Tufts University Initiative for Forecasting and Modeling of Infectious Diseases (InForMID), 196 Boston Ave, Medford, MA 02155, USA.
School of Life and Environmental Sciences and Marie Bashir Institute of Infectious Diseases and Biosecurity, The University of Sydney, Australia.
Sci Rep. 2017 Jan 10;7:40186. doi: 10.1038/srep40186.
Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three common mosquito-borne diseases in Australia that display notable seasonal patterns. Although all three diseases have been modeled on localized scales, no previous study has used harmonic models to compare seasonality of mosquito-borne diseases on a continent-wide scale. We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters. The models captured an average of 50-65% variability of the data. Disease incidence for all three diseases generally peaked in January or February, but peak timing was most variable for dengue. The most significant predictor parameters were trend and inter-annual periodicity for BFV, intra-annual periodicity for RRV, and trend for dengue. We found that a Temperature Suitability Index (TSI), designed to reclassify climate data relative to optimal conditions for vector establishment, could be applied to this context. Finally, we extrapolated our models to estimate the impact of a false-positive BFV epidemic in 2013. Creating these models and comparing variations in periodicities may provide insight into historical outbreaks as well as future patterns of mosquito-borne diseases.
罗斯河病毒（RRV）、班加罗尔森林病毒（BFV）和登革热是澳大利亚三种常见的蚊媒传染病，具有明显的季节性模式。尽管这三种疾病都在局部范围内进行了建模，但以前没有研究使用调和模型在全大陆范围内比较蚊媒传染病的季节性。我们使用泊松调和回归模型来拟合 RRV、BFV 和登革热的监测数据（分别从 1993 年、1995 年和 1991 年到 2015 年），纳入季节性、趋势和气候（温度和降雨）参数。这些模型捕捉到了数据的 50-65%的变化。所有三种疾病的发病率通常在 1 月或 2 月达到峰值，但登革热的峰值时间最不稳定。BFV 的最显著预测参数是趋势和年度周期性，RRV 是年度周期性，登革热是趋势。我们发现，一种温度适宜指数（TSI）可用于此环境，该指数旨在重新分类气候数据相对于建立媒介的最佳条件。最后，我们将我们的模型外推，以估计 2013 年 BFV 假阳性流行的影响。创建这些模型并比较周期性变化可能有助于深入了解历史疫情以及蚊媒传染病的未来模式。
