Spectrum Analysis of Contact Network for Public Policy in a Pandemic

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