Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, USA.
Bull Math Biol. 2020 Nov 24;82(12):152. doi: 10.1007/s11538-020-00831-x.
Factors such as seasonality and spatial connectivity affect the spread of an infectious disease. Accounting for these factors in infectious disease models provides useful information on the times and locations of greatest risk for disease outbreaks. In this investigation, stochastic multi-patch epidemic models are formulated with seasonal and demographic variability. The stochastic models are used to investigate the probability of a disease outbreak when infected individuals are introduced into one or more of the patches. Seasonal variation is included through periodic transmission and dispersal rates. Multi-type branching process approximation and application of the backward Kolmogorov differential equation lead to an estimate for the probability of a disease outbreak. This estimate is also periodic and depends on the time, the location, and the number of initial infected individuals introduced into the patch system as well as the magnitude of the transmission and dispersal rates and the connectivity between patches. Examples are given for seasonal transmission and dispersal in two and three patches.
季节和空间连通性等因素会影响传染病的传播。在传染病模型中考虑这些因素,可以提供有关疾病爆发的最危险时间和地点的有用信息。在这项研究中,我们制定了具有季节性和人口统计学可变性的随机多斑块传染病模型。使用随机模型来研究当感染个体被引入一个或多个斑块时疾病爆发的可能性。通过周期性的传播和扩散率来纳入季节性变化。多类型分支过程逼近和向后科尔莫戈罗夫微分方程的应用,导致了疾病爆发的概率估计。这个估计也是周期性的,取决于时间、位置和引入斑块系统的初始感染个体的数量,以及传播和扩散率以及斑块之间连通性的大小。给出了在两个和三个斑块中季节性传播和扩散的例子。