1,720,984 research outputs found
Nonparametric serial interval estimation with uniform mixtures
No full textThe serial interval of an infectious disease is a key instrument to understand transmission dynamics. Estimation of the serial interval distribution from illness onset data extracted from transmission pairs is challenging due to the presence of censoring and state-of-the-art methods mostly rely on parametric models. We present a fully data-driven methodology to estimate the serial interval distribution based on interval-censored serial interval data. The proposed nonparametric estimator of the cumulative distribution function of the serial interval is based on the class of uniform mixtures. Closed-form solutions are available for point estimates of different serial interval features and the bootstrap is used to construct confidence intervals. Algorithms underlying our approach are simple, stable, and computationally inexpensive, making them easily implementable in a programming language that is most familiar to a potential user. The nonparametric user-friendly routine is included in the EpiDelays package for ease of implementation. Our method complements existing parametric approaches for serial interval estimation and permits to analyze past, current, or future illness onset data streams following a set of best practices in epidemiological delay modeling.OG and NH were supported by the VERDI project (101045989) and the ESCAPE project (101095619), funded by the European Union. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them. OG and NH acknowledge the financial support of the Fondation Universitaire de Belgique (file nr. AS-0608). OG and NH were also supported by the BE-PIN project (contract nr. TD/231/BE-PIN) funded by BELSPO (Belgian Science Policy Office) as part of the POST-COVID programme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
An Efficient Approach to Nowcasting the Time-varying Reproduction Number
No full textEstimating the instantaneous reproduction number ( ) in near real time is crucial for monitoring and responding to epidemic outbreaks on a daily basis. However, such estimates often suffer from bias due to reporting delays inherent in surveillance systems. We propose a fast and flexible Bayesian methodology to overcome this challenge by estimating while taking into account reporting delays. Furthermore, the method naturally takes into account the uncertainty associated with the nowcasting of cases to get a valid uncertainty estimation of the nowcasted reproduction number. We evaluate the proposed methodology through a simulation study and apply it to COVID-19 incidence data in Belgium
Flexible Bayesian estimation of incubation times
No full textThe incubation period is of paramount importance in infectious disease epidemiology as it informs about the transmission potential of a pathogenic organism and helps the planning of public health strategies to keep an epidemic outbreak under control. Estimation of the incubation period distribution from reported exposure times and symptom onset times is challenging as the underlying data is coarse. We developed a new Bayesian methodology using Laplacian-P-splines that provides a semiparametric estimation of the incubation density based on a Langevinized Gibbs sampler. A finite mixture density smoother informs a set of parametric distributions via moment matching and an information criterion arbitrates between competing candidates. Algorithms underlying our method find a natural nest within the EpiLPS package, which has been extended to cover estimation of incubation times. Various simulation scenarios accounting for different levels of data coarseness are considered with encouraging results. Applications to real data on coronavirus disease 2019, Middle East respiratory syndrome, and Mpox reveal results that are in alignment with what has been obtained in recent studies. The proposed flexible approach is an interesting alternative to classic Bayesian parametric methods for estimation of the incubation distribution.Funding
This work was supported by the ESCAPE project (101095619) and the VERDI project (101045989), funded by the European Union.
Acknowledgments
We thank Jantien Backer and Jacco Wallinga from the National Institute for Public Health and the Environment (RIVM) for discussing their results on the COVID-19 incubation period estimation based on confirmed cases with Wuhan travel histor
Bayesian Nowcasting with Laplacian-P-Splines
No full textDuring an epidemic, the daily number of reported infected cases, deaths or hospitalizations is often lower than the actual number due to reporting delays. Nowcasting aims to estimate the cases that have not yet been reported and combine it with the already reported cases to obtain an estimate of the daily cases. In this article, we present a fast and flexible Bayesian approach for nowcasting by combining P-splines and Laplace approximations. Laplacian-P-splines provide a flexible framework for nowcasting that is computationally less demanding as compared to traditional Markov chain Monte Carlo techniques. The proposed approach also permits to naturally quantify the prediction uncertainty. Model performance is assessed through simulations and the nowcasting method is applied to COVID-19 mortality and incidence cases in Belgium. Supplementary materials for this article are available online.VERDI: This project was supported by the VERDI project (101045989), funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Health and Digital Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
ESCAPE: This project was supported by the ESCAPE project (101095619), funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them. The authors acknowledge funding from the Special Research Fund through the Methusalem project BOF22M01
Penalty parameter selection and asymmetry corrections to Laplace approximations in Bayesian P-splines models
Full text linkLaplacian-P-splines (LPS) associate the P-splines smoother and the Laplace approximation in a unifying framework for fast and flexible inference under the Bayesian paradigm. Gaussian Markov field priors imposed on penalized latent variables and the Bernstein-von Mises theorem typically ensure a razor-sharp accuracy of the Laplace approximation to the posterior distribution of these variables. This accuracy can be seriously compromised for some unpenalized parameters, especially when the information synthesized by the prior and the likelihood is sparse. We propose a refined version of the LPS methodology by splitting the latent space in two subsets. The first set involves latent variables for which the joint posterior distribution is approached from a non-Gaussian perspective with an approximation scheme that is particularly well tailored to capture asymmetric patterns, while the posterior distribution for parameters in the complementary latent set undergoes a traditional treatment with Laplace approximations. As such, the dichotomization of the latent space provides the necessary structure for a separate treatment of model parameters, yielding improved estimation accuracy as compared to a setting where posterior quantities are uniformly handled with Laplace. In addition, the proposed enriched version of LPS remains entirely sampling-free, so that it operates at a computing speed that is far from reach to any existing Markov chain Monte Carlo approach. The methodology is illustrated on the additive proportional odds model with an application on ordinal survey data
An approximate Bayesian approach for estimation of the instantaneous reproduction number under misreported epidemic data
No full textIn epidemic models, the effective reproduction number is of central importance to assess the transmission dynamics of an infectious disease and to orient health intervention strategies. Publicly shared data during an outbreak often suffers from two sources of misreporting (underreporting and delay in reporting) that should not be overlooked when estimating epidemiological parameters. The main statistical challenge in models that intrinsically account for a misreporting process lies in the joint estimation of the time-varying reproduction number and the delay/underreporting parameters. Existing Bayesian approaches typically rely on Markov chain Monte Carlo algorithms that are extremely costly from a computational perspective. We propose a much faster alternative based on Laplacian-P-splines (LPS) that combines Bayesian penalized B-splines for flexible and smooth estimation of the instantaneous reproduction number and Laplace approximations to selected posterior distributions for fast computation. Assuming a known generation interval distribution, the incidence at a given calendar time is governed by the epidemic renewal equation and the delay structure is specified through a composite link framework. Laplace approximations to the conditional posterior of the spline vector are obtained from analytical versions of the gradient and Hessian of the log-likelihood, implying a drastic speed-up in the computation of posterior estimates. Furthermore, the proposed LPS approach can be used to obtain point estimates and approximate credible intervals for the delay and reporting probabilities. Simulation of epidemics with different combinations for the underreporting rate and delay structure (one-day, two-day, and weekend delays) show that the proposed LPS methodology delivers fast and accurate estimates outperforming existing methods that do not take into account underreporting and delay patterns. Finally, LPS is illustrated in two real case studies of epidemic outbreaks.sponsorship: European Union's Research and Innovation Action EpiPose, Grant/Award Number: 101003688 (European Union's Research and Innovation Action EpiPose|101003688)status: Publishe
Laplacian‐P‐splines for Bayesian inference in the mixture cure model
No full textAbstract: The mixture cure model for analyzing survival data is characterized by the assumption that the population under study is divided into a group of subjects who will experience the event of interest over some finite time horizon and another group of cured subjects who will never experience the event irrespective of the duration of follow-up. When using the Bayesian paradigm for inference in survival models with a cure fraction, it is common practice to rely on Markov chain Monte Carlo (MCMC) methods to sample from posterior distributions. Although computationally feasible, the iterative nature of MCMC often implies long sampling times to explore the target space with chains that may suffer from slow convergence and poor mixing. Furthermore, extra efforts have to be invested in diagnostic checks to monitor the reliability of the generated posterior samples. A sampling-free strategy for fast and flexible Bayesian inference in the mixture cure model is suggested in this article by combining Laplace approximations and penalized B-splines. A logistic regression model is assumed for the cure proportion and a Cox proportional hazards model with a P-spline approximated baseline hazard is used to specify the conditional survival function of susceptible subjects. Laplace approximations to the posterior conditional latent vector are based on analytical formulas for the gradient and Hessian of the log-likelihood, resulting in a substantial speed-up in approximating posterior distributions. The spline specification yields smooth estimates of survival curves and functions of latent variables together with their associated credible interval are estimated in seconds. A fully stochastic algorithm based on a Metropolis-Langevin-within-Gibbs sampler is also suggested as an alternative to the proposed Laplacian-P-splines mixture cure (LPSMC) methodology. The statistical performance and computational efficiency of LPSMC is assessed in a simulation study. Results show that LPSMC is an appealing alternative to MCMC for approximate Bayesian inference in standard mixture cure models. Finally, the novel LPSMC approach is illustrated on three applications involving real survival data
EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number
No full textIn infectious disease epidemiology, the instantaneous reproduction number [Formula: see text] is a time-varying parameter defined as the average number of secondary infections generated by an infected individual at time t. It is therefore a crucial epidemiological statistic that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool (EpiLPS) for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible intervals of [Formula: see text] by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates of [Formula: see text] in only a few seconds; and (2) an approach based on a Markov chain Monte Carlo (MCMC) scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a negative binomial distribution to account for potential overdispersion in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a "plug-in'' estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression of [Formula: see text] as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and on the SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France. Author summary The instantaneous reproduction number R-t is a key statistic that provides important insights into an epidemic outbreak as it informs about the average number of secondary infections engendered by an infectious agent. We present a flexible Bayesian approach called EpiLPS (Epidemiological modeling with Laplacian-P-Splines) for efficient estimation of the epidemic curve and Rt based on daily case count data and the serial interval distribution. Computational speed and absence of arbitrary assumptions on smoothing makes EpiLPS an interesting tool for estimation of the reproduction number. Our methodology is validated through different simulation scenarios by using the associated R software package (https://cran.r-project.org/package=EpiLPS). We also demonstrate the use of EpiLPS on real data from two historical outbreaks and on the SARS-CoV-2 pandemic
Cohort-based smoothing methods for age-specific contact rates
No full textInternational audienceThe use of social contact rates is widespread in infectious disease modeling since it has been shown that they are key driving forces of important epidemiological parameters. Quantification of contact patterns is crucial to parameterize dynamic transmission models and to provide insights on the (basic) reproduction number. Information on social interactions can be obtained from population-based contact surveys, such as the European Commission project POLYMOD. Estimation of age-specific contact rates from these studies is often done using a piecewise constant approach or bivariate smoothing techniques. For the latter, typically, smoothness is introduced in the dimensions of the respondent’s and contact’s age (i.e., the rows and columns of the social contact matrix). We propose a smoothing constrained approach—taking into account the reciprocal nature of contacts—introducing smoothness over the diagonal (including all subdiagonals) of the social contact matrix. This modeling approach is justified assuming that when people age their contact behavior changes smoothly. We call this smoothing from a cohort perspective. Two approaches that allow for smoothing over social contact matrix diagonals are proposed, namely (i) reordering of the diagonal components of the contact matrix and (ii) reordering of the penalty matrix ensuring smoothness over the contact matrix diagonals. Parameter estimation is done in the likelihood framework by using constrained penalized iterative reweighted least squares. A simulation study underlines the benefits of cohort-based smoothing. Finally, the proposed methods are illustrated on the Belgian POLYMOD data of 2006. Code to reproduce the results of the article can be downloaded on this GitHub repository https://github.com/oswaldogressani/Cohort_smoothing
The combined effect of systemic antibiotics and proton pump inhibitors on Clostridioides difficile infection and recurrence
No full textBackground Antibiotics and proton pump inhibitors (PPI) are recognized risk factors for acquisition and recurrence of Clostridioides difficile infection (CDI), yet combined effects remain unclear.Objectives To assess the short- and long-term effects of antibiotics and PPIs on CDI risk and recurrence.Methods Population-based study including all 43 152 patients diagnosed with CDI in Sweden (2006-2019), and 355 172 matched population controls without CDI. The impact of antibiotics and PPIs on CDI risk and recurrence was explored for recent (0-30 days) and preceding (31-180 days) use prior to their first CDI diagnosis, using multivariable conditional logistic regression presented as odds ratios (ORs) and 95% confidence interval, adjusted for demographics, comorbidities and other drugs.Results Compared to controls, the combined effect of recent PPIs and antibiotics [ORAB+PPI = 17.51 (17.48-17.53)] on CDI risk was stronger than the individual effects [ORAB = 15.37 (14.83-15.93); ORPPI = 2.65 (2.54-2.76)]. Results were less pronounced for exposure during the preceding months. Dose-response analyses showed increasing exposure correlated with CDI risk [recent use: ORAB = 6.32 (6.15-6.49); ORPPI = 1.65 (1.62-1.68) per prescription increase]. Compared to individuals without recurrence (rCDI), recent [ORAB = 1.30 (1.23-1.38)] and preceding [ORAB = 1.23 (1.16-1.31); ORPPI = 1.12 (1.03-1.21)] use also affected the risk of recurrence yet without significant interaction between both. Recent macrolides/lincosamides/streptogramins; other antibacterials including nitroimidazole derivates; non-penicillin beta lactams and quinolones showed the strongest association with CDI risk and recurrence, particularly for recent use. PPI use, both recent and preceding, further increased the CDI risk associated with almost all antibiotic classes.Results Compared to controls, the combined effect of recent PPIs and antibiotics [ORAB+PPI = 17.51 (17.48-17.53)] on CDI risk was stronger than the individual effects [ORAB = 15.37 (14.83-15.93); ORPPI = 2.65 (2.54-2.76)]. Results were less pronounced for exposure during the preceding months. Dose-response analyses showed increasing exposure correlated with CDI risk [recent use: ORAB = 6.32 (6.15-6.49); ORPPI = 1.65 (1.62-1.68) per prescription increase]. Compared to individuals without recurrence (rCDI), recent [ORAB = 1.30 (1.23-1.38)] and preceding [ORAB = 1.23 (1.16-1.31); ORPPI = 1.12 (1.03-1.21)] use also affected the risk of recurrence yet without significant interaction between both. Recent macrolides/lincosamides/streptogramins; other antibacterials including nitroimidazole derivates; non-penicillin beta lactams and quinolones showed the strongest association with CDI risk and recurrence, particularly for recent use. PPI use, both recent and preceding, further increased the CDI risk associated with almost all antibiotic classes.Conclusion Recent and less recent use of PPIs and systemic antibiotics was associated with an increased risk of CDI, particularly in combination.This work was supported by the Centre for Translational Microbiome Research (CTMR), Karolinska Institutet, Sweden through a Research Collaboration Agreement with Ferring Pharmaceuticals
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