Estimating seroconversion rates accounting for repeated infections by approximate Bayesian computation.
approximate Bayesian computation
empirical distribution function
reinfection
seroincidence
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
10 Dec 2023
10 Dec 2023
Historique:
revised:
01
09
2023
received:
09
01
2023
accepted:
03
09
2023
medline:
20
11
2023
pubmed:
27
9
2023
entrez:
27
9
2023
Statut:
ppublish
Résumé
This study presents a novel approach for inferring the incidence of infections by employing a quantitative model of the serum antibody response. Current methodologies often overlook the cumulative effect of an individual's infection history, making it challenging to obtain a marginal distribution for antibody concentrations. Our proposed approach leverages approximate Bayesian computation to simulate cross-sectional antibody responses and compare these to observed data, factoring in the impact of repeated infections. We then assess the empirical distribution functions of the simulated and observed antibody data utilizing Kolmogorov deviance, thereby incorporating a goodness-of-fit check. This new method not only matches the computational efficiency of preceding likelihood-based analyses but also facilitates the joint estimation of antibody noise parameters. The results affirm that the predictions generated by our within-host model closely align with the observed distributions from cross-sectional samples of a well-characterized population. Our findings mirror those of likelihood-based methodologies in scenarios of low infection pressure, such as the transmission of pertussis in Europe. However, our simulations reveal that in settings of higher infection pressure, likelihood-based approaches tend to underestimate the force of infection. Thus, our novel methodology presents significant advancements in estimating infection incidence, thereby enhancing our understanding of disease dynamics in the field of epidemiology.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5160-5188Subventions
Organisme : FIC NIH HHS
ID : K01 TW012177-01A1
Pays : United States
Organisme : FIC NIH HHS
ID : K01 TW012177-01A1
Pays : United States
Informations de copyright
© 2023 John Wiley & Sons Ltd.
Références
de Greeff SC, Teunis P, de Melker HE, et al. Two-component cluster analysis of a large serodiagnostic database for specificity of increases of IgG antibodies against pertussis toxin in paired serum samples and of absolute values in single serum samples. Clin Vaccine Immunol. 2012;19(9):1452-1456. doi:10.1128/CVI.00229-12
Konda T, Kamachi K, Iwaki M, Matsunaga Y. Distribution of pertussis antibodies among different age groups in Japan. Vaccine. 2002;20:1711-1717.
Nardone A, Pebody RG, Maple PAC, Andrews N, Gay NJ, Miller E. Sero-epidemiology of Bordetella pertussis infections in England and Wales. Vaccine. 2004;22(9-10):1314-1319. doi:10.1016/j.vaccine.2003.08.039
Peasey AE, Ruiz-Palacios GM, Quigley M, et al. Seroepidemiology and risk factors for sporadic norovirus/Mexico strain. J Infect Dis. 2004;189(11):2027-2036.
Teunis PFM, van Eijkeren JCH, Ang CW, et al. Biomarker dynamics: estimating infection rates from serological data. Stat Med. 2012;31(20):2240-2248. doi:10.1002/sim.5322
Glynn MK, Friedman CR, Gold BD, et al. Seroincidence of Helicobacter pylori infection in a cohort of rural Bolivian children: acquisition and analysis of possible risk factors. Clin Infect Dis. 2002;35(9):1059-1065. doi:10.1086/342910
Hagan H, Thiede H, Des Jarlais DC. Hepatitis C virus infection among injection drug users: survival analysis of time to seroconversion. Epidemiology. 2004;15(5):543-549.
Farrington CP. Modelling forces of infection for measles, mumps and rubella. Stat Med. 1990;9(8):953-967.
Shkedy Z, Aerts M, Molenberghs G, Beutels P, van Damme P. Modelling age-dependent force of infection from prevalence data using fractional polynomials. Stat Med. 2006;25(9):1577-1599. doi:10.1002/sim.2291
Arnold BF, van der Laan MJ, Hubbard AE, et al. Measuring changes in transmission of neglected tropical diseases, malaria, and enteric pathogens from quantitative antibody levels. PLoS Negl Trop Dis. 2017;11(5):e0005616. doi:10.1371/journal.pntd.0005616
Longini IM, Byers RH, Hessol NA, Tan WY. Estimating the stage-specific numbers of HIV infection using a Markov model and back-calculation. Stat Med. 1992;11(6):831-843. doi:10.1002/sim.4780110612
Simonsen J, Mølbak K, Falkenhorst G, Krogfelt KA, Linneberg A, Teunis PF. Estimation of incidences of infectious diseases based on antibody measurements. Stat Med. 2009;28(14):1882-1895. doi:10.1002/sim.3592
Monge S, Teunis P, Friesema I, et al. Immune response-eliciting exposure to Campylobacter vastly exceeds the incidence of clinically overt campylobacteriosis but is associated with similar risk factors: a nationwide serosurvey in the Netherlands. J Infect. 2018;77(3):171-177. doi:10.1016/j.jinf.2018.04.016
Teunis PFM, van Eijkeren JCH. Estimation of seroconversion rates for infectious diseases: effects of age and noise. Stat Med. 2020;39(21):2799-2814. doi:10.1002/sim.8578
de Graaf WF, Kretzschmar MEE, Teunis PFM, Diekmann O. A two-phase within-host model for immune response and its application to serological profiles of pertussis. Epidemics. 2014;9:1-7. doi:10.1016/j.epidem.2014.08.002
Teunis PFM, van Eijkeren JCH, de Graaf WF, Bonačić Marinović A, Kretzschmar MEE. Linking the seroresponse to infection to within-host heterogeneity in antibody production. Epidemics. 2016;16:33-39. doi:10.1016/j.epidem.2016.04.001
Wielders CCH, Teunis PFM, Hermans MHA, van der Hoek W, Schneeberger PM. Kinetics of antibody response to Coxiella burnetii infection (Q fever): estimation of the seroresponse onset from antibody levels. Epidemics. 2015;13:37-43. doi:10.1016/j.epidem.2015.07.001
Vaitkeviciute I, Teunis PFM, van Pelt W, Krogfelt KA. Kinetics of serum antibodies in response to infection with Yersinia enterocolitica. Epidemiol Infect. 2019;147(e165):1-7. doi:10.1017/S0950268819000530
Aiemjoy K, Seidman JC, Saha S, et al. Estimating typhoid incidence from community-based serosurveys: a multicohort study. Lancet Microbe. 2022;3:E578-E587. doi:10.1016/S2666-5247(22)00114-8
Diekmann O, de Graaf WF, Kretzschmar MEE, Teunis PFM. Waning and boosting: on the dynamics of immune status. J Math Biol. 2018;77(6):2023-2048. doi:10.1007/s00285-018-1239-5
Teunis PFM, Falkenhorst G, Ang CW, et al. Campylobacter seroconversion rates in selected countries in the European Union. Epidemiol Infect. 2013;141(10):2051-2057. doi:10.1017/S0950268812002774
Conover WJ. Practical Nonparametric Statistics. 3rd ed. New York, NY: Wiley; 1999:369-406.
Kolmogoroff A. Confidence limits for an unknown distribution function. Ann Math Stat. 1941;12(4):461-463.
Kuiper NH. Tests concerning random points on a circle. Proc K Ned Akad Wet Ser A. 1960;63:38-47.
Stephens MA. EDF statistics for goodness of fit and some comparisons. J Am Stat Assoc. 1974;69(347):730-737. doi:10.2307/2286009
Perez-Cruz F. Kullback-Leibler divergence estimation of continuous distributions. 2008 IEEE International Symposium on Information Theory. Piscataway, NJ: IEEE; 2008:1666-1670.
Press WH, Flannery BP, Teukolsky SA, Vetterling WT. Numerical Recipes in C: The Art of Scientific Computing. 2nd ed. New York, NY: Cambridge University Press; 1992.
Jiang B, Wu TY, Wong WH. Approximate Bayesian computation with Kullback-Leibler divergence as data discrepancy. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS). Vol 84. McKees Rocks, PA: PMLR; 2018:1711-1721.
Marjoram P, Molitor J, Plagnol V, Tavaré S. Markov chain Monte Carlo without likelihoods. Proc Natl Acad Sci USA. 2003;100(26):15324-15328. doi:10.1073/pnas.0306899100
Gilks WR, Richardson S, Spiegelhalter DJ, eds. Markov Chain Monte Carlo in Practice. London: Chapman and Hall; 1996.
Versteegh FGA, Mertens PLJM, de Melker HE, Roord JJ, Schellekens JFP, Teunis PFM. Age-specific long-term course of IgG antibodies to pertussis toxin after symptomatic infection with Bordetella pertussis. Epidemiol Infect. 2005;133:737-748.
Simonsen J, Teunis P, van Pelt W, et al. Usefulness of seroconversion rates for comparing infection pressures between countries. Epidemiol Infect. 2011;139(4):636-643. doi:10.1017/S0950268810000750
R Core Team. R: A Language and Environment for Statistical Computing, Vienna, Austria; 2023. http://www.R-project.org/
Brock K, Slade D. Poisson: simulating homogenous & non-homogenous Poisson processes. R package version 1.0; 2015. https://CRAN.R-project.org/package=poisson
Pebody RG, Gay NJ, Giammanco A, et al. The seroepidemiology of Bordetella pertussis infection in Western Europe. Epidemiol Infect. 2005;133(1):159-171.
Kretzschmar MEE, Teunis PFM, Pebody RG. Incidence and reproduction numbers of pertussis: estimates from serological and social contact data in five European countries. PLoS Med. 2010;7(6):e1000291.
Berbers GAM, van de Wetering MSE, van Gageldonk PGM, Schellekens JFP, Versteegh FGA, Teunis PFM. A novel method for evaluating natural and vaccine induced serological responses to Bordetella pertussis antigens. Vaccine. 2013;31(36):3732-3738. doi:10.1016/j.vaccine.2013.05.073
Baker JM, Nelson KN, Overton E, et al. Quantification of occupational and community risk factors for SARS-CoV-2 seropositivity among healthcare workers in a large U.S. healthcare system. Ann Intern Med. 2021;174(5):649-654. doi:10.7326/M20-7145
Havers FP, Reed C, Lim T, et al. Seroprevalence of Antibodies to SARS-CoV-2 in 10 Sites in the United States, March 23-May 12, 2020. JAMA Intern Med. 2020;180(12):1576-1586. doi:10.1001/jamainternmed.2020.4130
Whitaker HJ, Farrington CP. Estimation of infectious disease parameters from serological survey data: the impact of regular epidemics. Stat Med. 2004;23(15):2429-2443.
Roitt IM, Brostoff J, Male DK. Immunology. Maryland Heights, MO: Mosby; 1993.
Plummer M. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), Vienna, Austria; 2003:1-10.