Bayesian dynamic modeling of time series of dengue disease case counts.
Bayesian dynamic modeling of time series of dengue disease case counts.
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DOI：
10.1371/journal.pntd.0005696
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发表时间：
2017-07
影响因子：
3.8
通讯作者：
Torres-Prieto A
中科院分区：
文献类型：
--
作者：
Martínez-Bello DA;López-Quílez A;Torres-Prieto A
本研究的目的是在2008年1月至2015年8月期间，在哥伦比亚的一个高发城市，应用贝叶斯分层动态广义线性模型，对登革热病例计数的每周时间序列与气象变量之间的关联进行建模。此外，我们还评估了该模型预测登革热病例的短期性能。该方法显示了动态泊松对数链接模型，包括气象变量的常数或随时间变化的系数。日历效应使用常数或一阶或二阶随机游走时变系数建模。气象变量采用常系数和一阶随机游走时变系数建模。我们应用马尔可夫链蒙特卡罗模拟的参数估计，偏差信息准则统计（DIC）的模型选择。我们使用平均绝对百分比误差在研究期间的几个时间点评估了所选最终模型的短期预测性能。结果表明，最佳模型包括日历趋势的一阶随机游走时变系数和气象变量的一阶随机游走时变系数。除了计算的挑战，解释结果意味着一个完整的分析登革热的时间序列相对于气象影响的参数估计。我们发现，对于大多数预测点，在一周或两周的样本外预测中，平均绝对百分比误差值较小，与登革热计数的低波动期相关。我们讨论了动态泊松模型的优势和局限性，研究登革热疾病的时间序列和气象变量之间的关联。该研究的主要结论是，动态泊松模型解释了登革热疾病时间序列建模中涉及的变量的动态性质，为公共卫生决策提供了有用的模型。目前采用登革热病例数的时间序列分析来建立登革热与环境、社会经济和气候变量之间的联系，并预测登革热流行的演变。如今，人们普遍认为，环境温度、降雨量和相对湿度等气候因素会改变登革热病媒的行为，从而影响疾病的传播。因此，在缺乏病媒数据的情况下，气候因素通常被用来输入登革热病在几个时间和空间尺度上的传播模型。我们应用层次贝叶斯动态广义模型，登革热病例数在哥伦比亚的一个中等规模的城市，与常数和随时间变化的日历趋势系数，常数和随时间变化的气象变量（温度，降雨量，太阳辐射和相对湿度）的系数。我们选择了一个最终模型，用于探索气候变量和登革热之间的时变关联，以及研究期间登革热计数的短期样本外预测。我们说明了建模过程，以便多学科研究团队的数据分析师可以集成时间序列模型，以考虑数据的时变性质。
The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health. Time series analysis of dengue disease case counts are currently employed to establish associations between dengue disease and environmental, socioeconomic and climatic variables and to predict the evolution of dengue epidemics. Nowadays there is acceptance that climatic factors like environmental temperature, rainfall and relative humidity modify the behavior of the dengue vectors, affecting the transmission of the disease. Thus, in the absence of vector data, climatic factors are commonly used to input transmission models of dengue disease on several temporal and spatial scales. We applied hierarchical Bayesian dynamic generalized models to dengue diseases case counts in a medium-sized city in Colombia, with constant and time-varying coefficients for calendar trend, and constant and time-varying coefficients for meteorological variables (temperature, rainfall, solar radiation and relative humidity). We selected a final model useful for exploring of the time-varying association between climatic variables and dengue, and the short-term out-of-sample predictions of dengue counts within the study period. We illustrate the modeling process so a data analyst on a multidisciplinary research team could integrate a time series model accounting for the time-varying nature of the data.
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DOI：
10.1111/1467-9876.00280
发表时间：
2002-01-01
影响因子：
1.6
作者：
Chiogna, M;Gaetan, C
通讯作者：
Gaetan, C
影响因子：
2.7
作者：
Gamerman, D
通讯作者：
Gamerman, D
影响因子：
2.8
作者：
Malhao, Thaina Alves;Casquilho Resende, Camila Maria;Medronho, Roberto de Andrade
通讯作者：
Medronho, Roberto de Andrade
影响因子：
8.1
作者：
Ehelepola ND;Ariyaratne K;Buddhadasa WM;Ratnayake S;Wickramasinghe M
通讯作者：
Wickramasinghe M
影响因子：
3.8
作者：
Villar LA;Rojas DP;Besada-Lombana S;Sarti E
通讯作者：
Sarti E