论文标题
流行病在社交网络中散布，面具戴着个人
Epidemic Spreading in a Social Network with Facial Masks wearing Individuals
论文作者
论文摘要
在本文中，我们提出了一个易感感染的（SIR）模型，其中戴着面具和不戴面膜的个人。疾病的传播率，恢复率和戴口罩的个人的比例一直依赖于模型。我们根据约翰·霍普金斯大学（John Hopkins University）发布的COVID-19数据，对疾病传播率和恢复率进行了逐步估算。我们通过最大似然估计来确定戴口罩的个人比例，这最大化了随机易感感染的恢复模型的过渡概率。如果感染个体的数量较大，则在数值上很难计算过渡概率。我们基于中央极限定理和平均场近似开发了过渡概率的近似值。我们通过数值研究表明我们的近似值很好。我们开发了键合分析，以预测被感染的人群的最终部分，假设SIR模型的参数不再改变。我们使用我们的理论来预测Covid-19-19的结果。
In this paper, we present a susceptible-infected-recovered (SIR) model with individuals wearing facial masks and individuals who do not. The disease transmission rates, the recovering rates and the fraction of individuals who wear masks are all time dependent in the model. We develop a progressive estimation of the disease transmission rates and the recovering rates based on the COVID-19 data published by John Hopkins University. We determine the fraction of individual who wear masks by a maximum likelihood estimation, which maximizes the transition probability of a stochastic susceptible-infected-recovered model. The transition probability is numerically difficult to compute if the number of infected individuals is large. We develop an approximation for the transition probability based on central limit theorem and mean field approximation. We show through numerical study that our approximation works well. We develop a bond percolation analysis to predict the eventual fraction of population who are infected, assuming that parameters of the SIR model do not change anymore. We predict the outcome of COVID-19 pandemic using our theory.
