Biology subjects

Beutels, P.

Publications and source records attributed to Beutels, P..

3 recordsLinked to original sources

bioRxiv2018

An ODE-based mixed modelling approach for B- and T-cell dynamics induced by Varicella-Zoster Virus vaccines in adults shows higher T-cell proliferation with Shingrix compared to Varilrix

Clinical trials covering the immunogenicity of a vaccine aim to study the longitudinal dynamics of certain immune cells after vaccination. The corresponding immunogenicity datasets are mainly analyzed by the use of statistical (mixed effects) models. This paper proposes the use of mathematical ordinary differential equation (ODE) models, combined with a mixed effects approach. ODE models are capable of translating underlying immunological post vaccination processes into mathematical formulas thereby enabling a testable data analysis. Mixed models include both population-averaged parameters (fixed effects) and individual-specific parameters (random effects) for dealing with inter-and intra-individual variability, respectively.\n\nThis paper models B-cell and T-cell datasets of a phase I/II, open-label, randomized, parallel-group study in which the immunogenicity of a new Herpes Zoster vaccine (Shingrix) is compared with the original Varicella Zoster Virus vaccine (Varilrix).\n\nSince few significant correlations were assessed between the B-cell datasets and T-cell datasets, each dataset was modeled separately. By following a general approach to both the formulation of several different models and the procedure of selecting the most suitable model, we were able propose a mathematical ODE mixed-effects model for each dataset. As such, the use of ODE-based mixed effects models offers a suitable framework for handling longitudinal vaccine immunogenicity data. Moreover, it is possible to test differences in immunological processes between the two vaccines.\n\nWe found that the Shingrix vaccination schedule led to a more pronounced proliferation of T-cells, without a difference in T-cell decay rate compared to the Varilrix vaccination schedule.\n\nAuthor summaryUpon vaccination, B-cells and T-cells are activated to induce an immune response against the vaccine antigen at hand. In this paper, we study and compare the longitudinal dynamics of the specific immune response based on a vaccine trial in which the immunogenicity of a new Herpes Zoster vaccine (Shingrix) is compared with the original Varicella Zoster Virus vaccine (Varilrix). We combine the use of ordinary differential equations (ODEs), i.e. mathematical models which are used to describe the dynamics of the immune response, with advanced regression analyses enabling us to infer the model parameters describing these dynamics. The resulting ODE-based mixed effects models enable describing the immune response dynamics allowing for both inter-and intra-individual variability; comparing the dynamics induced by the two vaccines and studying the B-and T-cell interactions. We found a more pronounced proliferation of T-cells for the Shingrix vaccination schedule as compared to the Varilrix vaccination schedule. The proposed methodology offers a suitable framework for better understanding the immunogenicity of vaccines.

immunology↗

bioRxiv2018

A systematic review of social contact surveys to inform transmission models of close contact infections

Social contact data are increasingly being used to inform models for infectious disease spread with the aim of guiding effective policies on disease prevention and control. In this paper, we undertake a systematic review of the study design, statistical analyses and outcomes of the many social contact surveys that have been published. Our primary focus is to identify the designs that have worked best and the most important determinants and to highlight the most robust [fi]ndings.\n\nTwo publicly accessible online databases were systematically searched for articles regarding social contact surveys. PRISMA guidelines were followed as closely as possible. In total, 64 social contact surveys were identi[fi]ed. These surveys were conducted in 24 countries, and more than 80% of the surveys were conducted in high-income countries. Study settings included general population (58%), schools/universities (37%) and health care/conference/research institutes (5%). The majority of studies did not focus on a speci[fi]c age group (38%), whereas others focused on adults (32%) or children (19%). Retrospective and prospective designs were used mostly (45% and 41% of the surveys, respectively) with 6% using both for comparison purposes. The de[fi]nition of a contact varied among surveys, e.g. a non-physical contact may require conversation, close proximity or both. Age, time schedule (e.g., weekday/weekend) and household size were identi[fi]ed as relevant determinants for contact pattern across a large number of studies. The surveys present a wide range of study designs. Throughout, we found that the overall contact patterns were remarkably robust for the study details. By considering the most common approach in each aspect of design (e.g., sampling schemes, data collection, de[fi]nition of contact), we could identify a common practice approach that can be used to facilitate comparison between studies and for benchmarking future studies.

epidemiology↗

bioRxiv2017

Household Members Do Not Contact Each Other at Random: Implications for Infectious Disease Modelling

Airborne infectious diseases such as influenza are primarily transmitted from human to human by means of social contacts and thus easily spread within households. Epidemic models, used to gain insight in infectious disease spread and control, typically rely on the assumption of random mixing within households. Until now there was no direct empirical evidence to support this assumption. Here, we present the first social contact survey specifically designed to study contact networks within households. The survey was conducted in Belgium (Flanders and Brussels) in 2010-2011. We analyzed data from 318 households totaling 1266 individuals with household sizes ranging from 2 to 7 members. Exponential-family random graph models (ERGMs) were fitted to the within-household contact networks to reveal the processes driving contact between household members, both on weekdays and weekends. The ERGMs showed a high degree of clustering and, specifically on weekdays, decreasing connectedness with increasing household size. Furthermore, we found that the odds of a contact between father and child is smaller than for any other pair except for older siblings. Epidemic simulation results suggest that within-household contact density is the main driver of differences in epidemic spread between complete and empirical-based household contact networks. The homogeneous mixing assumption may therefore be an adequate characterization of the within-household contact structure for the purpose of epidemic simulation. However, ignoring the contact density when inferring from an epidemic model will result in biased estimates of within-household transmission rates. Further research on the implementation of within-household contact networks in epidemic models is necessary.\n\nSignificance StatementHouseholds have a pivotal role in the spread of airborne infectious diseases. Households are bridging units between schools and workplaces, and social contacts within households are frequent and intimate, allowing for rapid disease spread. Infectious disease models typically assume that members of a household contact each other randomly. Until now there was no direct empirical evidence to support this assumption. In this paper, we present the first social contact survey specifically designed to study contact networks within households with young children. We investigate which factors drive contacts between household members on one particular day by means of a statistical model. Our results suggest the importance of connectedness within households over heterogeneity in number of contacts.

epidemiology↗