An individual level infectious disease model in the presence of uncertainty from multiple, imperfect diagnostic tests

Caitlin Ward, Grant D. Brown, Jacob J. Oleson

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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. Although 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.

Original languageEnglish (US)
Pages (from-to)426-436
Number of pages11
Issue number1
StatePublished - Mar 2023
Externally publishedYes

Bibliographical note

Funding Information:
Research reported in this publication was supported by the Fogarty International Center of the National Institutes of Health under Award Number R01TW010500. The content is solely the authors' responsibility and does not necessarily represent the official views of the National Institutes of Health. We would also like to thank Mike Pentella and the State Hygienic Lab at the University of Iowa for supplying the data. We thank the associate editor and two anonymous reviewers for their insightful comments on previous versions of the manuscript.

Publisher Copyright:
© 2021 The International Biometric Society.


  • Bayesian
  • compartmental model
  • diagnostic uncertainty
  • infectious disease modeling
  • reversible-jump MCMC

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural


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