Biology subjects

Scire, J.

Publications and source records attributed to Scire, J..

4 recordsLinked to original sources

bioRxiv2022

Robust phylodynamic analysis of genetic sequencing data from structured populations

The multi-type birth-death model with sampling is a phylodynamic model which enables quantification of past population dynamics in structured populations, based on phylogenetic trees. The BEAST 2 package bdmm implements an algorithm for numerically computing the probability density of a phylogenetic tree given the population dynamic parameters under this model. In the initial release of bdmm, analyses were limited computationally to trees consisting of up to approximately 250 genetic samples. We implemented important algorithmic changes to bdmm which dramatically increase the number of genetic samples that can be analyzed, and improve the numerical robustness and efficiency of the calculations. Including more samples leads to improved precision of parameter estimates, particularly for structured models with a high number of inferred parameters. Furthermore, we report on several model extensions to bdmm, inspired by properties common to empirical datasets. We apply this improved algorithm to two partly overlapping datasets of Influenza A virus HA sequences sampled around the world, one with 500 samples, the other with only 175, for comparison. We report and compare the global migration patterns and seasonal dynamics inferred from each dataset. In that way, we show what information is gained by analyzing the bigger dataset which became possible with the presented algorithmic changes to bdmm. In summary, bdmm allows for robust, faster and more general phylodynamic inference of larger datasets.

evolutionary biology↗

bioRxiv2021

Disentangling the role of poultry farms and wild birds in the spread of highly pathogenic avian influenza virus H5N8 in Europe

Recent outbreaks of highly pathogenic avian influenza H5N8 virus in Europe have caused severe damage to animal health, wildlife conservation and livestock economic sustainability. While epidemiological and phylogenetic studies have generated important clues about the virus spread in Europe, they remained opaque to the specific role of poultry farms and wild birds. Using a phylodynamic framework, we inferred the H5N8 virus transmission dynamics among poultry farms and wild birds in four severely affected countries and investigated drivers of spread between farms across borders during the 2016-17 epidemic. Based on existing genetic data, we showed that the virus was likely introduced into poultry farms during the autumn, in line with the timing of arrival of migratory wild birds. Then, transmission was mainly driven by farm-to-farm transmission in Germany, Hungary and Poland, suggesting that better understanding of how infected farms are connected in those countries would greatly help control efforts. In contrast, the epidemic was dominated by wild bird-to-farm transmission in Czech Republic, meaning that more sustainable prevention strategies should be developed to reduce virus exposure from wild birds. We inferred effective reproduction number Re estimates among poultry farms and wild birds. We expect those estimates being useful to parameterize predictive models of virus spread aiming at optimising control strategies. None of the investigated predictors related to live poultry trade, poultry census and geographic proximity were identified as supportive predictors of the viral spread between farms across borders, suggesting that other drivers should be considered in future studies. Significance statementIn winter 2016-17, Europe was severely hit by an unprecedented epidemic of highly pathogenic avian influenza (HPAI) H5N8 virus, causing significant impact on animal health, wildlife conservation and livestock economic sustainability. By applying phylodynamic tools to H5N8 sequence data collected from poultry farms and wild birds during the epidemic, we quantified how effectively the first infections were detected, how fast the virus spread, how many infections were missed and how many transmission events occurred at the wildlife-domestic interface. Also, we investigated predictors of the virus spread between farms across borders. These results are crucial to better understand the virus transmission dynamics, with the view to inform policy decision-making and reduce the impact of future epidemics of HPAI viruses.

ecology↗

bioRxiv2020

Improved multi-type birth-death phylodynamic inference in BEAST 2

The multi-type birth-death model with sampling is a phylodynamic model which enables quantification of past population dynamics in structured populations, based on phylogenetic trees. The BEAST 2 package bdmm implements an algorithm for numerically computing the probability density of a phylogenetic tree given the population dynamic parameters under this model. In the initial release of bdmm, analyses were limited to trees consisting of up to approximately 250 genetic samples for numerical reasons. We implemented important algorithmic changes to bdmm which dramatically increase the number of genetic samples that can be analyzed, and improve the numerical robustness and efficiency of the calculations. Being able to use bigger datasets leads to improved precision of parameter estimates. Furthermore, we report on several model extensions to bdmm, inspired by properties common to empirical datasets. We apply this improved algorithm to two partly overlapping datasets of Influenza A virus HA sequences sampled around the world, one with 500 samples, the other with only 175, for comparison. We report and compare the global migration patterns and seasonal dynamics inferred from each dataset. AvailabilityThe latest release with our updates, bdmm 0.3.5, is freely available as an open access package of BEAST 2. The source code can be accessed at https://github.com/denisekuehnert/bdmm.

evolutionary biology↗

bioRxiv2018

Aggressive or moderate drug therapy for infectious diseases? Trade-offs between different treatment goals at the individual and population levels

Antimicrobial resistance is one of the major public health threats of the 21st century. There is a pressing need to adopt more efficient treatment strategies in order to prevent the emergence and spread of resistant strains. The common approach is to treat patients with high drug doses, both to clear the infection quickly and to reduce the risk of de novo resistance. Recently, several studies have argued that, at least in some cases, low-dose treatments could be more suitable to reduce the within-host emergence of antimicrobial resistance. However, the choice of a drug dose may have consequences at the population level, which has received little attention so far.\n\nHere, we study the influence of the drug dose on resistance and disease management at the host and population levels. We develop a nested two-strain model and unravel trade-offs in treatment benefits between an individual and the community. We use several measures to evaluate the benefits of any dose choice. Two measures focus on the emergence of resistance, at the host level and at the population level. The other two focus on the overall treatment success: the outbreak probability and the disease burden. We find that different measures can suggest different dosing strategies. In particular, we identify situations where low doses minimize the risk of emergence of resistance at the individual level, while high or intermediate doses prove most beneficial to improve the treatment efficiency or even to reduce the risk of resistance in the population.\n\nAuthor summaryThe obvious goals of antimicrobial drug therapy are rapid patient recovery and low disease prevalence in the population. However, achieving these goals is complicated by the rapid evolution and spread of antimicrobial resistance. A sustainable treatment strategy needs to account for the risk of resistance and keep it in check. One parameter of treatment is the drug dosage, which can vary within certain limits. It has been proposed that lower doses may, in some cases, be more suitable than higher doses to reduce the risk of resistance evolution in any one patient. However, if lower doses prolong the period of infectiousness, such a strategy has consequences for the pathogen dynamics of both strains at the population level. Here, we set up a nested model of within-host and between-host dynamics for an acute self-limiting infection. We explore the consequences of drug dosing on several measures of treatment success: the risk of resistance at the individual and population levels and the outbreak probability and the disease burden of an epidemic. Our analysis shows that trade-offs may exist between optimal treatments under these various criteria. The criterion given most weight in the decision process ultimately depends on the disease and population under consideration.

epidemiology↗