Journal article
An individual level infectious disease model in the presence of uncertainty from multiple, imperfect diagnostic tests
Biometrics, Vol.79(4), pp.426-436
03/2023
DOI: 10.1111/biom.13579
PMID: 34636415
Abstract
Bayesian compartmental infectious disease models yield important inference on disease transmission by appropriately accounting for the dynamics and uncertainty of infection processes. In addition to estimating transition probabilities and reproductive numbers, these statistical models allow researchers to assess the probability of disease risk and quantify the effectiveness of interventions. These infectious disease models rely on data collected from all individuals classified as positive based on various diagnostic tests. In infectious disease testing, however, such procedures produce both false-positives and false-negatives at varying rates depending on the sensitivity and specificity of the diagnostic tests being used. We propose a novel Bayesian spatio-temporal infectious disease modeling framework that accounts for the additional uncertainty in the diagnostic testing and classification process that provides estimates of the important transmission dynamics of interest to researchers. The method is applied to data on the 2006 mumps epidemic in Iowa, in which over 6,000 suspected mumps cases were tested using a buccal or oral swab specimen, a urine specimen, and/or a blood specimen. While all procedures are believed to have high specificities, the sensitivities can be low and vary depending on the timing of the test as well as the vaccination status of the individual being tested. This article is protected by copyright. All rights reserved.
Keywords: bayesian; compartmental model; diagnostic uncertainty; infectious disease modeling; reversible-jump MCMC.
This article is protected by copyright. All rights reserved.
Details
- Title: Subtitle
- An individual level infectious disease model in the presence of uncertainty from multiple, imperfect diagnostic tests
- Creators
- Caitlin Ward - University of Iowa, NursingGrant D Brown - University of Iowa, BiostatisticsJacob J Oleson - University of Iowa, Biostatistics
- Resource Type
- Journal article
- Publication Details
- Biometrics, Vol.79(4), pp.426-436
- DOI
- 10.1111/biom.13579
- PMID
- 34636415
- NLM abbreviation
- Biometrics
- eISSN
- 1541-0420
- Grant note
- DOI: 10.13039/100000061, name: Fogarty International Center, award: R01TW010500
- Language
- English
- Electronic publication date
- 10/12/2021
- Date published
- 03/2023
- Academic Unit
- Biostatistics; Nursing
- Record Identifier
- 9984186764702771
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