To Simulate the Spread of Infectious Diseases by the Random Matrix

TL;DR

This paper introduces a novel random matrix-based model to efficiently simulate infectious disease spread in social networks, capturing realistic contact patterns and invariant key indicators, facilitating optimal control strategy development.

Contribution

The paper presents a new model using random matrices and Markov processes to simulate disease spread, with size-invariant key indicators, enabling realistic and efficient scenario analysis.

Findings

Disease spread indicators are invariant to population size.

The model effectively simulates realistic contact scenarios.

Facilitates large-scale simulation for control strategy optimization.

Abstract

The main aim to build models capable of simulating the spreading of infectious diseases is to control them. And along this way, the key to find the optimal strategy for disease control is to obtain a large number of simulations of disease transitions under different scenarios. Therefore, the models that can simulate the spreading of diseases under scenarios closer to the reality and are with high efficiency are preferred. In the realistic social networks, the random contact, including contacts between people in the public places and the public transits, becomes the important access for the spreading of infectious diseases. In this paper, a model can efficiently simulate the spreading of infectious diseases under random contacts is proposed. In this approach, the random contact between people is characterized by the random matrix with elements randomly generated and the spread of the diseases is simulated by the Markov process. We report an interesting property of the proposed model: the main indicators of the spreading of the diseases such as the death rate are invariant of the size of the population. Therefore, representative simulations can be conducted on models consist of small number of populations. The main advantage of this model is that it can easily simulate the spreading of diseases under more realistic scenarios and thus is able to give a large number of simulations needed for the searching of the optimal control strategy. Based on this work, the reinforcement learning will be introduced to give the optimal control strategy in the following work.