Imperial College London
Portrait Picture

Dr Natsuko Imai

Faculty of Medicine, School of Public Health

Honorary Senior Research Fellow
 
 
 
//

Contact

 

n.imai Website

 
 
//

Location

 

G26Medical SchoolSt Mary's Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Cox:2022:10.1371/journal.pntd.0010592,
author = {Cox, V and O'Driscoll, M and Imai, N and Prayitno, A and Hadinegoro, SR and Taurel, A-F and Coudeville, L and Dorigatti, I},
doi = {10.1371/journal.pntd.0010592},
journal = {PLoS Neglected Tropical Diseases},
pages = {e0010592--e0010592},
title = {Estimating dengue transmission intensity from serological data: a comparative analysis using mixture and catalytic models.},
url = {http://dx.doi.org/10.1371/journal.pntd.0010592},
volume = {16},
year = {2022}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BACKGROUND: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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.
AU  - Cox,V
AU  - O'Driscoll,M
AU  - Imai,N
AU  - Prayitno,A
AU  - Hadinegoro,SR
AU  - Taurel,A-F
AU  - Coudeville,L
AU  - Dorigatti,I
DO  - 10.1371/journal.pntd.0010592
EP  - 0010592
PY  - 2022///
SN  - 1935-2727
SP  - 0010592
TI  - Estimating dengue transmission intensity from serological data: a comparative analysis using mixture and catalytic models.
T2  - PLoS Neglected Tropical Diseases
UR  - http://dx.doi.org/10.1371/journal.pntd.0010592
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35816508
UR  - https://journals.plos.org/plosntds/s/accepted-manuscripts#loc-early-version
UR  - http://hdl.handle.net/10044/1/98303
VL  - 16
ER  -
Download