论文标题
大流行中多种感染波的起源：固有的敏感性和外部感染分布的影响
Origin of Multiple Infection Waves in a Pandemic: Effects of Inherent Susceptibility and External Infectivity Distributions
论文作者
论文摘要
通常被忽略但在传染病进展中可能发挥关键作用的两个因素是固有的易感性（$σ_{inh} $）和外部感染性（$σ_{inh} $）的分布（$ c {inh} $）（$ 〜bimin_ {ext} $）。尽管前者是由个体对疾病的免疫力决定的，但后者取决于暴露于感染的持续时间。我们通过引入易感性和感染性分布来理解其综合效应，使用广义的SIR（易感性感染摄影）模型对大流行的时空传播进行建模，这似乎截止至今仍未得到充分解决。我们通过新的关键感染参数（cip）（$γ_C$）考虑$σ_{inh} $和$〜的$σ_{inh} $之间的耦合。我们发现，对这些分布的忽视，就像在天真的SIR模型中一样，导致人群中感染量高估，从而导致对达到群免疫性阈值所需的感染的不正确（较高）估计。此外，我们还包括通过长期迁移来包括人群中感染的影响。我们通过执行动力学蒙特卡洛细胞自动机（KMC-CA）模拟来解决所得的主方程。重要的是，我们的模拟可以重现大流行的多重感染峰值情景。疾病迁移与敏感性和感染性分布之间的潜在相互作用可以使发展与天真的SIR模型大不相同。特别是，包含这些附加功能会使问题的特征成为一个活着的渗透系统的特征，在该系统中，疾病簇通过从地区到地区迁移而生存。
Two factors that are often ignored but could play a crucial role in the progression of an infectious disease are the distributions of inherent susceptibility ($σ_{inh}$) and external infectivity ($ι_{ext}$), in a given population. While the former is determined by the immunity of an individual towards a disease, the latter depends on the duration of exposure to the infection. We model the spatio-temporal propagation of a pandemic using a generalized SIR (Susceptible-Infected-Removed) model by introducing the susceptibility and infectivity distributions to understand their combined effects, which appear to remain inadequately addressed till date. We consider the coupling between $σ_{inh}$ and $ι_{ext}$ through a new Critical Infection Parameter (CIP) ($γ_c$). We find that the neglect of these distributions, as in the naive SIR model, results in an overestimation of the amount of infection in a population, which leads to incorrect (higher) estimates of the infections required to achieve the herd immunity threshold. Additionally, we include the effects of seeding of infection in a population by long-range migration. We solve the resulting master equations by performing Kinetic Monte Carlo Cellular Automata (KMC-CA) simulations. Importantly, our simulations can reproduce the multiple infection peak scenario of a pandemic. The latent interactions between disease migration and the distributions of susceptibility and infectivity can render the progression a character vastly different from the naive SIR model. In particular, inclusion of these additional features renders the problem a character of a living percolating system where the disease cluster survives by migrating from region to region.
