Jue Tao Lim, Dickens Borame Sue Lee, Yinan Mao, Woon Kwak Chae, Ching Ng Lee, Cook Alex R
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
Environmental Health Institute, National Environmental Agency, Singapore, Singapore.
J R Soc Interface. 2020 Jul;17(168):20200340. doi: 10.1098/rsif.2020.0340. Epub 2020 Jul 22.
Dengue is hyper-endemic in Singapore and Malaysia, and daily movement rates between the two countries are consistently high, allowing inference on the role of local transmission and imported dengue cases. This paper describes a custom built sparse space-time autoregressive (SSTAR) model to infer and forecast contemporaneous and future dengue transmission patterns in Singapore and 16 administrative regions within Malaysia, taking into account connectivity and geographical adjacency between regions as well as climatic factors. A modification to forecast impulse responses is developed for the case of the SSTAR and is used to simulate changes in dengue transmission in neighbouring regions following a disturbance. The results indicate that there are long-term responses of the neighbouring regions to shocks in a region. By computation of variable inclusion probabilities, we found that each region's own past counts were important to describe contemporaneous case counts. In 15 out of 16 regions, other regions case counts were important to describe contemporaneous case counts even after controlling for past local dengue transmissions and exogenous factors. Leave-one-region-out analysis using SSTAR showed that dengue transmission counts could be reconstructed for 13 of 16 regions' counts using external dengue transmissions compared to a climate only approach. Lastly, one to four week ahead forecasts from the SSTAR were more accurate than baseline univariate autoregressions.
登革热在新加坡和马来西亚高度流行，两国之间的日常流动率一直很高，这使得我们能够推断本地传播和输入性登革热病例的作用。本文描述了一个定制的稀疏时空自回归（SSTAR）模型，用于推断和预测新加坡以及马来西亚16个行政区内当前和未来的登革热传播模式，同时考虑到各地区之间的连通性、地理邻接性以及气候因素。针对SSTAR的情况，开发了一种预测脉冲响应的修正方法，并用于模拟受干扰后邻近地区登革热传播的变化。结果表明，邻近地区对某一地区的冲击存在长期响应。通过计算变量包含概率，我们发现每个地区自身过去的病例数对于描述当前病例数很重要。在16个地区中的15个地区，即使在控制了过去本地登革热传播和外部因素之后，其他地区的病例数对于描述当前病例数也很重要。使用SSTAR进行的留一地区法分析表明，与仅考虑气候因素的方法相比，利用外部登革热传播情况可以重建16个地区中13个地区的登革热传播病例数。最后，SSTAR提前一到四周的预测比基线单变量自回归更准确。
