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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22050

Title: Estimating Time of Infection Using Prior Serological and Individual Information Can Greatly Improve Incidence Estimation of Human and Wildlife Infections
Authors: Borremans, Benny
Hens, Niel
Beutels, Philippe
Leirs, Herwig
Reijniers, Jonas
Issue Date: 2016
Citation: PLOS COMPUTATIONAL BIOLOGY, 12(5) (Art N° e1004882)
Abstract: Diseases of humans and wildlife are typically tracked and studied through incidence, the number of new infections per time unit. Estimating incidence is not without difficulties, as asymptomatic infections, low sampling intervals and low sample sizes can introduce large estimation errors. After infection, biomarkers such as antibodies or pathogens often change predictably over time, and this temporal pattern can contain information about the time since infection that could improve incidence estimation. Antibody level and avidity have been used to estimate time since infection and to recreate incidence, but the errors on these estimates using currently existing methods are generally large. Using a semi-parametric model in a Bayesian framework, we introduce a method that allows the use of multiple sources of information (such as antibody level, pathogen presence in different organs, individual age, season) for estimating individual time since infection. When sufficient background data are available, this method can greatly improve incidence estimation, which we show using are-navirus infection in multimammate mice as a test case. The method performs well, especially compared to the situation in which seroconversion events between sampling sessions are the main data source. The possibility to implement several sources of information allows the use of data that are in many cases already available, which means that existing incidence data can be improved without the need for additional sampling efforts or laboratory assays.
Notes: [Borremans, Benny; Leirs, Herwig; Reijniers, Jonas] Univ Antwerp, Evolutionary Ecol Grp, Antwerp, Belgium. [Hens, Niel; Beutels, Philippe] Univ Antwerp, Vaccine & Infect Dis Inst VAXINFECTIO, Ctr Hlth Econ Res & Modelling Infect Dis CHERMID, Antwerp, Belgium. [Hens, Niel] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat I BIOST, Diepenbeek, Belgium. [Reijniers, Jonas] Univ Antwerp, Dept Engn Management, Antwerp, Belgium.
URI: http://hdl.handle.net/1942/22050
DOI: 10.1371/journal.pcbi.1004882
ISI #: 000379348100008
ISSN: 1553-734X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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