时间序列分析在手足口病流行预测中的应用研究

Numerous large outbreaks of HFMD have occurred in China since 2008, which have caused death and neurological sequelae, and have become a growing public health threat. Currently, neither vaccine nor effective drug against enterovirus is available for human use. Thus, epidemiological surveillance of HFMD and its pathogens is important to take the proper and timely public health interventions to prevent its outbreaks. Early warning of HFMD outbreaks could improve the efficiency of vector control campaigns and help to take prevention actions to delay the epidemic, thus reducing its impact on health system. HFMD morbidity and mortality would be minimized through earlier and efficient public health response. Many studies have indicated that meteorological conditions are the most important factors of HFMD outbreaks. Many mathematical models have been developed to predict the occurrence of outbreaks using a combined environmental approach. Seasonal Autoregressive Integrated Moving Average (SARIMA) models allow the integration of external factors, such as climatic variables, to increase their predictive power. In this study, we proposed to develop Seasonal Autoregressive Integrated Moving Average (SARIMA) models using time series analysis of the number of laboratory-confirmed HFMD hospitalizations. The goals of this study were to characterize whether climatic factors are associated with HFMD epidemics among children and whether inclusion of such factors is useful to predict epidemics with higher precision. This predicable model would be used to facilitate efficient HFMD control.

2008年以来，我国进入手足口病流行期，手足口病患者及重症、死亡病例逐年升高，防控形势十分严峻。对于手足口病的治疗，目前仍没有确切有效的抗病毒药物，且没有安全有效的疫苗应用于人群。建立高度敏感的监测系统，对疾病的爆发流行进行预测，是手足口病防治的关键。手足口病的流行有明显的季节差异，温度和湿度对手足口病发病有重要影响。时间序列法特别适用于时序规律不明显，或有明显季节性和周期性的情况，季节性自回归移动平均模型可引入自变量提高模型的预测精确度，其过程简便、经济、预测精度较高。本项目拟采用时间序列方法，分析气候因素对手足口病流行的影响，引入自变量（气候变量）建立手足口病季节性自回归移动平均模型，研究结果将为手足口病的预测和对制定预防和控制手足口病的长远或近期应对策略有重要意义。