Biology subjects

Hens, N.

Publications and source records attributed to Hens, N..

10 recordsLinked to original sources

bioRxiv2018

SimpactCyan 1.0: An Open-source Simulator for Individual-Based Models in HIV Epidemiology with R and Python Interfaces

SimpactCyan is an open-source simulator for individual-based models in HIV epidemiology. Its core algorithm is written in C++ for computational efficiency, while the R and Python interfaces aim to make the tool accessible to the fast-growing community of R and Python users. Transmission, treatment and prevention of HIV infections in dynamic sexual networks are simulated by discrete events. A generic "intervention" event allows model parameters to be changed over time, and can be used to model medical and behavioural HIV prevention programmes. First, we describe a more efficient variant of the modified Next Reaction Method that drives our continuous-time simulator. Next, we outline key built-in features and assumptions of individual-based models formulated in SimpactCyan, and provide code snippets for how to formulate, execute and analyse models in SimpactCyan through its R and Python interfaces. Lastly, we give two examples of applications in HIV epidemiology: the first demonstrates how the software can be used to estimate the impact of progressive changes to the eligibility criteria for HIV treatment on HIV incidence. The second example illustrates the use of SimpactCyan as a data-generating tool for assessing the performance of a phylodynamic inference framework.

epidemiology↗

bioRxiv2018

The impact of behavioral interventions on co-infectiondynamics: an exploration of the effects of home isolation

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.\n\nThe 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.\n\nTwo 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.\n\nThe 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. 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.\n\nIn 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.

epidemiology↗

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↗

bioRxiv2018

Cohort-based Smoothing Methods for Age-specific Contact Rates

The use of social contact rates is widespread in infectious disease modelling, since it is known that they provide proxies of crucial determinants of epidemiological and disease transmission parameters. Information on social contact rates can, for example, be obtained from a population-based contact diary survey, such as the POLYMOD study. Estimation of age-specific contact rates from these studies is often done using bivariate smoothing techniques. Typically, smoothing is done in the dimensions of the respondents and contacts age. In this paper, we introduce a smoothing constrained approach - taking into account the reciprocal nature of contacts - where the contact rates are assumed smooth from a cohort perspective as well as from the age distribution of contacts. This is achieved by smoothing over the diagonal components (including all subdiagonals) of the social contact matrix. This approach is supported by the fact that people age with time and thus contact rates should vary smoothly by cohorts. Two approaches that allow for smoothing of social contact data over cohorts are proposed: (1) reordening of the diagonal components of the social contact rate matrix; and (2) reordening of the penalty matrix associated with the diagonal components. Parameter estimation is done using constrained penalized iterative reweighted least squares. A simulation study is presented to compare methods. The proposed methods are illustrated on the Belgian POLYMOD data of 2006.

epidemiology↗

bioRxiv2018

Sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling

BackgroundOur work was motivated by the need to, given serum availability and/or financial resources, decide on which samples to test for different pathogens in a serum bank. Simulation-based sample size calculations were performed to determine the age-based sampling structures and optimal allocation of a given number of samples for testing across various age groups best suited to estimate key epidemiological parameters (e.g., seroprevalence or force of infection) with acceptable precision levels in a cross-sectional seroprevalence survey.\n\nMethodsStatistical and mathematical models and three age-based sampling structures (survey-based structure, population-based structure, uniform structure) were used. Our calculations are based on Belgian serological survey data collected in 2002 where testing was done, amongst others, for the presence of IgG antibodies against measles, mumps, and rubella, for which a national mass immunisation programme was introduced in 1985 in Belgium, and against varicella-zoster virus and parvovirus B19 for which the endemic equilibrium assumption is tenable in Belgium.\n\nResultsThe optimal age-based sampling structure to use in the sampling of a serological survey as well as the optimal allocation distribution varied depending on the epidemiological parameter of interest for a given infection and between infections.\n\nConclusionsWhen estimating key epidemiological parameters with acceptable levels of precision within the context of a single cross-sectional serological survey, attention should be given to the age-based sampling structure. Simulation-based sample size calculations in combination with mathematical modelling can be utilised for choosing the optimal allocation of a given number of samples over various age groups.

epidemiology↗

bioRxiv2018

Modelling longitudinal binary outcomes with outcome-dependent observation times: an application to a malaria cohort study

Inspite of the global reduction of 21% in malaria incidence between 2010 and 2015, the disease still threatens many lives of children and pregnant mothers in African countries. A correct assessment and evaluation of the impact of malaria control strategies still remains quintessential in order to eliminate the disease and its burden. Malaria follow-up studies typically involve routine visits at pre-scheduled time points and/or clinical visits whenever individuals experience malaria-like symptoms. In the latter case, infection triggers outcome assessment, thereby leading to outcome-dependent sampling (ODS). Ordinary methods used to analyse such longitudinal data ignore ODS and potentially lead to biased estimates of malaria-specific transmission parameters, hence, inducing an incorrect assessment and evaluation of malaria control strategies. In this paper, we propose novel methodology to handle ODS using a joint model for the longitudinal binary outcome measured at routine visits and the clinical event times. The methodology is applied to malaria parasitaemia data from a cohort of n = 988 Ugandan children aged 0.5-10 years from 3 regions (Walukuba - 300 children, Kihihi - 355 children and Nagongera - 333 children) with varying transmission intensities (entomological inoculation rate equal to 2.8, 32 and 310 infectious bites per unit year, respectively) collected between 2011-2014. The results indicate that malaria parasite prevalence and force of infection (FOI) increase with age in the region of high malaria intensities with FOI highest in age group 5-10 years. For the region of medium intensity, the prevalence slightly increases with age and the FOI for the routine process is highest in age group 5-10 years yet for the clinically observed infections, the FOI gradually decreases with increasing age. For the region with low intensity, both the prevalence and FOI peak at the age of one year after which the former remains constant with age yet the latter suddenly decreases with age for the clinically observed infections. In all study sites, both the prevalence and FOI are highest among previously asymptomatic children and lowest among their symptomatic counterparts. Using a simulation study inspired by the malaria data at hand, the proposed methodology shows to have the smallest bias, especially when consecutive positive malaria parasitaemia presence results within a time period of 35 days were considered to be due to the same infection.

epidemiology↗

bioRxiv2017

The impact of regular school closure on seasonal influenza epidemics: a data-driven spatial transmission model for Belgium

School closure is often considered as an option to mitigate influenza epidemics because of its potential to reduce transmission in children and then in the community. The policy is still however highly debated because of controversial evidence. Moreover, the specific mechanisms leading to mitigation are not clearly identified.\n\nWe introduced a stochastic spatial age-specific metapopulation model to assess the role of holiday-associated behavioral changes and how they affect seasonal influenza dynamics. The model is applied to Belgium, parameterized with country-specific data on social mixing and travel, and calibrated to the 2008/2009 influenza season. It includes behavioral changes occurring during weekend vs. weekday, and holiday vs. school-term. Several experimental scenarios are explored to identify the relevant social and behavioral mechanisms.\n\nStochastic numerical simulations show that holidays considerably delay the peak of the season and mitigate its impact. Changes in mixing patterns are responsible for the observed effects, whereas changes in travel behavior do not alter the epidemic. Weekends are important in slowing down the season by periodically dampening transmission. Christmas holidays have the largest impact on the epidemic, however later school breaks may help in reducing the epidemic size, stressing the importance of considering the full calendar. An extension of the Christmas holiday of 1 week may further mitigate the epidemic.\n\nChanges in the way individuals establish contacts during holidays are the key ingredient explaining the mitigating effect of regular school closure. Our findings highlight the need to quantify these changes in different demographic and epidemic contexts in order to provide accurate and reliable evaluations of closure effectiveness. They also suggest strategic policies in the distribution of holiday periods to minimize the epidemic impact.

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↗

bioRxiv2017

The shape of the contact-density function matters when modelling disease transmission in fluctuating populations

Models of disease transmission in a population with changing densities must assume a relation between infectious contacts and density. Typically, a choice is made between a constant (frequency-dependence) and a linear (density-dependence) contact-density function, but it is becoming increasingly clear that intermediate, nonlinear functions intermediate are more realistic. It is currently not clear however what the exact consequences would be of different contact-density functions in fluctuating populations. By combining field data on rodent host (Mastomys natalensis) demography, experimentally-derived contact-density data, and laboratory and field data Morogoro virus infection dynamics, we explored the effects of different contact-density function shapes on transmission dynamics and invasion/persistence. While invasion and persistence were clearly affected by the shape of the function, the effects on outbreak characteristics such as infection prevalence and seroprevalence were less obvious. This means that it may be difficult to distinguish between the different shapes based on how well models fit to real data. The shape of the transmission-density function should therefore be chosen with care, and is ideally based on existing information such as a previously quantified contact- or transmission-density relationship or the underlying biology of the host species in relation to the infectious agent.

epidemiology↗