Simulated serological data using serosim and subsequent results using the full mixture model on (a) HAI, (b) IgG and (c) IgM data with additional observational noise.

Simulated serological data using serosim and subsequent results using the full mixture model on (a) HAI, (b) IgG and (c) IgM data with additional observational noise.

<p>For each we present the predicted infection status using the mixture model approach along with the true underlying infection status. Parameters chosen for forward simulation are presented in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013708#pcbi.1013...

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Detaylı Bibliyografya
Yazar:	Marco Hamins-Puértolas (15238661) (author)
Diğer Yazarlar:	Darunee Buddhari (625855) (author), Henrik Salje (589157) (author), Angkana T. Huang (12262139) (author), Taweewun Hunsawong (798675) (author), Derek A.T. Cummings (14971341) (author), Stefan Fernandez (146590) (author), Aaron Farmer (4288318) (author), Surachai Kaewhiran (22683535) (author), Direk Khampaen (22683538) (author), Anon Srikiatkhachorn (110184) (author), Sopon Iamsirithaworn (260152) (author), Adam Waickman (118668) (author), Stephen J. Thomas (7105673) (author), Timothy Endy (150971) (author), Alan L. Rothman (6835178) (author), Kathryn B. Anderson (6835166) (author), Isabel Rodriguez-Barraquer (233670) (author)
Baskı/Yayın Bilgisi:	2025
Konular:	Medicine Biotechnology Ecology Immunology Cancer Science Policy Infectious Diseases Virology Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified since many cases linked immunosorbent assay independently utilize information hemagglutination inhibition assay globe &# 8217 currently implemented cut convalescent serum collected 87 &# 8211 82 &# 8211 real world data multiple serological assays infer infection events generate finalized interpretations bayesian mixture model prior standard interpretations pcr confirmed infections jointly model data gold standard ). f1 scores ). hai data consistently direct detection methods gold standard f1 scores infection detection eia ). including data individual assays new model igm data whether individuals transmission potential results provide recently experienced provide insight probabilistic framework population living point approaches pathogen systems kamphaeng phet interpreting results incorporating igg important role higher accuracy first test diagnostic process creating uncertainty combined metric already half 677 pairs
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Comparison between simulation based estimates for boosts in GM HAI, IgG, and IgM that is experienced after an infection event as a function of acute GM HAI, IgG, and IgM titers individuals under the varying levels of observational noise.
Yazar:: Marco Hamins-Puértolas (15238661)
Baskı/Yayın Bilgisi: (2025)

Parameters for forward simulation using serosim [38].
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Distribution of paired serological data that fall into probability buckets when using Model 9 (incorporating paired IgG, IgM, GM HAI, and acute GM HAI serological data) and the number of serological assays (RT-PCR, HAI, and EIA) that interpret the individual as having an infection.
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Baskı/Yayın Bilgisi: (2025)

Acute and convalescent samples from cohort study data for serological assays, colored by the number of serological assays (RT-PCR, HAI, and EIA) that interpret the individual as having an infection. a) Geometric mean titers of a haemagglutination inhibition assay (GM HAI) for all four serotypes of dengue virus. b) Immunoglobulin G (IgG) c) Immunoglobulin M (IgM) d) Ratio of IgM to IgG at both acute and convalescent serum samples.
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Acute and convalescent data for serological assays, colored by probability of infection found during model testing for Model 9. a) Geometric mean titers of a haemagglutination inhibition assay (GM HAI) for all four serotypes of dengue virus. b) Immunoglobulin G (IgG) c) Immunoglobulin M (IgM) d) Ratio of IgM to IgG at both acute and convalescent sera samples.
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Relationship between estimated probability of a dengue virus infection (DENV) found during model testing for Model 9 (incorporating paired IgG, IgM, GM HAI, and acute GM HAI serological data) and the number of serological assays (RT-PCR, HAI, and EIA) that interpret the individual as having an infection.
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Relative observational noise by assay and dataset.
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Comparison of each accuracy metric (sensitivity, specificity, F1, and area under the curve for reporter operator curve (AUC ROC)) on each model. Models are defined by which assays were incorporated (i.e., HAI, IgG, and/or IgM). Bolded values represent the best performing model for the infection definition and metric of interest.
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Model comparisons for simulated data with differing amounts of observational noise.
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Acute and convalescent samples from cohort study data for serological assays, colored by whether they are predicted as infections using Model 9.
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Acute and convalescent serological data from Thai cohorts used in this analyses, colored by true infections status and prediction of infection during model testing.
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Characteristics for Kamphaeng Phet cohort studies split by source (KPS1 and KFCS) as well as across the entire dataset. Information on age, hospitalization as well as serological assay results for RT-PCR, haemagglutination inhibition assay (HAI), and enzyme immunoassay (EIA) are presented.
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Theoretical relationship between time since symptom onset and titers in one infected (red) and three uninfected (blue) individuals.
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(A) Estimated intercept for titer boost when acute titers are initially low for each assay.
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