Forecasting dengue epidemics using a hybrid methodology
Forecasting dengue epidemics using a hybrid methodology
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DOI：
10.1016/j.physa.2019.121266
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发表时间：
2019-08-01
影响因子：
3.3
通讯作者：
Ghosh, Indrajit
中科院分区：
文献类型：
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作者：
Chakraborty, Tanujit;Chattopadhyay, Swarup;Ghosh, Indrajit
登革热病例管理是一个非常重要的全球卫生问题。由于外部和内部因素造成登革热流行率的非线性波动，资源的有效分配往往很困难。我们的目标是建立一个预警系统，可以准确地预测随后的登革热病例在三个登革热流行地区，即圣胡安，伊基托斯和菲律宾。该问题被单独视为一个时间序列预测问题，忽略了已知的登革热流行病学以及其他气象变量。自回归积分滑动平均（ARIMA）模型是线性数据结构的经典时间序列模型，而随着神经网络的出现，可以处理数据集中的非线性结构。在本文中，我们提出了一种新的混合模型结合ARIMA和神经网络自回归（NNAR）模型来捕捉数据集的线性和非线性。ARIMA模型过滤掉数据中的线性趋势，并将残差值传递给NNAR模型。所提出的混合方法被应用到三个登革热时间序列数据集，并发现提供更好的预测精度相比，国家的最先进的。本研究的结果表明，登革热病例可以准确地预测在足够的时间内使用所提出的混合方法。(C)2019 Elsevier B.V.版权所有。
Dengue case management is an alarmingly important global health issue. The effective allocation of resources is often difficult due to external and internal factors imposing nonlinear fluctuations in the prevalence of dengue fever. We aimed to construct an early-warning system that could accurately forecast subsequent dengue cases in three dengue endemic regions, namely San Juan, Iquitos, and the Philippines. The problem is solely regarded as a time series forecasting problem ignoring the known epidemiology of dengue fever as well as the other meteorological variables. Autoregressive integrated moving average (ARIMA) model is a popular classical time series model for linear data structures whereas with the advent of neural networks, nonlinear structures in the data set can be handled. In this paper, we propose a novel hybrid model combining ARIMA and neural network autoregressive (NNAR) model to capture both linearity and nonlinearity in the data sets. The ARIMA model filters out linear tendencies in the data and passes on the residual values to the NNAR model. The proposed hybrid approach is applied to three dengue time-series data sets and is found to give better forecasting accuracy in comparison to the state-of-the-art. The results of this study indicate that dengue cases can be accurately forecasted over a sufficient time period using the proposed hybrid methodology. (C) 2019 Elsevier B.V. All rights reserved.