Biology subjects

Zarebski, A. E.

Publications and source records attributed to Zarebski, A. E..

6 recordsLinked to original sources

bioRxiv2026

MxSure: a mixture model for inferring within-host substitution rates and transmission SNP thresholds

Quantifying short-term evolutionary rates of microbial genomes is essential for understanding the processes that shape within-host evolution and for establishing thresholds needed to track transmission. In studies of short-term evolutionary rates, samples are often collected from closely related clusters (e.g. longitudinally from the same host or from transmission pairs), with substantial time intervals separating genomes between clusters. Distinguishing strain replacement from persistence presents is also difficult in these studies. In addition, many public health and metagenomic bacterial strain tracking pipelines output pairwise SNP distances rather than the multiple sequence alignments required by common substitution rate estimation pipelines. This makes it hard to estimate within-host evolutionary rates in many commensal bacterial species that are difficult to culture and isolate. To address these challenges, we introduce MxSure, a tool for estimating substitution rates and transmission thresholds while accounting for strain replacement from pairwise SNP distance data, as commonly generated by transmission tracking and metagenomic analysis pipelines. We demonstrate the accuracy of MxSure through extensive simulations and by analysing species with previously estimated substitution rates from longitudinal metagenomic datasets. Using MxSure, we estimated within-host substitution rates and transmission SNP thresholds for multiple commensal bacterial species including Bifidobacterium longum and Bifidobacterium bifidum from a longitudinal study of the infant gut microbiome.

bioinformatics↗

bioRxiv2024

Enhanced metagenomics-enabled transmission inference with TRACS

Coexisting strains of the same species within the human microbiota pose a substantial challenge to inferring the host-to-host transmission of both pathogenic and commensal microbes. Here, we present TRACS, a highly accurate algorithm for estimating genetic distances between strains at the level of individual SNPs, which is robust to intra-species diversity within the host. Analysis of well-characterised Faecal Microbiota Transplantation datasets, along with extensive simulations, demonstrates that TRACS substantially outperforms existing strain aware transmission inference methods. We use TRACS to infer transmission networks in patients colonised with multiple strains, including SARS-CoV-2 amplicon sequencing data from UK hospitals, deep population sequencing data of Streptococcus pneumoniae and single-cell genome sequencing data from malaria patients infected with Plasmodium falciparum. Applying TRACS to gut metagenomic samples from a large cohort of 176 mothers and 1,288 infants born in UK hospitals revealed species-specific transmission rates between mothers and their infants. Notably, TRACS identified increased persistence of Bifidobacterium breve in infants, a finding missed by previous analyses due to the presence of multiple strains.

bioinformatics↗

bioRxiv2020

A computationally tractable birth-death model that combines phylogenetic and epidemiological data

Inferring the dynamics of pathogen transmission during an outbreak is an important problem in both infectious disease epidemiology. In mathematical epidemiology, estimates are often informed by time series of confirmed cases, while in phylodynamics genetic sequences of the pathogen, sampled through time, are the primary data source. Each data type provides different, and potentially complementary, insight; recent studies have recognised that combining data sources can improve estimates of the transmission rate and number of infected individuals. However, inference methods are typically highly specialised and field-specific and are either computationally prohibitive or require intensive simulation, limiting their real-time utility. We present a novel birth-death phylogenetic model and derive a tractable analytic approximation of its likelihood, the computational complexity of which is linear in the size of the dataset. This approach combines epidemiological and phylodynamic data to produce estimates of key parameters of transmission dynamics and the number of unreported infections. Using simulated data we show (a) that the approximation agrees well with existing methods, (b) validate the claim of linear complexity and (c) explore robustness to model misspecification. This approximation facilitates inference on large datasets, which is increasingly important as large genomic sequence datasets become commonplace. Author summaryMathematical epidemiologists typically studies time series of cases, ie the epidemic curve, to understand the spread of pathogens. Genetic epidemiologists study similar problems but do so using measurements of the genetic sequence of the pathogen which also contain information about the transmission process. There have been many attempts to unite these approaches so that both data sources can be utilised. However, striking a suitable balance between model flexibility and fidelity, in a way that is computationally tractable, has proven challenging; there are several competing methods but for large datasets they are intractable. As sequencing of pathogen genomes becomes more common, and an increasing amount of epidemiological data is collected, this situation will only be exacerbated. To bridge the gap between the time series and genomic methods we developed an approximation scheme, called TimTam, which can accurately and efficiently estimate key features of an epidemic such as the prevalence of the infection and the effective reproduction number, ie how many people are currently infected and the degree to which the infection is spreading.

genomics↗

bioRxiv2019

A sub-exponential branching process to study early epidemic dynamics with application to Ebola

Exponential growth is a mathematically convenient model for the early stages of an outbreak of an infectious disease. However, for many pathogens (such as Ebola virus) the initial rate of transmission may be sub-exponential, even before transmission is affected by depletion of susceptible individuals.\n\nWe present a stochastic multi-scale model capable of representing sub-exponential transmission: an in-homogeneous branching process extending the generalised growth model. To validate the model, we fit it to data from the Ebola epidemic in West Africa (2014-2016). We demonstrate how a branching process can be fit to both time series of confirmed cases and chains of infection derived from contact tracing. Our estimates of the parameters suggest transmission of Ebola virus was sub-exponential during this epidemic. Both the time series data and the chains of infections lead to consistent parameter estimates. Differences in the data sets meant consistent estimates were not a foregone conclusion. Finally, we use a simulation study to investigate the properties of our methodology. In particular, we examine the extent to which the estimates obtained from time series data and those obtained from chains of infection data agree.\n\nOur method, based on a simple branching process, is well suited to real-time analysis of data collected during contact tracing. Identifying the characteristic early growth dynamics (exponential or sub-exponential), including an estimate of uncertainty, during the first phase of an epidemic should prove a useful tool for preliminary outbreak investigations.\n\nAuthor SummaryEpidemic forecasts have the potential to support public health decision making in outbreak scenarios for diseases such as Ebola and influenza. In particular, reliable predictions of future incidence data may guide surveillance and intervention responses. Existing methods for producing forecasts, based upon mechanistic transmission models, often make an implicit assumption that growth is exponential, at least while susceptible depletion remains negligible. However, empirical studies suggest that many infectious disease outbreaks display sub-exponential growth early in the epidemic. Here we introduce a mechanistic model of early epidemic growth that allows for sub-exponential growth in incidence. We demonstrate how the model can be applied to the types of data that are typically available in (near) real-time, including time series data on incidence as well as individual-level case series and chains of transmission data. We apply our methods to publically available data from the 2014-2016 West Africa Ebola epidemic and demonstrate that early epidemic growth was sub-exponential. We also investigate the statistical properties of our model through a simulation re-estimation study to identify it performance characteristics and avenues for further methodological research.

bioinformatics↗

bioRxiv2019

Efferocytosis perpetuates substance accumulation inside macrophage populations

In both cells and animals, cannibalism can transfer harmful substances from the consumed to the consumer. Macrophages are immune cells that consume their own dead via a process called cannibalistic efferocytosis. Macrophages that contain harmful substances are found at sites of chronic inflammation, yet the role of cannibalism in this context remains unexplored. Here we take mathematical and experimental approaches to study the relationship between cannibalistic efferocytosis and substance accumulation in macrophages. Through mathematical modelling, we deduce that substances which transfer between individuals through cannibalism will concentrate inside the population via a coalescence process. This prediction was confirmed for macrophage populations inside a closed system. We used image analysis of whole slide photomicrographs to measure both latex microbead and neutral lipid accumulation inside murine bone marrow-derived macrophages (104 - 105 cells) following their stimulation into an inflammatory state ex vivo. While the total number of phagocytosed beads remained constant, cell death reduced cell numbers and efferocytosis concentrated the beads among the surviving macrophages. Since lipids are also conserved during efferocytosis, these cells accumulated lipid derived from the membranes of dead and consumed macrophages (becoming macrophage foam cells). Consequently, enhanced macrophage cell death increased the rate and extent foam cell formation. Our results demonstrate that cannibalistic efferocytosis perpetuates exogenous (e.g. beads) and endogenous (e.g. lipids) substance accumulation inside macrophage populations. As such, cannibalism has similar detrimental consequences in both cells and animals.

immunology↗

bioRxiv2018

Accounting for healthcare-seeking behaviours and testing practices in real-time influenza forecasts

For diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., in order to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for RT-PCR influenza tests. In this study we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data.

epidemiology↗