title: Strategies for controlling non-transmissible infection outbreaks using a large human movement data set.
abstract: Prediction and control of the spread of infectious disease in human populations benefits greatly from our growing capacity to quantify human movement behavior. Here we develop a mathematical model for non-transmissible infections contracted from a localized environmental source, informed by a detailed description of movement patterns of the population of Great Britain. The model is applied to outbreaks of Legionnaires' disease, a potentially life-threatening form of pneumonia caused by the bacteria Legionella pneumophilia. We use case-report data from three recent outbreaks that have occurred in Great Britain where the source has already been identified by public health agencies. We first demonstrate that the amount of individual-level heterogeneity incorporated in the movement data greatly influences our ability to predict the source location. The most accurate predictions were obtained using reported travel histories to describe movements of infected individuals, but using detailed simulation models to estimate movement patterns offers an effective fast alternative. Secondly, once the source is identified, we show that our model can be used to accurately determine the population likely to have been exposed to the pathogen, and hence predict the residential locations of infected individuals. The results give rise to an effective control strategy that can be implemented rapidly in response to an outbreak.
authors.affiliation: Department of Zoology, University of Oxford, Oxford, United Kingdom; School of Life Sciences, University of Warwick, Coventry, United Kingdom.
authors.forename: Penelope A
authors.initials: PA
authors.lastname: Hancock
authors.affiliation: Field Epidemiology Service (Birmingham), Public Health England, West Midlands, United Kingdom.
authors.forename: Yasmin
authors.initials: Y
authors.lastname: Rehman
authors.affiliation: Emergency Response Unit, Public Health England, Porton Down, United Kingdom.
authors.forename: Ian M
authors.initials: IM
authors.lastname: Hall
authors.affiliation: Field Epidemiology Service (Birmingham), Public Health England, West Midlands, United Kingdom.
authors.forename: Obaghe
authors.initials: O
authors.lastname: Edeghere
authors.affiliation: School of Mathematics, Queen Mary University of London, London, United Kingdom; Warwick Mathematics Institute, University of Warwick, Coventry, United Kingdom.
authors.forename: Leon
authors.initials: L
authors.lastname: Danon
authors.affiliation: Warwick Mathematics Institute, University of Warwick, Coventry, United Kingdom.
authors.forename: Thomas A
authors.initials: TA
authors.lastname: House
authors.affiliation: School of Life Sciences, University of Warwick, Coventry, United Kingdom; Warwick Mathematics Institute, University of Warwick, Coventry, United Kingdom.
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Keeling
mesh_terms: D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D007877:Legionnaires' Disease / Q000453:epidemiology*; D008297:Male; D008962:Models, Theoretical; D011159:Population Surveillance / Q000379:methods*; D006113:United Kingdom / Q000453:epidemiology