Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models.
<h4>Background</h4>Dengue virus (DENV) infection is a global health concern of increasing magnitude. To target intervention strategies, accurate estimates of the force of infection (FOI) are necessary. Catalytic models have been widely used to estimate DENV FOI and rely on a binary class...
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| Language: | English |
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Public Library of Science (PLoS)
2022-07-01
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| Series: | PLoS Neglected Tropical Diseases |
| Online Access: | https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0010592&type=printable |
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| author | Victoria Cox Megan O'Driscoll Natsuko Imai Ari Prayitno Sri Rezeki Hadinegoro Anne-Frieda Taurel Laurent Coudeville Ilaria Dorigatti |
| author_facet | Victoria Cox Megan O'Driscoll Natsuko Imai Ari Prayitno Sri Rezeki Hadinegoro Anne-Frieda Taurel Laurent Coudeville Ilaria Dorigatti |
| author_sort | Victoria Cox |
| collection | DOAJ |
| description | <h4>Background</h4>Dengue virus (DENV) infection is a global health concern of increasing magnitude. To target intervention strategies, accurate estimates of the force of infection (FOI) are necessary. Catalytic models have been widely used to estimate DENV FOI and rely on a binary classification of serostatus as seropositive or seronegative, according to pre-defined antibody thresholds. Previous work has demonstrated the use of thresholds can cause serostatus misclassification and biased estimates. In contrast, mixture models do not rely on thresholds and use the full distribution of antibody titres. To date, there has been limited application of mixture models to estimate DENV FOI.<h4>Methods</h4>We compare the application of mixture models and time-constant and time-varying catalytic models to simulated data and to serological data collected in Vietnam from 2004 to 2009 (N ≥ 2178) and Indonesia in 2014 (N = 3194).<h4>Results</h4>The simulation study showed larger mean FOI estimate bias from the time-constant and time-varying catalytic models (-0.007 (95% Confidence Interval (CI): -0.069, 0.029) and -0.006 (95% CI -0.095, 0.043)) than from the mixture model (0.001 (95% CI -0.036, 0.065)). Coverage of the true FOI was > 95% for estimates from both the time-varying catalytic and mixture model, however the latter had reduced uncertainty. When applied to real data from Vietnam, the mixture model frequently produced higher FOI and seroprevalence estimates than the catalytic models.<h4>Conclusions</h4>Our results suggest mixture models represent valid, potentially less biased, alternatives to catalytic models, which could be particularly useful when estimating FOI from data with largely overlapping antibody titre distributions. |
| format | Article |
| id | doaj-art-1140e54e0f7041fbb3fc593fdab0542d |
| institution | Kabale University |
| issn | 1935-2727 1935-2735 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Neglected Tropical Diseases |
| spelling | doaj-art-1140e54e0f7041fbb3fc593fdab0542d2025-08-20T03:25:16ZengPublic Library of Science (PLoS)PLoS Neglected Tropical Diseases1935-27271935-27352022-07-01167e001059210.1371/journal.pntd.0010592Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models.Victoria CoxMegan O'DriscollNatsuko ImaiAri PrayitnoSri Rezeki HadinegoroAnne-Frieda TaurelLaurent CoudevilleIlaria Dorigatti<h4>Background</h4>Dengue virus (DENV) infection is a global health concern of increasing magnitude. To target intervention strategies, accurate estimates of the force of infection (FOI) are necessary. Catalytic models have been widely used to estimate DENV FOI and rely on a binary classification of serostatus as seropositive or seronegative, according to pre-defined antibody thresholds. Previous work has demonstrated the use of thresholds can cause serostatus misclassification and biased estimates. In contrast, mixture models do not rely on thresholds and use the full distribution of antibody titres. To date, there has been limited application of mixture models to estimate DENV FOI.<h4>Methods</h4>We compare the application of mixture models and time-constant and time-varying catalytic models to simulated data and to serological data collected in Vietnam from 2004 to 2009 (N ≥ 2178) and Indonesia in 2014 (N = 3194).<h4>Results</h4>The simulation study showed larger mean FOI estimate bias from the time-constant and time-varying catalytic models (-0.007 (95% Confidence Interval (CI): -0.069, 0.029) and -0.006 (95% CI -0.095, 0.043)) than from the mixture model (0.001 (95% CI -0.036, 0.065)). Coverage of the true FOI was > 95% for estimates from both the time-varying catalytic and mixture model, however the latter had reduced uncertainty. When applied to real data from Vietnam, the mixture model frequently produced higher FOI and seroprevalence estimates than the catalytic models.<h4>Conclusions</h4>Our results suggest mixture models represent valid, potentially less biased, alternatives to catalytic models, which could be particularly useful when estimating FOI from data with largely overlapping antibody titre distributions.https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0010592&type=printable |
| spellingShingle | Victoria Cox Megan O'Driscoll Natsuko Imai Ari Prayitno Sri Rezeki Hadinegoro Anne-Frieda Taurel Laurent Coudeville Ilaria Dorigatti Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models. PLoS Neglected Tropical Diseases |
| title | Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models. |
| title_full | Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models. |
| title_fullStr | Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models. |
| title_full_unstemmed | Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models. |
| title_short | Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models. |
| title_sort | estimating dengue transmission intensity from serological data a comparative analysis using mixture and catalytic models |
| url | https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0010592&type=printable |
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