intermediate response delays minimize epidemic-control cost before fatigue-driven feedback loops push the system into collapse-prone behavior.

Public-health responses often weaken over time as fatigue builds, and delayed interventions can interact with that fatigue in nonlinear ways. This experiment asks whether there is an intermediate intervention delay that outperforms both immediate and very late responses once epidemiological spread and response outages feed back on each other.

The model runs grid-based outbreak scenarios across multiple delays and fatigue levels, then measures total control cost, attack rate, peak infection, and mean outage. It also quantifies whether high-outage states line up with larger outbreaks and how often the system enters a collapse-like regime with both large peaks and large outages.

That turns delay choice into a structural stability question rather than a simple timing preference. The interesting outcome is whether the best response delay sits in the middle of the tested range, indicating a true feedback window instead of a monotone tradeoff.

Method: Repeated epidemic-grid simulations over intervention delays and fatigue strengths, followed by profile analysis of cost, outbreak size, outages, and collapse indicators.

What is measured: Best delay, intervention cost, attack rate, peak infection, mean outage, outage-outbreak correlation, fatigue slope, and collapse probability.