Thiruchelvam Loshini, Dass Sarat C, Zaki Rafdzah, Yahya Abqariyah, Asirvadam Vijanth S
Fundamental and Applied Sciences Department, Faculty of Science and Information Technology, Universiti Teknologi Petronas, Bandar Seri Iskandar, Tronoh, Perak.
Geospat Health. 2018 May 7;13(1):613. doi: 10.4081/gh.2018.613.
This study investigated the potential relationship between dengue cases and air quality - as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were -800.66, -796.22, and -790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio- temporally based on neighbouring zones, do not have a significant effect on dengue cases.
本研究调查了马来西亚雪兰莪州五个区域的登革热病例与空气质量（通过空气污染指数（API）衡量）之间的潜在关系。可以使用基于反馈（滞后项）的预测模型来了解登革热病例模式。然而，基于此类反馈模型，空气质量是否影响登革热病例这一问题仍未得到充分研究。本研究采用自回归积分移动平均（ARIMA）和带外生变量的ARIMA（ARIMAX）时间序列方法，以API作为外生变量，开发了登革热预测模型。基于最大似然的Box Jenkins方法用于分析，因为它能给出有效的模型估计和预测。对每个区域进行了三个阶段的模型比较：首先是不含API的ARIMA模型，然后是使用该区域API监测站的API数据的ARIMAX模型，最后是使用该区域及其空间相邻区域的API数据的ARIMAX模型。贝叶斯信息准则（BIC）给出了所有得出的模型之间拟合优度与简约性的比较。我们的研究发现，BIC值最低的ARIMA模型在所有五个区域中表现优于其他模型。对于瓜拉雪兰莪区域，仅ARIMA、具有单个API成分的ARIMAX以及具有来自其区域和空间相邻区域的API成分的ARIMAX的BIC值分别为-800.66、-796.22和-790.5229。因此，我们得出结论，每个区域的API水平，无论是在时间上还是基于相邻区域的时空上，对登革热病例均无显著影响。
