1,721,183 research outputs found
Statistical models for estimating individual heterogeneity in acquisition of infectious diseases and outbreak risk in highly vaccinated populations
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Statistical models for estimating individual heterogeneity in acquisition of infectious diseases and outbreak risk in highly vaccinated populations
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The impact of behavioral interventions on co-infection dynamics: An exploration of the effects of home isolation
No full textBehavioral epidemiology, the field aiming to determine the impact of individual behavior on the spread of an epidemic, has gained increased recognition during the last few decades. Behavioral changes due to the development of symptoms have been studied in mono-infections. However, in reality, multiple infections are circulating within the same time period and behavioral changes resulting from contraction of one of the diseases affect the dynamics of the other. The present study aims at assessing the effect of home isolation on the joint dynamics of two infectious diseases, including co-infection, assuming that the two diseases do not confer cross-immunity. We use an age- and time- structured co-infection model based on partial differential equations. Social contact matrices, describing different mixing patterns of symptomatic and asymptomatic individuals are incorporated into the calculation of the age- and time-specific marginal forces of infection. Two scenarios are simulated, assuming that one of the diseases has more severe symptoms than the other. In the first scenario, people stay only at home when having symptoms of the most severe disease. In the second scenario, twice as many people stay at home when having symptoms of the most severe disease than when having symptoms of the other disease. The results show that the impact of home isolation on the joint dynamics of two infectious diseases depends on the epidemiological parameters and properties of the diseases (e.g., basic reproduction number, symptom severity). In case both diseases have a low to moderate basic reproduction number, and there is no home isolation for the less severe disease, the final size of the less severe disease increases with the proportion of symptomatic cases of the most severe disease staying at home, after an initial decrease. This counterintuitive result could be explained by a shift in the peak time of infection of the disease with the most severe symptoms, resulting in a smaller number of people with less contacts at the peak time of the other infection. When twice as many people stay at home when having symptoms of the most severe disease than when having symptoms of the other disease, increasing the proportion staying at home always reduces the final size of both diseases, and the number of co-infections. In conclusion, when providing advise if people should stay at home in the context of two or more co-circulating diseases, one has to take into account epidemiological parameters and symptom severity. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)This research is part of a project that has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement 682540 TransMID). NH gratefully acknowledges support from the University of Antwerp scientific chair in Evidence-Based Vaccinology, financed in 2009-2017 by a gift from Pfizer and in 2016 by a gift from GSK. We gratefully acknowledge Thomas Kovac (UHasselt) for improving our R code for running the model. We thank Kim Van Kerckhove (UHasselt, Ugentec) and Eva Santermans (UHasselt, Galapagos) for their input and discussions on social contact data. We thank James Wood (UNSW Sidney, Australia) for his helpful comments and discussions that improved the manuscript. We like to thank the reviewers for the constructive comments
Bernstein-based estimation of the cross ratio function
No full textLocal association measures provide useful insights in time-varying changes in association, especially between time-to-event variables. Such local dependence between two correlated random variables can be measured using the cross ratio function. The cross ratio function is defined as the ratio of conditional hazard functions which have been estimated using Bernstein polynomials before. Alternatively, the cross ratio function can be expressed in terms of (derivatives of) the joint survival function of the two random variables. In this paper, we discuss an alternative Bernstein-based plug-in estimator of the cross ratio function in which each of the ingredients is estimated separately. Next to asymptotic normality of the nonparametric estimator, a simulation study is used to assess its finite-sample performance. Finally, the novel estimator is applied to a real-life data application.Funding
SA andOS gratefully acknowledge support of theResearch Foundation - Flanders (FWO) (grant nr. G011022N).
Acknowledgments
The authors express their gratitude to access and use the hospital data collected in Ziekenhuis Oost Limburg to illustrate the novel methodology presented in this paper
Quantiles of the conditional residual lifetime
No full textThe study of the residual lifetime received considerable attention in survival analysis and in other disciplines like reliability theory and actuarial science. The quantile residual lifetime function, the inverse of the residual lifetime distribution P(T-1 - t(1) t(1)), provides an interesting and well-studied measure to analyse residual lifetimes. In this paper we generalize the residual lifetime distribution and the quantile residual lifetime function by adding an extra conditioning of the form {T-2 t(2)}, where T-2 is a second variable containing extra information on T-1. Wepropose, for right-censored lifetimes, nonparametric estimators for this generalized conditional remaining lifetime distribution and the corresponding quantile function and we derive the asymptotic theory. In a simulation study, we show the good performance of the newly proposed quantile estimators and we discuss an application to real data on primary biliary cirrhosis
A Bayesian mixture model accounting for individual heterogeneity in response to pathogenic infection
Full text linkThe analysis of multivariate serological data derived from blood serum samples and tested for the presence of antibodies against multiple pathogens gained attention in recent years. Despite the common use of a so-called threshold approach to classify individuals as seronegative or -positive, limitations of such an approach have been reported in the literature, with the subjective choice of the threshold being the most important. Here, we consider a Bayesian mixture approach to model continuous IgG antibody concentrations directly while accounting for the presence of individual heterogeneity and implied association between antibody titer levels for two infections. We fitted the proposed model to Belgian bivariate serological data on the varicella-zoster virus (VZV) and parvovirus B19 (PVB19). Given the existing body of evidence with respect to possible reinfections with PVB19, we investigated whether models explicitly accounting for waning of humoral immunity improved model fit. Our results showed that although after a steep rise with age, the observed seroprevalence for PVB19 decreases between the ages of 20 and 40, the mean IgG antibody concentration remains constant with age among individuals in the seropositive component. This could provide evidence of a direct impact of reinfections with PVB19 on the observed IgG antibody levels, while individuals with loss of humoral immunity after natural infection imply an increase in susceptibility. For VZV, the mean IgG antibody levels slightly decrease with increasing age among seropositive individuals, indicating only very limited waning of humoral immunity as age-dependent seroprevalence estimates are monotonically increasing with increasing age. In general, based on our analyses, we showed that mixture models provide additional insights concerning the waning of humoral immunity as compared to more traditional frailty approaches, which focus on estimating the seroprevalence solely while the model is sufficiently flexible to capture observed dynamics in IgG antibody decay
Planning for transit system reliability using productive performance and risk assessment
No full textUrban transit system performance may be quantified and assessed using transit capacity and productive capacity for planning, design and operational management. Bunker (4) defines important productive performance measures of an individual transit service and transit line. Transit work (p-km) captures transit task performed over distance. Transit productiveness (p-km/h) captures transit work performed over time. This paper applies productive performance with risk assessment to quantify transit system reliability. Theory is developed to monetize transit segment reliability risk on the basis of demonstration Annual Reliability Event rates by transit facility type, segment productiveness, and unit-event severity. A comparative example of peak hour performance of a transit sub-system containing bus-on-street, busway, and rail components in Brisbane, Australia demonstrates through practical application the importance of valuing reliability. Comparison reveals the highest risk segments to be long, highly productive on street bus segments followed by busway (BRT) segments and then rail segments. A transit reliability risk reduction treatment example demonstrates that benefits can be significant and should be incorporated into project evaluation in addition to those of regular travel time savings, reduced emissions and safety improvements. Reliability can be used to identify high risk components of the transit system and draw comparisons between modes both in planning and operations settings, and value improvement scenarios in a project evaluation setting. The methodology can also be applied to inform daily transit system operational management
A general frailty model to accommodate individual heterogeneity in the acquisition of multiple infections: An application to bivariate current status data
No full textThe analysis of multivariate time-to-event (TTE) data can become complicated due to the presence of clustering, leading to dependence between multiple event times. For a long time, (conditional) frailty models and (marginal) copula models have been used to analyze clustered TTE data. In this article, we propose a general frailty model employing a copula function between the frailty terms to construct flexible (bivariate) frailty distributions with the application to current status data. The model has the advantage to impose a less restrictive correlation structure among latent frailty variables as compared to traditional frailty models. Specifically, our model uses a copula function to join the marginal distributions of the frailty vector. In this article, we considered different copula functions, and we relied on marginal gamma distributions due to their mathematical convenience. Based on a simulation study, our novel model outperformed the commonly used additive correlated gamma frailty model, especially in the case of a negative association between the frailties. At the end of the article, the new methodology is illustrated on real-life data applications entailing bivariate serological survey data.Methusalem research grant from the Flemish government; IAP Research
Network [P7/06]; Research Fund of Hasselt University [BOF11NI31]Tran, TMP (reprint author), Hasselt Univ, Data Sci Inst, Interuniv Inst Biostat & Stat Bioinformat, Diepenbeek, Belgium.
[email protected]
Inferring rubella outbreak risk from seroprevalence data in Belgium
No full textRubella is usually a mild disease for which infections often pass by unnoticed. In approximately 50% of the cases, there are no or only few clinical symptoms. However, rubella contracted during early pregnancy could lead to spontaneous abortion, to central nervous system defects, or to one of a range of other serious and debilitating conditions in a newborn such as the congenital rubella syndrome. Before the introduction of mass vaccination, rubella was a common childhood infection occurring all over the world. However, since the introduction of rubella antigen-containing vaccines, the incidence of rubella has declined dramatically in high-income countries. Recent large-scale mumps outbreaks, one of the components in the combined measles-mumps-rubella vaccine, occurring in countries throughout Europe with
high vaccination coverage, provide evidence of pathogen-specific waning of vaccine-induced immunity and primary vaccine failure. In addition, recent measles outbreaks affecting populations with suboptimal vaccination coverages stress the importance of maintaining high vaccination coverages. In this paper, we focus on the assessment of rubella outbreak risk using a previously developed method to identify geographic regions of high outbreak potential. The methodology relies on 2006 rubella seroprevalence data and vaccination coverage data from Belgium and information on primary and secondary vaccine failure obtained from extensive literature reviews. We estimated the rubella outbreak risk in Belgium to be low, however maintaining high levels of immunisation and surveillance are of utmost importance to avoid
future outbreaks.This work was supported by the Research Fund of Hasselt University (grant BOF11NI31 to SA). NH and PB acknowledge support from the Scientific Chair in Evidence-based Vaccinology, financed by a gift from Pfizer, and the Antwerp Study Centre for Infectious Diseases (ASCID), both at the University of Antwerp
Nonparametric estimation of risk ratios for bivariate data
No full textInspired by the cross-ratio proposed by Clayton, we study a new risk ratio to describe the relation between the components of the random vector (T-1, T-2). It is the ratio of the conditional hazard rate function of T-1 at t(1), given that T-2 >= t(2) and the conditional hazard rate function of T-1 at t(1), given that T-2 >= t(2). A nonparametric estimator is proposed and its asymptotic distribution is obtained using Bernstein smoothing for the survival copula of (T-1, T-2) and its derivatives. The finite sample performance of the estimator is studied via simulations. The practical use of the risk ratio is illustrated in two real datasets, one on food expenditure and net income and one on the relation between maximum heart rate and age, for patients suffering from heart disease versus control patients (no heart disease). Extensions of the proposed risk ratio are given in the discussion section
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