RT Journal Article
SR Electronic
T1 A modelling framework for the prediction of the herd-level probability of infection from longitudinal data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.07.10.197426
DO 10.1101/2020.07.10.197426
A1 Madouasse, Aurélien
A1 Mercat, Mathilde
A1 van Roon, Annika
A1 Graham, David
A1 Guelbenzu, Maria
A1 Berends, Inge Santman
A1 van Schaik, Gerdien
A1 Nielen, Mirjam
A1 Frössling, Jenny
A1 Ågren, Estelle
A1 Humphry, Roger
A1 Eze, Jude
A1 Gunn, George
A1 Gethmann, Jörn
A1 More, Simon J.
A1 Fourichon, Christine
YR 2020
UL http://biorxiv.org/content/early/2020/07/12/2020.07.10.197426.abstract
AB For many infectious diseases of farm animals, there exist collective control programmes (CPs) that rely on the application of diagnostic testing at regular time intervals for the identification of infected animals or herds. The diversity of these CPs complicates the trade of animals between regions or countries because the definition of freedom from infection differs from one CP to another. In this paper, we describe a statistical model for the prediction of herd level probabilities of infection from longitudinal data collected as part of CPs against infectious diseases of cattle. The model was applied to data collected as part of a CP against infections by the bovine viral diarrhoea virus (BVDV) in Loire-Atlantique, France. The model represents infection as a herd latent status with a monthly dynamics. This latent status determines test results through test sensitivity and test specificity. The probability of becoming status positive between consecutive months is modelled as a function of risk factors (when available) using logistic regression. Modelling is performed in a Bayesian framework. Prior distributions need to be provided for the sensitivities and specificities of the different tests used, for the probability of remaining status positive between months as well as for the probability of becoming positive between months. When risk factors are available, prior distributions need to be provided for the coefficients of the logistic regression in place of the prior for the probability of becoming positive. From these prior distributions and from the longitudinal data, the model returns posterior probability distributions for being status positive in all herds on the current months. Data from the previous months are used for parameter estimation. The impact of using different prior distributions and model settings on parameter estimation was evaluated using the data. The main advantage of this model is its ability to predict a probability of being status positive on a month from inputs that can vary in terms of nature of test, frequency of testing and risk factor availability. The main challenge in applying the model to the BVDV CP data was in identifying prior distributions, especially for test characteristics, that corresponded to the latent status of interest, i.e. herds with at least one persistently infected (Pl) animal. The model is available on Github as an R package (https://github.com/AurMad/STOCfree).Competing Interest StatementThe authors have declared no competing interest.