【摘要】 目的　采用多维可视化分析研究北京市2003年严重急性呼吸综合征(SARS)疫情的扩散过程。方法　以北京市SARS疫情数据,结合北京市地理信息系统,基于数据驱动和模型驱动的理论和技术,利用热点分析、空间过程分析和因子识别等数据探索分析方法,根据传染病的多维传播特性,同时利用遗传规划和模拟退火相结合的算法对易感 感染 移出(SIR)模型求解,从模型直接求取SARS流行病学参数。结果　SARS密切接触者在城市内部呈现出大尺度上沿交通线聚集和在小尺度上随机分布态势;在不同发展阶段,北京市SARS发病趋势存在显著的空间聚集和扩散变化特征;地理位置、人口以及医院和医生数量是SARS空间传播的重要影响因子;通过对 SIR模型直接求解,反演传染病参数,对SARS确诊病例数可进行早期预报性分析。结论　对 SARS密切接触者的集聚探测揭示了北京市人群存在两个空间尺度上的流动接触过程,为人 人接触性传染病的预防和控制策略制定提供重要依据;多个环境和人文因子对 SARS传播起作用,其统计显著性随时间变化;有效的算法可以对SIR直接求解,使其可以用于传染病参数反演和早期预测。更多还原

【Abstract】 Objective To analyse the multi-dimension nature of severe acute respiratory syndrome (SARS) transmission. Methods Based on the data of SARS in 2003 and the geographic information system of Beijing, as well as under the broad range of the theorems and techniques of data-driven and model-driven knowledge mining, hierarchical techniques were used to test the hot spots. Wavelet technique was also used to decompose Moran’s I frequency to survey the spatial clustering process of SARS. For factors analysis, BW test was used to distinguish factors which influencing SARS process. In temporal aspects, susceptive-infective-removal model(SIR) without Taylor expansion was solved by a genetic-simulated annealing algorithm, that directly provided a new approach to obtain epidemic parameters from the SIR model. Results Different order of spatial hot spots were noticed and the clustering were relevant with the means of transportation. Diffusion dynamics were changed along with the temporal process of SARS. Regarding factor analysis, geographic relationship, population density, the amount of doctors and hospitals appeared to be the key elements influencing the transmission of SARS. The predictable number of SARS cases evolving with time were also calculated. Conclusions Cluster detection of close contacts of SARS infective in Beijing revealed the spatial characters of urban population flow and having important implications in the prevention and control of this communicable diseases. Some human and physical environment factors played statistical significant roles in different periods during SARS epidemics. An efficient algorithm was developed to solve SIR model directly, enabling the estimation of epidemic parameters from SIR and early forecast.更多还原