Using combined diagnostic test results to hindcast trends of infection from cross-sectional data

Rydevik, Gustaf and Innocent, Giles T. and Marion, Glenn and Davidson, Ross S. and White, Piran C.L. and Billinis, Charalambos and Barrow, Paul and Mertens, Peter P.C. and Gavier-Widén, Dolores and Hutchings, Michael R. (2016) Using combined diagnostic test results to hindcast trends of infection from cross-sectional data. PLoS Computational Biology, 12 (7). e1004901/1-e1004901/19. ISSN 1553-7358

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Abstract

Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health. Following the detection of a disease outbreak, a response in proportion to the severity of the outbreak is required. It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory. However, there is often a lack of situational awareness that may lead to over- or under-reaction. There is a widening range of tests available for detecting pathogens, with typically different temporal characteristics, e.g. in terms of when peak test response occurs relative to time of exposure. We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to ‘hindcast’ (infer the historical trend of) an infectious disease epidemic. Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate time of exposure, and the overall epidemic trend in the population prior to the time of sampling. We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends. We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle, and a whooping cough outbreak in humans. Together, these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends, and can be used to distinguish whether an outbreak is increasing or past its peak. We conclude that if temporal characteristics of diagnostics are known, it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time.

Item Type: Article
Schools/Departments: University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Veterinary Medicine and Science
Identification Number: 10.1371/journal.pcbi.1004901
Depositing User: Eprints, Support
Date Deposited: 22 Jun 2017 12:51
Last Modified: 12 Oct 2017 23:59
URI: http://eprints.nottingham.ac.uk/id/eprint/43720

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