title: Using Combined Diagnostic Test Results to Hindcast Trends of Infection from Cross-Sectional Data.
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.
authors.affiliation: Biomathematics and Statistics Scotland (BIOSS), Edinburgh, United Kingdom.|SRUC, Edinburgh, United Kingdom.|Environment Department, University of York, York, United Kingdom.
authors.forename: Gustaf
authors.initials: G
authors.lastname: Rydevik
authors.affiliation: Biomathematics and Statistics Scotland (BIOSS), Edinburgh, United Kingdom.
authors.forename: Giles T
authors.initials: GT
authors.lastname: Innocent
authors.affiliation: Biomathematics and Statistics Scotland (BIOSS), Edinburgh, United Kingdom.
authors.forename: Glenn
authors.initials: G
authors.lastname: Marion
authors.affiliation: SRUC, Edinburgh, United Kingdom.
authors.forename: Ross S
authors.identifier: 0000-0003-1468-5867
authors.initials: RS
authors.lastname: Davidson
authors.affiliation: Environment Department, University of York, York, United Kingdom.
authors.forename: Piran C L
authors.initials: PC
authors.lastname: White
authors.affiliation: Laboratory of Microbiology and Parasitology, Faculty of Veterinary Medicine, University of Thessaly, Karditsa, Greece.|Department of Biomedicine, Institute for Research and Technology of Thessaly, Larissa, Greece.
authors.forename: Charalambos
authors.initials: C
authors.lastname: Billinis
authors.affiliation: School of Veterinary Medicine and Science, University of Nottingham, Nottingham, United Kingdom.
authors.forename: Paul
authors.initials: P
authors.lastname: Barrow
authors.affiliation: The Vector-Borne Viral Diseases Programme, The Pirbright Institute, Surrey, United Kingdom.
authors.forename: Peter P C
authors.initials: PP
authors.lastname: Mertens
authors.affiliation: National Veterinary Institute (SVA), Uppsala, Sweden.
authors.forename: Dolores
authors.initials: D
authors.lastname: Gavier-Widén
authors.affiliation: SRUC, Edinburgh, United Kingdom.
authors.forename: Michael R
authors.initials: MR
authors.lastname: Hutchings
mesh_terms: D000465:Algorithms; D000818:Animals; D001819:Bluetongue; D002417:Cattle; D002418:Cattle Diseases; D019295:Computational Biology / Q000379:methods*; D003430:Cross-Sectional Studies; D058872:Epidemics / Q000517:prevention & control / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D014917:Whooping Cough