ArticleBMC public health2018
Pathogen seasonality and links with weather in England and Wales: a big data time series analysis.
Article in BMC public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review and meta-analysis of ambient temperature and precipitation with infections from five food-borne bacterial pathogens.Epidemiology and infection · 2024Pooled it
- Occurrence of coliphage in raw wastewater and in ambient water: A meta-analysis.Water research · 2019Pooled it
- Review of published research on primary dysautonomia of domestic animals.The Veterinary record · 2026Review
- Review
- The Eat-Out-to-Help-Out incentive: A trigger for gastrointestinal infections in England, 2020?Epidemiology and infection · 2025Article
- Decoding Weather Fingerprints of Upper Respiratory Pathogens: A One-year Observational Study in Northern Greece.Current microbiology · 2025Observational
- Oral and Faecal Viromes of New Zealand Calves on Pasture With an Idiopathic Ill-Thrift Syndrome.Transboundary and emerging diseases · 2025Article
- Climate change and Vibrio vulnificus dynamics: A blueprint for infectious diseases.PLoS pathogens · 2024Review
- Analyzing the seasonality of tuberculosis case notifications in the UK, 2000-2018.Epidemiology and infection · 2024Article
- A mathematical, classical stratification modeling approach to disentangling the impact of weather on infectious diseases: A case study using spatio-temporally disaggregated Campylobacter surveillance data for England and Wales.PLoS computational biology · 2024Article
- Article
- Impact of meteorological factors on the incidence of hand-foot-mouth disease in Yangzhou from 2017 to 2022: a time series study.Frontiers in public health · 2023Article
- Climate Change and Health in Urban Areas with a Mediterranean Climate: A Conceptual Framework with a Social and Climate Justice Approach.International journal of environmental research and public health · 2022Article
- Prevalence and Genomic Diversity ofMicroorganisms · 2022Article
- Dairy Cattle Density and Temporal Patterns of Human Campylobacteriosis and Cryptosporidiosis in New Zealand.EcoHealth · 2022Article
- Optimizing Crop Planting Schedule Considering Planting Window and Storage Capacity.Frontiers in plant science · 2022Article
- Epidemiological trend in scarlet fever incidence in China during the COVID-19 pandemic: A time series analysis.Frontiers in public health · 2022Article
- Article
- Coronavirus seasonality, respiratory infections and weather.BMC infectious diseases · 2021Article
- Climate change and infectious disease in Europe: Impact, projection and adaptation.The Lancet regional health. Europe · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundMany 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.
methodsData 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.
resultsExamination 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).
conclusionsThe 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.
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