Title
Estimating time of infection using prior serological and individual information can greatly improve incidence estimation of human and wildlife infections Estimating time of infection using prior serological and individual information can greatly improve incidence estimation of human and wildlife infections
Author
Faculty/Department
Faculty of Applied Economics
Faculty of Sciences. Biology
Faculty of Medicine and Health Sciences
Publication type
article
Publication
San Francisco, Calif. ,
Subject
Mathematics
Chemistry
Biology
Computer. Automation
Source (journal)
PLoS computational biology. - San Francisco, Calif.
Volume/pages
12(2016) :5 , 18 p.
ISSN
1553-734X
1553-734X
Article Reference
e1004882
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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 arenavirus 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.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/a6e76d/133450.pdf
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