Improving Dengue Virus Infection Detection with Serological Assays
Author Information
Author(s): Hamins-Puértolas Marco, Buddhari Darunee, Salje Henrik, Huang Angkana T., Hunsawong Taweewun, Cummings Derek A.T., Fernandez Stefan, Farmer Aaron, Kaewhiran Surachai, Khampaen Direk, Srikiatkhachorn Anon, Iamsirithaworn Sopon, Waickman Adam, Thomas Stephen J., Endy Timothy, Rothman Alan L., Anderson Kathryn B., Rodriguez-Barraquer Isabel
Primary Institution: Cold Spring Harbor Laboratory
Hypothesis
Can a Bayesian mixture model improve the accuracy of dengue virus infection detection using multiple serological assays?
Conclusion
The new model improves the accuracy of dengue virus infection detection compared to standard methods.
Supporting Evidence
- The model was tested with simulated data before applying it to real-world samples.
- The study included data from 232 RT-PCR confirmed infections as a gold standard.
- The new model achieved 87-90% F1 scores compared to 82-84% for standard methods.
Takeaway
This study shows that using more blood tests together can help doctors better understand if someone has dengue fever.
Methodology
A Bayesian mixture model was developed and tested using simulated data and applied to paired serum samples from two longitudinal cohort studies.
Participant Demographics
Data collected from two longitudinal cohort studies in Kamphaeng Phet, Thailand.
Digital Object Identifier (DOI)
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