Pathogen seasonality and links with weather in England and Wales: a big data time series analysis.
Pathogen seasonality and links with weather in England and Wales: a big data time series analysis.
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DOI：
10.1186/s12889-018-5931-6
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发表时间：
2018-08-28
影响因子：
4.5
通讯作者：
Fleming LE
中科院分区：
文献类型：
--
作者：
Cherrie MPC;Nichols G;Iacono GL;Sarran C;Hajat S;Fleming LE
许多对公共卫生具有重要意义的传染病的发病率呈现年度季节性模式。我们的目标是系统地记录英格兰和威尔士几种人类传染病病原体的季节性，突出那些对天气敏感的生物，因此可能会受到未来气候变化的影响。1989年至2014年英格兰和威尔士的感染数据摘自英格兰公共卫生（PHE）SGSS监测数据库。我们对277种病原体血清型进行了每周、每月和每季度的时间序列分析。每个生物体的时间序列预测使用R中的TBATS包，使用模型拟合统计检测季节性。2001-2011年期间，气象卫星平台上托管的气象数据是以每月分辨率提取的。然后根据与天气变量的互相关系数，通过K均值将生物体聚类为两组。对1290万次感染事件的检查发现，91/277（33%）种微生物血清型中存在季节性成分。沙门氏菌有季节性和非季节性血清型。这些结果在一个在线Rshiny应用程序中可视化。然后根据季节性生物与天气的相关性将其分为两组。第1组与最高、平均、最低气温、日照和水汽压呈正相关，与平均风速、相对湿度、地面霜和空中霜呈负相关。第2组与降雨量（mm、> 1 mm、> 10 mm）呈负相关，但也有轻微的正相关。检测病原体时间序列数据中的季节性和确定相关的天气预报因子可以改善预测和公共卫生规划。大数据分析和在线可视化可以澄清病原体发病率和天气模式之间的关系。本文的在线版本（10.1186/s12889-018-5931-6）包含补充材料，可供授权用户使用。
Many infectious diseases of public health importance display annual seasonal patterns in their incidence. We aimed to systematically document the seasonality of several human infectious disease pathogens in England and Wales, highlighting those organisms that appear weather-sensitive and therefore may be influenced by climate change in the future. Data on infections in England and Wales from 1989 to 2014 were extracted from the Public Health England (PHE) SGSS surveillance database. We conducted a weekly, monthly and quarterly time series analysis of 277 pathogen serotypes. Each organism’s time series was forecasted using the TBATS package in R, with seasonality detected using model fit statistics. Meteorological data hosted on the MEDMI Platform were extracted at a monthly resolution for 2001–2011. The organisms were then clustered by K-means into two groups based on cross correlation coefficients with the weather variables. Examination of 12.9 million infection episodes found seasonal components in 91/277 (33%) organism serotypes. Salmonella showed seasonal and non-seasonal serotypes. These results were visualised in an online Rshiny application. Seasonal organisms were then clustered into two groups based on their correlations with weather. Group 1 had positive correlations with temperature (max, mean and min), sunshine and vapour pressure and inverse correlations with mean wind speed, relative humidity, ground frost and air frost. Group 2 had the opposite but also slight positive correlations with rainfall (mm, > 1 mm, > 10 mm). The detection of seasonality in pathogen time series data and the identification of relevant weather predictors can improve forecasting and public health planning. Big data analytics and online visualisation allow the relationship between pathogen incidence and weather patterns to be clarified. The online version of this article (10.1186/s12889-018-5931-6) contains supplementary material, which is available to authorized users.
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