Bifolchi Nadia, Deardon Rob, Feng Zeny
Department of Mathematics & Statistics, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada.
Spat Spatiotemporal Epidemiol. 2013 Sep;6:59-70. doi: 10.1016/j.sste.2013.07.001. Epub 2013 Jul 22.
Often, when modeling infectious disease spread, the complex network through which the disease propagates is approximated by simplified spatial information. Here, we simulate epidemic spread through various contact networks and fit spatial-based models in a Bayesian framework using Markov chain Monte Carlo methods. These spatial models are individual-level models which account for the spatio-temporal dynamics of infectious disease. The focus here is on choosing a spatial model which best predicts the true probabilities of infection, as well as determining under which conditions such spatial models fail. Spatial models tend to predict infection probability reasonably well when disease spread is propagated through contact networks in which contacts are only within a certain distance of each other. If contacts exist over long distances, the spatial models tend to perform worse when compared to the network model.
在对传染病传播进行建模时，疾病传播所经由的复杂网络通常会通过简化的空间信息来近似。在此，我们通过各种接触网络模拟疫情传播，并使用马尔可夫链蒙特卡罗方法在贝叶斯框架下拟合基于空间的模型。这些空间模型是个体层面的模型，考虑了传染病的时空动态。这里的重点是选择一个能最佳预测真实感染概率的空间模型，以及确定在哪些条件下此类空间模型会失效。当疾病通过接触仅在彼此一定距离内的接触网络传播时，空间模型往往能较好地预测感染概率。如果存在远距离接触，与网络模型相比，空间模型往往表现更差。
