Zero-State Coupled Markov Switching Count Models for Spatio-Temporal Infectious Disease Spread
针对传染病病例时空计数中零值过多的问题，提出零状态耦合马尔可夫切换负二项模型，允许疾病在区域间存在与消失的动态切换，并用里约热内卢登革热数据验证了贝叶斯推断与预测效果。
Abstract Spatio-temporal counts of infectious disease cases often contain an excess of zeros. With existing zero-inflated count models applied to such data it is difficult to quantify space-time heterogeneity in the effects of disease spread between areas. Also, existing methods do not allow for separate dynamics to affect the reemergence and persistence of the disease. As an alternative, we develop a new zero-state coupled Markov switching negative binomial model, under which the disease switches between periods of presence and absence in each area through a series of partially hidden nonhomogeneous Markov chains coupled between neighbouring locations. When the disease is present, an autoregressive negative binomial model generates the cases with a possible zero representing the disease being undetected. Bayesian inference and prediction is illustrated using spatio-temporal counts of dengue fever cases in Rio de Janeiro, Brazil.