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Application of kernel smoothing to estimate the spatio-temporal variation in risk of STEC O157 in England

Lookup NU author(s): Professor Sarah O'Brien



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Identifying geographical areas with significantly higher or lower rates of infectious diseases can provide important aetiological clues to inform the development of public health policy and interventions designed to reduce morbidity. We applied kernel smoothing to estimate the spatial and spatio-temporal variation in risk of STEC O157 infection in England between 2009 and 2015, and to explore differences between the residential locations of cases reporting travel and those not reporting travel. We provide evidence that the distribution of STEC O157 infection in England is non-uniform with respect to the distribution of the at-risk population; that the spatial distribution of the three main genetic lineages infecting humans (I, II and I/II) differs significantly and that the spatio-temporal risk is highly dynamic. Our results also indicate that cases of STEC O157 reporting travel within or outside the UK are more likely to live in the south/south-east of the country, meaning that their residential location may not reflect the location of exposure that led to their infection. We suggest that the observed variation in risk reflects exposure to sources of STEC O157 that are geographically prescribed. These differences may be related to a combination of changes in the strains circulating in the ruminant reservoir, animal movements (livestock, birds or wildlife) or the behavior of individuals prior to infection. Further work to identify the importance of behaviours and exposures reported by cases relative to residential location is needed.

Publication metadata

Author(s): Elson R, Davies TM, Jenkins C, Vivancos R, O'Brien SJ, Lake IR

Publication type: Article

Publication status: Published

Journal: Spatial and Spatio-temporal Epidemiology

Year: 2020

Volume: 32

Print publication date: 01/02/2020

Online publication date: 24/10/2019

Acceptance date: 16/09/2019

Date deposited: 30/04/2021

ISSN (electronic): 1877-5845

Publisher: Elsevier


DOI: 10.1016/j.sste.2019.100305

PubMed id: 32007279


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