Zhao Desheng, Wang Lulu, Cheng Jian, Xu Jun, Xu Zhiwei, Xie Mingyu, Yang Huihui, Li Kesheng, Wen Lingying, Wang Xu, Zhang Heng, Wang Shusi, Su Hong
Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui Province, 230032, China.
School of Nursing, Anhui Medical University, Hefei, Anhui, China.
Int J Biometeorol. 2017 Mar;61(3):453-461. doi: 10.1007/s00484-016-1225-9. Epub 2016 Aug 24.
Hand, foot, and mouth disease (HFMD) is one of the most common communicable diseases in China, and current climate change had been recognized as a significant contributor. Nevertheless, no reliable models have been put forward to predict the dynamics of HFMD cases based on short-term weather variations. The present study aimed to examine the association between weather factors and HFMD, and to explore the accuracy of seasonal auto-regressive integrated moving average (SARIMA) model with local weather conditions in forecasting HFMD. Weather and HFMD data from 2009 to 2014 in Huainan, China, were used. Poisson regression model combined with a distributed lag non-linear model (DLNM) was applied to examine the relationship between weather factors and HFMD. The forecasting model for HFMD was performed by using the SARIMA model. The results showed that temperature rise was significantly associated with an elevated risk of HFMD. Yet, no correlations between relative humidity, barometric pressure and rainfall, and HFMD were observed. SARIMA models with temperature variable fitted HFMD data better than the model without it (sR increased, while the BIC decreased), and the SARIMA (0, 1, 1)(0, 1, 0) offered the best fit for HFMD data. In addition, compared with females and nursery children, males and scattered children may be more suitable for using SARIMA model to predict the number of HFMD cases and it has high precision. In conclusion, high temperature could increase the risk of contracting HFMD. SARIMA model with temperature variable can effectively improve its forecast accuracy, which can provide valuable information for the policy makers and public health to construct a best-fitting model and optimize HFMD prevention.
手足口病（HFMD）是中国最常见的传染病之一，当前气候变化已被认为是一个重要因素。然而，尚未提出基于短期天气变化来预测手足口病病例动态的可靠模型。本研究旨在探讨天气因素与手足口病之间的关联，并探索结合当地天气条件的季节性自回归积分滑动平均（SARIMA）模型在预测手足口病方面的准确性。使用了中国淮南2009年至2014年的天气和手足口病数据。采用泊松回归模型结合分布滞后非线性模型（DLNM）来研究天气因素与手足口病之间的关系。通过使用SARIMA模型建立手足口病预测模型。结果表明，气温升高与手足口病风险升高显著相关。然而，未观察到相对湿度、气压和降雨量与手足口病之间存在相关性。包含温度变量的SARIMA模型对手足病数据的拟合效果优于不包含该变量的模型（sR增加，而BIC降低），并且SARIMA(0, 1, 1)(0, 1, 0)对手足病数据的拟合效果最佳。此外，与女性和托幼儿童相比，男性和散居儿童可能更适合使用SARIMA模型来预测手足口病病例数，且该模型具有较高的精度。总之，高温会增加感染手足口病的风险。包含温度变量的SARIMA模型可以有效提高其预测准确性，可为政策制定者和公共卫生部门构建最优拟合模型及优化手足口病预防提供有价值的信息。
