Computational Epidemiology Laboratory, Institute for Scientific Interchange-ISI, Torino, Italy.
Sci Rep. 2012;2:476. doi: 10.1038/srep00476. Epub 2012 Jun 27.
Infectious diseases outbreaks are often characterized by a spatial component induced by hosts' distribution, mobility, and interactions. Spatial models that incorporate hosts' movements are being used to describe these processes, to investigate the conditions for propagation, and to predict the spatial spread. Several assumptions are being considered to model hosts' movements, ranging from permanent movements to daily commuting, where the time spent at destination is either infinite or assumes a homogeneous fixed value, respectively. Prompted by empirical evidence, here we introduce a general metapopulation approach to model the disease dynamics in a spatially structured population where the mobility process is characterized by a heterogeneous length of stay. We show that large fluctuations of the length of stay, as observed in reality, can have a significant impact on the threshold conditions for the global epidemic invasion, thus altering model predictions based on simple assumptions, and displaying important public health implications.
传染病的爆发通常具有由宿主的分布、流动性和相互作用引起的空间成分。纳入宿主运动的空间模型正被用于描述这些过程,以研究传播的条件并预测空间传播。正在考虑几种假设来模拟宿主的运动,从永久性运动到日常通勤不等,其中在目的地停留的时间要么无限,要么分别假定为同质的固定值。受经验证据的启发,我们在这里引入了一种一般的复域种群方法来模拟空间结构种群中的疾病动态,其中流动过程的特点是停留时间的异质性。我们表明,在现实中观察到的停留时间的大波动会对全球流行病入侵的阈值条件产生重大影响,从而改变基于简单假设的模型预测,并显示出重要的公共卫生意义。