Biology subjects

Baguelin, M.

Publications and source records attributed to Baguelin, M..

4 recordsLinked to original sources

bioRxiv2018

Bayesian coalescent inference of in-host evolution using Next Generation Sequencing

Within an infected individual, influenza virus exists as a heterogeneous population of variants. When representing the viral population as a consensus sequence, information about minority variants is lost. However, using next generation sequencing (NGS), it is possible to identify nucleotide substitutions which segregate at low frequencies in the viral population, and can give insight into the within-host processes that drive the viruss evolution, and is a step towards understanding the dynamics of the disease. During the course of an infection, mutations may occur, and at each segregating site, the frequency of the derived allele in the population will fluctuate. We develop a method which can use information about the relative frequencies of mutations in NGS data from a viral population sampled at multiple time points, to infer past population dynamics with a Bayesian skyline model. By using coalescent theory, we analytically derive the joint allele frequency spectrum for a population across multiple time points, and relate this to the coalescent intervals generated from the skyline model. We demonstrate the model on data taken from populations of equine influenza virus sampled during an infection, and show that it is possible to infer a posterior distribution of effective viral population size through time. We also show how the model can be used to infer the probability that a mutation occurred within-host, as opposed to being an ancestral mutation which occurred prior to infection.\n\nAuthor SummaryWhen a host is infected by a virus, many particles of the infecting agent enter the body of the host. This viral population is composed of many closely related viruses that continue diversifying by mutating while reproducing in the host. New sequencing technologies allow the quantifying of the proportion of the different variants present in the host at a particular time. Unfortunately, the data resulting from such sequencing techniques are difficult to interpret as they consist of many unlinked copies of relatively small fragments of genetic code distributed along the genome of the virus.\n\nWe designed a method combining models of virus genealogies and frequency of mutations appearing in the data to reconstruct the variation of the viral population inside the host. It also allows us to time the apparition of particular variants. This could be useful to detect if a particular mutation (e.g. providing drug resistance) has appeared in host or was circulating before. We applied our method to data of within-host evolution of equine influenza.

bioinformatics↗

bioRxiv2018

Estimates for quality of life loss due to RSV

A number of vaccines against Respiratory Syncytial Virus (RSV) infection are approaching licensure. Deciding which RSV vaccine strategy, if any, to introduce, will partly depend on cost-effectiveness analyses, which compares the relative costs and health benefits of a potential vaccination programme. Health benefits are usually measured in Quality Adjusted Life Year (QALY) loss, however, there are no QALY loss estimates for RSV that have been determined using standardised instruments. Moreover, in children under the age of five years in whom severe RSV episodes predominantly occur, there are no appropriate standardised instruments to estimate QALY loss. We estimated the QALY loss due to RSV across all ages by developing a novel regression model which predicts the QALY loss without the use of standardised instruments. To do this, we conducted a surveillance study which targeted confirmed episodes in children under the age of five years (confirmed cases) and their household members who experienced symptoms of RSV during the same time (suspected cases.) All participants were asked to complete questions regarding their health during the infection, with the suspected cases aged 5-14 and 15+ years old additionally providing Health-Related Quality of Life (HR-QoL) loss estimates through completing EQ-5D-3L-Y and EQ-5D-3L instruments respectively. The questionnaire responses from the suspected cases were used to calibrate the regression model. The calibrated regression model then used other questionnaire responses to predict the HR-QoL loss without the use of EQ-5D instruments. The age-specific QALY loss was then calculated by multiplying the HR-QoL loss on the worst day predicted from the regression model, with estimates for the duration of infection from the questionnaires and a scaling factoring for disease severity. Our regression model for predicting HR-QoL loss estimates that for the worst day of infection, suspected RSV cases in persons five years and older who do and do not seek healthcare have an HR-QoL loss of 0{middle dot}616 (95% CI 0{middle dot}155-1{middle dot}371) and 0{middle dot}405 (95% CI 0{middle dot}111-1{middle dot}137) respectively. This leads to a QALY loss per RSV episode of 1{middle dot}950 x 10-3 (95% CI 0{middle dot}185 x 10-3 -9{middle dot}578 x 10-3) and 1{middle dot}543 x 10-3 (95% CI 0{middle dot}136 x 10-3 -6{middle dot}406 x 10-3) respectively. For confirmed cases in a child under the age of five years who sought healthcare, our model predicted a HR-QoL loss on the worst day of infection of 0{middle dot}820 (95% CI 0{middle dot}222-1{middle dot}450) resulting in a QALY loss per RSV episode of 3{middle dot}823 x 10-3 (95% CI 0{middle dot}492 x 10-3 -12{middle dot}766 x 10-3). Combing these results with previous estimates of RSV burden in the UK, we estimate the annual QALY loss of healthcare seeking RSV episodes as 1,199 for individuals aged five years and over and 1,441 for individuals under five years old. The QALY loss due to an RSV episode is less than the QALY loss due to an Influenza episode. These results have important implications for potential RSV vaccination programmes, which has so far focused on preventing infections in infants--where the highest reported disease burden lies. Future potential RSV vaccination programmes should also evaluate their impact on older children and adults, where there is a substantial but unsurveilled QALY loss.

epidemiology↗

bioRxiv2017

Influenza Interaction with Cocirculating Pathogens, and Its Impact on Surveillance, Pathogenesis and Epidemic Profile: A Key Role for Mathematical Modeling

Evidence is mounting that influenza virus, a major contributor to the global disease burden, interacts with other pathogens infecting the human respiratory tract. Taking into account interactions with other pathogens may be critical to determining the real influenza burden and the full impact of public health policies targeting influenza. That necessity is particularly true for mathematical modeling studies, which have become critical in public health decision-making, despite their usually focusing on lone influenza virus acquisition and infection, thereby making broad oversimplifications regarding pathogen ecology. Herein, we review evidence of influenza virus interaction with bacteria and viruses, and the modeling studies that incorporated some of these. Despite the many studies examining possible associations between influenza and Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Neisseria meningitides, respiratory syncytial virus, human rhinoviruses, human parainfluenza viruses, etc., very few mathematical models have integrated other pathogens alongside influenza. A notable exception is the recent modeling of the pneumococcus-influenza interaction, which highlighted potential influenza-related increased pneumococcal transmission and pathogenicity. That example demonstrates the power of dynamic modeling as an approach to test biological hypotheses concerning interaction mechanisms and estimate the strength of those interactions. We explore how different interference mechanisms may lead to unexpected incidence trends and misinterpretations. Using simple transmission models, we illustrate how existing interactions might impact public health surveillance systems and demonstrate that the development of multipathogen models is essential to assess the true public health burden of influenza, and help improve planning and evaluation of control measures. Finally, we identify the public health needs, surveillance, modeling and biological challenges, and propose avenues of research for the coming years.\n\nAuthor SummaryInfluenza is a major pathogen responsible for important morbidity and mortality burdens worldwide. Mathematical models of influenza virus acquisition have been critical to understanding its epidemiology and planning public health strategies of infection control. It is increasingly clear that microbes do not act in isolation but potentially interact within the host. Hence, studying influenza alone may lead to masking effects or misunderstanding information on its transmission and severity. Herein, we review the literature on bacterial and viral species that interact with the influenza virus, interaction mechanisms, and mathematical modeling studies integrating interactions. We report evidence that, beyond the classic secondary bacterial infections, many pathogenic bacteria and viruses probably interact with influenza. Public health relevance of pathogen interactions is detailed, showing how potential misreading or a narrow outlook might lead to mistaken public health decisionmaking. We describe the role of mechanistic transmission models in investigating this complex system and obtaining insight into interactions between influenza and other pathogens. Finally, we highlight benefits and challenges in modeling, and speculate on new opportunities made possible by taking a broader view: including basic science, clinical relevance and public health.

epidemiology↗

bioRxiv2017

Vaccination of health care workers to control Ebola virus disease

BackgroundHealth care workers (HCW) are at risk of infection during Ebola virus disease outbreaks and therefore may be targeted for vaccination before or during outbreaks. The effect of these strategies depends on the role of HCW in transmission which is understudied.\n\nMethodsTo evaluate the effect of HCW-targeted or community vaccination strategies, we used a transmission model to explore the relative contribution of HCW and the community to transmission. We calibrated the model to data from multiple Ebola outbreaks. We quantified the impact of ahead-of-time HCW-targeted strategies, and reactive HCW and community vaccination.\n\nResultsWe found that for some outbreaks (we call \"type 1\") HCW amplified transmission both to other HCW and the community, and in these outbreaks prophylactic vaccination of HCW decreased outbreak size. Reactive vaccination strategies had little effect because type 1 outbreaks ended quickly. However, in outbreaks with longer time courses (\"type 2 outbreaks\"), reactive community vaccination decreased the number of cases, with or without prophylactic HCW-targeted vaccination. For both outbreak types, we found that ahead-of-time HCW-targeted strategies had an impact at coverage of 30%.\n\nConclusionsThe optimal vaccine strategy depends on the dynamics of the outbreak and the impact of other interventions on transmission. Although we will not know the characteristics of a new outbreak, ahead-of-time HCW-targeted vaccination can decrease the total outbreak size, even at low vaccine coverage.\n\nsummaryTargeting health care workers for Ebola virus disease vaccination can decrease the size of outbreaks, and the number of health care workers infected. The impact of these strategies decrease depends on timing, coverage, and the dynamics of the outbreak.

epidemiology↗