Using Combined Diagnostic Test Results to Hindcast Trends of Infection from Cross-Sectional Data

by Gustaf Rydevik, Giles T. Innocent, Glenn Marion, Ross S. Davidson, Piran C. L. White, Charalambos Billinis, Paul Barrow, Peter P. C. Mertens, Dolores Gavier-Widén, Michael R. Hutchings 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 ...
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research