Targeted pandemic containment through identifying local contact network bottlenecks
Fig 10
Simulation results under alternative model parametrization.
The transmission rate parameter is calibrated so that 55% of the population would be affected without intervention. We initialized both population-based and individual-based models using random initialization, where a few randomly selected nodes are labelled as infectious at time 0. The plots show for each dataset the final epidemic sizes under different intervention strategies and various percentage coverages. We average over 50 runs for random initialization. (A) Results for Facebook County. LF for λ ∈ {1/10, 1/50} still leads to the most reduction in epidemic sizes when the coverage level is less than 30%. When targeting more than 30% edges, uniform intervention (UI), which uniformly reduces the transmission rate over all edges, becomes more effective for Facebook County. This is because the transmission rate parameter is getting close to a threshold under which a pandemic would not emerge, which is seen in the 0% final size under UI at 50% coverage level. (B) Results for Wi-Fi Montreal. LF interventions still lead to the most reduction in the final sizes. (C) Results for Port. Sub. CF and LF with λ = 1/2 have the best performance overall, while LF with λ = 1/10 is the most effective when targeting less than 15% edges. (D) Results for Portland. We used λ = 1/1000 in order to scalably compute LF. We see that LF has better performance when targeting no more than 40% of all edges.