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01.12.2018 | Research article | Ausgabe 1/2018 Open Access

BMC Public Health 1/2018

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

Zeitschrift:
BMC Public Health > Ausgabe 1/2018
Autoren:
Mark P. C. Cherrie, Gordon Nichols, Gianni Lo Iacono, Christophe Sarran, Shakoor Hajat, Lora E. Fleming
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12889-018-5931-6) contains supplementary material, which is available to authorized users.

Abstract

Background

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.

Methods

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.

Results

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).

Conclusions

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.
Zusatzmaterial
Additional file 1: Figure S1. Time series plots of meteorological variables. (PNG 314 kb)
12889_2018_5931_MOESM1_ESM.png
Additional file 2: Table S1. Weekly, monthly and quarterly breakdown of pathogen seasonality. (CSV 34 kb)
12889_2018_5931_MOESM2_ESM.csv
Additional file 3: Figure S2. Seasonal pathogen cross correlations with meteorological variables. (PNG 397 kb)
12889_2018_5931_MOESM3_ESM.png
Additional file 4: Table S2. Mean cross correlations for weather Groups. (CSV 346 bytes)
12889_2018_5931_MOESM4_ESM.csv
Additional file 5: Figure S3. Max correlation with meteorological variable by weather cluster group. (PNG 366 kb)
12889_2018_5931_MOESM5_ESM.png
Literatur
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