Spectrum Analysis of Contact Network for Public Policy in a Pandemic

In a pandemic, in order to slow down the spread of virus, protect national health, and maintain normal operation economic activities, countries around world will formulate public policies limit number citizens that can gather. Our research focuses on how achieve optimal policy under different conditions. Traditional SIR SEIR models well reflect transmission process obtain credible prediction results from macro perspective, but lack sensitivity micro data, cannot assess risk epidemic brought by close contacts sub-close contacts. Based Barabási-Albert scale-free network Random spanning tree algorithm, we generate simulated for non-specific infectious diseases. At same time, also group networks gather constraints. The superposition two forms composite contact network. work shows after considering contacts, optimization problem slowing be answered spectrum analysis We perform computer simulations theoretical proofs this model, conduct its process.