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Pathogen seasonality and links with weather in England and Wales: A big data time series analysis

Cherrie, Mark P. C., Nichols, Gordon, Lo Iacono, Gianni, Sarran, Christophe, Hajat, Shakoor and Fleming, Lora E. (2018) Pathogen seasonality and links with weather in England and Wales: A big data time series analysis BMC Public Health, 18, 1067.

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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 1989-2014 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, >1mm, >10mm).


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.

Item Type: Article
Divisions : Faculty of Health and Medical Sciences > School of Veterinary Medicine
Authors :
Cherrie, Mark P. C.
Nichols, Gordon
Lo Iacono,
Sarran, Christophe
Hajat, Shakoor
Fleming, Lora E.
Date : 28 August 2018
Funders : Medical Research Council (MRC), Natural Environment Research Council (NERC)
DOI : 10.1186/s12889-018-5931-6
Grant Title : The MEDMI Project
Copyright Disclaimer : © The Author(s). 2018. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.
Uncontrolled Keywords : Epidemiology; Laboratory surveillance; Statistics; Pathogen; Weather; Time-series; Salmonella
Related URLs :
Depositing User : Clive Harris
Date Deposited : 28 Aug 2018 12:33
Last Modified : 11 Dec 2018 11:24

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