Imperial College London

DrThibautJombart

Faculty of MedicineSchool of Public Health

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 3658t.jombart Website

 
 
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Location

 

UG11Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
to

80 results found

Polonsky JA, Baidjoe A, Kamvar ZN, Cori A, Durski K, Edmunds WJ, Eggo RM, Funk S, Kaiser L, Keating P, de Waroux OLP, Marks M, Moraga P, Morgan O, Nouvellet P, Ratnayake R, Roberts CH, Whitworth J, Jombart Tet al., 2019, Outbreak analytics: a developing data science for informing the response to emerging pathogens, Philosophical Transactions B: Biological Sciences, Vol: 374, ISSN: 0962-8436

Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens.This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control‘. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

Journal article

Leiva C, Taboada S, Kenny NJ, Combosch D, Giribet G, Jombart T, Riesgo Aet al., 2019, Population substructure and signals of divergent adaptive selection despite admixture in the sponge Dendrilla antarctica from shallow waters surrounding the Antarctic Peninsula., Mol Ecol

Antarctic shallow-water invertebrates are exceptional candidates to study population genetics and evolution, because of their peculiar evolutionary history and adaptation to extreme habitats that expand and retreat with the ice sheets. Among them, sponges are one of the major components, yet population connectivity of none of their many Antarctic species has been studied. To investigate gene flow, local adaptation and resilience to near-future changes caused by global warming, we sequenced 62 individuals of the sponge Dendrilla antarctica along the Western Antarctic Peninsula (WAP) and the South Shetlands (spanning ~900 km). We obtained information from 577 double digest restriction site-associated DNA sequencing (ddRADseq)-derived single nucleotide polymorphism (SNP), using RADseq techniques for the first time with shallow-water sponges. In contrast to other studies in sponges, our 389 neutral SNPs data set showed high levels of gene flow, with a subtle substructure driven by the circulation system of the studied area. However, the 140 outlier SNPs under positive selection showed signals of population differentiation, separating the central-southern WAP from the Bransfield Strait area, indicating a divergent selection process in the study area despite panmixia. Fourteen of these outliers were annotated, being mostly involved in immune and stress responses. We suggest that the main selective pressure on D. antarctica might be the difference in the planktonic communities present in the central-southern WAP compared to the Bransfield Strait area, ultimately depending on sea-ice control of phytoplankton blooms. Our study unveils an unexpectedly long-distance larval dispersal exceptional in Porifera, broadening the use of genome-wide markers within nonmodel Antarctic organisms.

Journal article

Jombart T, Kamvar ZN, Cai J, Pulliam J, Chisholm S, Fitzjohn R, Schumacher J, Bhatia Set al., 2019, reconhub/incidence: Incidence version 1.7.0

Incidence can now handle standardised weeks starting on any day thanks to the aweek package :tada:library(incidence)library(ggplot2)library(cowplot)d <- as.Date("2019-03-11") + -7:6setNames(d, weekdays(d))#> Monday Tuesday Wednesday Thursday Friday #> "2019-03-04" "2019-03-05" "2019-03-06" "2019-03-07" "2019-03-08" #> Saturday Sunday Monday Tuesday Wednesday #> "2019-03-09" "2019-03-10" "2019-03-11" "2019-03-12" "2019-03-13" #> Thursday Friday Saturday Sunday #> "2019-03-14" "2019-03-15" "2019-03-16" "2019-03-17"imon <- incidence(d, "mon week") # also ISO weekitue <- incidence(d, "tue week")iwed <- incidence(d, "wed week")ithu <- incidence(d, "thu week")ifri <- incidence(d, "fri week")isat <- incidence(d, "sat week")isun <- incidence(d, "sun week") # also MMWR week and EPI weekpmon <- plot(imon, show_cases = TRUE, labels_week = FALSE)ptue <- plot(itue, show_cases = TRUE, labels_week = FALSE)pwed <- plot(iwed, show_cases = TRUE, labels_week = FALSE)pthu <- plot(ithu, show_cases = TRUE, labels_week = FALSE)pfri <- plot(ifri, show_cases = TRUE, labels_week = FALSE)psat <- plot(isat, show_cases = TRUE, labels_week = FALSE)psun <- plot(isun, show_cases = TRUE, labels_week = FALSE)s <- scale_x_date(limits = c(as.Date("2019-02-26"), max(d) + 7L))plot_grid(pmon + s,ptue + s,pwed + s,pthu + s,pfri + s,psat + s,psun + s)multi-weeks/months/years can now be handledlibrary(incidence)library(outbreaks)d <- ebola_sim_clean$linelist$date_of_onseth <- ebola_sim_clean$linelist$hospitalplot(incidence(d, interval = "1 epiweek", group = h))plot(incidence(d, interval = "2 epiweeks", group = h))plot(incide

Software

Campbell F, Cori A, Ferguson N, Jombart Tet al., 2019, Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data, PLOS COMPUTATIONAL BIOLOGY, Vol: 15

Journal article

Dighe A, Jombart T, van Kerkhove M, Ferguson Net al., 2019, A mathematical model of the transmission of middle East respiratory syndrome coronavirus in dromedary camels (Camelus dromedarius), Publisher: ELSEVIER SCI LTD, Pages: 1-1, ISSN: 1201-9712

Conference paper

Kamvar ZN, Cai J, Pulliam JRC, Schumacher J, Jombart Tet al., 2019, Epidemic curves made easy using the R package incidence., F1000Research, Vol: 8, ISSN: 2046-1402

The epidemiological curve (epicurve) is one of the simplest yet most useful tools used by field epidemiologists, modellers, and decision makers for assessing the dynamics of infectious disease epidemics. Here, we present the free, open-source package incidence for the R programming language, which allows users to easily compute, handle, and visualise epicurves from unaggregated linelist data. This package was built in accordance with the development guidelines of the R Epidemics Consortium (RECON), which aim to ensure robustness and reliability through extensive automated testing, documentation, and good coding practices. As such, it fills an important gap in the toolbox for outbreak analytics using the R software, and provides a solid building block for further developments in infectious disease modelling. incidence is available from https://www.repidemicsconsortium.org/incidence.

Journal article

Kamvar Z, Cai J, Pulliam JRC, Schumacher J, Jombart Tet al., Epidemic curves made easy using the R package incidence [version 1; referees: awaiting peer review], F1000Research, Vol: 8, ISSN: 2046-1402

The epidemiological curve (epicurve) is one of the simplest yet most useful tools used by field epidemiologists, modellers, and decision makers for assessing the dynamics of infectious disease epidemics. Here, we present the free, open-source package incidence for the R programming language, which allows users to easily compute, handle, and visualise epicurves from unaggregated linelist data. This package was built in accordance with the development guidelines of the R Epidemics Consortium (RECON), which aim to ensure robustness and reliability through extensive automated testing, documentation, and good coding practices. As such, it fills an important gap in the toolbox for outbreak analytics using the R software, and provides a solid building block for further developments in infectious disease modelling. incidence is available from https://www.repidemicsconsortium.org/incidence.

Journal article

Jombart T, Kamvar ZN, Cai J, Pulliam J, Chisholm S, Fitzjohn R, Schumacher J, Bhatia Set al., 2019, reconhub/incidence 1.5

☣:chart_with_upwards_trend::chart_with_downwards_trend:☣ Compute and visualise incidence

Software

Cori A, Nouvellet P, Garske T, Bourhy H, Nakouné E, Jombart Tet al., 2018, A graph-based evidence synthesis approach to detecting outbreak clusters: An application to dog rabies, PLoS Computational Biology, Vol: 14, ISSN: 1553-734X

Early assessment of infectious disease outbreaks is key to implementing timely and effective control measures. In particular, rapidly recognising whether infected individuals stem from a single outbreak sustained by local transmission, or from repeated introductions, is crucial to adopt effective interventions. In this study, we introduce a new framework for combining several data streams, e.g. temporal, spatial and genetic data, to identify clusters of related cases of an infectious disease. Our method explicitly accounts for underreporting, and allows incorporating preexisting information about the disease, such as its serial interval, spatial kernel, and mutation rate. We define, for each data stream, a graph connecting all cases, with edges weighted by the corresponding pairwise distance between cases. Each graph is then pruned by removing distances greater than a given cutoff, defined based on preexisting information on the disease and assumptions on the reporting rate. The pruned graphs corresponding to different data streams are then merged by intersection to combine all data types; connected components define clusters of cases related for all types of data. Estimates of the reproduction number (the average number of secondary cases infected by an infectious individual in a large population), and the rate of importation of the disease into the population, are also derived. We test our approach on simulated data and illustrate it using data on dog rabies in Central African Republic. We show that the outbreak clusters identified using our method are consistent with structures previously identified by more complex, computationally intensive approaches.

Journal article

Jombart T, Kamvar Z, Cai J, Chisholm S, Fitzjohn R, Schumacher J, Bhatia Set al., 2018, reconhub/incidence: Incidence version 1.5.3

This is a patch release that fixes an issue with handling single-group incidence curves.You can install this version like so:remotes::install_github("reconhub/incidence@1.5.3")

Software

Campbell F, Didelot X, Fitzjohn R, Ferguson N, Cori A, Jombart Tet al., 2018, outbreaker2: a modular platform for outbreak reconstruction, BMC Bioinformatics, Vol: 19, ISSN: 1471-2105

Background:Reconstructing individual transmission events in an infectious disease outbreak can provide valuable information and help inform infection control policy. Recent years have seen considerable progress in the development of methodologies for reconstructing transmission chains using both epidemiological and genetic data. However, only a few of these methods have been implemented in software packages, and with little consideration for customisability and interoperability. Users are therefore limited to a small number of alternatives, incompatible tools with fixed functionality, or forced to develop their own algorithms at considerable personal effort.Results:Here we present outbreaker2, a flexible framework for outbreak reconstruction. This R package re-implements and extends the original model introduced with outbreaker, but most importantly also provides a modular platform allowing users to specify custom models within an optimised inferential framework. As a proof of concept, we implement the within-host evolutionary model introduced with TransPhylo, which is very distinct from the original genetic model in outbreaker, and demonstrate how even complex model results can be successfully included with minimal effort.Conclusions:outbreaker2provides a valuable starting point for future outbreak reconstruction tools, and represents a unifying platform that promotes customisability and interoperability. Implemented in the R software, outbreaker2joins a growing body of tools for outbreak analysis

Journal article

Nagraj VP, Randhawa N, Campbell F, Crellen T, Sudre B, Jombart Tet al., 2018, epicontacts: Handling, visualisation and analysis of epidemiological contacts, f1000research Open for Science

Epidemiological outbreak data is often captured in line list and contact format to facilitate contact tracing for outbreak control. epicontacts is an R package that provides a unique data structure for combining these data into a single object in order to facilitate more efficient visualisation and analysis. The package incorporates interactive visualisation functionality as well as network analysis techniques. Originally developed as part of the Hackout3 event, it is now developed, maintained and featured as part of the R Epidemics Consortium (RECON). The package is available for download from the Comprehensive R Archive Network (CRAN) and GitHub .

Journal article

Nagraj VP, Randhawa N, Campbell F, Crellen T, Sudre B, Jombart Tet al., 2018, epicontacts: Handling, visualisation and analysis of epidemiological contacts, F1000Research, ISSN: 2046-1402

Epidemiological outbreak data is often captured in line list and contact format to facilitate contact tracing for outbreak control. epicontacts is an R package that provides a unique data structure for combining these data into a single object in order to facilitate more efficient visualisation and analysis. The package incorporates interactive visualisation functionality as well as network analysis techniques. Originally developed as part of the Hackout3 event, it is now developed, maintained and featured as part of the R Epidemics Consortium (RECON). The package is available for download from the Comprehensive R Archive Network (CRAN) and GitHub .

Journal article

Moraga P, Dorigatti I, Kamvar Z, Piatkowski P, Toikkanen S, Nagraj VP, Donnelly C, Jombart Tet al., 2018, epiflows : an R package for risk assessment of travel- related spread of disease [version 1; referees: 2 approved with reservations], F1000Research, Vol: 7, ISSN: 2046-1402

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease. epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.

Journal article

Beugin M-P, Gayet T, Pontier D, Devillard S, Jombart Tet al., 2018, A fast likelihood solution to the genetic clustering problem, Methods in Ecology and Evolution, Vol: 9, Pages: 1006-1016, ISSN: 2041-210X

The investigation of genetic clusters in natural populations is an ubiquitous problem in a range of fields relying on the analysis of genetic data, such as molecular ecology, conservation biology and microbiology. Typically, genetic clusters are defined as distinct panmictic populations, or parental groups in the context of hybridisation. Two types of methods have been developed for identifying such clusters: model-based methods, which are usually computer-intensive but yield results which can be interpreted in the light of an explicit population genetic model, and geometric approaches, which are less interpretable but remarkably faster.Here, we introduce snapclust, a fast maximum-likelihood solution to the genetic clustering problem, which allies the advantages of both model-based and geometric approaches. Our method relies on maximising the likelihood of a fixed number of panmictic populations, using a combination of geometric approach and fast likelihood optimisation, using the Expectation-Maximisation (EM) algorithm. It can be used for assigning genotypes to populations and optionally identify various types of hybrids between two parental populations. Several goodness-of-fit statistics can also be used to guide the choice of the retained number of clusters.Using extensive simulations, we show that snapclust performs comparably to current gold standards for genetic clustering as well as hybrid detection, with some advantages for identifying hybrids after several backcrosses, while being orders of magnitude faster than other model-based methods. We also illustrate how snapclust can be used for identifying the optimal number of clusters, and subsequently assign individuals to various hybrid classes simulated from an empirical microsatellite dataset.snapclust is implemented in the package adegenet for the free software R, and is therefore easily integrated into existing pipelines for genetic data analysis. It can be applied to any kind of co-dominant markers, and ca

Journal article

Dupuis JR, Bremer FT, Jombart T, Sim SB, Geib SMet al., 2018, mvmapper: Interactive spatial mapping of genetic structures, Molecular Ecology Resources, Vol: 18, Pages: 362-367, ISSN: 1755-098X

Characterizing genetic structure across geographic space is a fundamental challenge in population genetics. Multivariate statistical analyses are powerful tools for summarizing genetic variability, but geographic information and accompanying metadata are not always easily integrated into these methods in a user-friendly fashion. Here, we present a deployable Python-based web-tool, mvmapper, for visualizing and exploring results of multivariate analyses in geographic space. This tool can be used to map results of virtually any multivariate analysis of georeferenced data, and routines for exporting results from a number of standard methods have been integrated in the R package adegenet, including principal components analysis (PCA), spatial PCA, discriminant analysis of principal components, principal coordinates analysis, nonmetric dimensional scaling and correspondence analysis. mvmapper's greatest strength is facilitating dynamic and interactive exploration of the statistical and geographic frameworks side by side, a task that is difficult and time-consuming with currently available tools. Source code and deployment instructions, as well as a link to a hosted instance of mvmapper, can be found at https://popphylotools.github.io/mvMapper/.

Journal article

Campbell F, Strang C, Ferguson N, Cori A, Jombart Tet al., 2018, When are pathogen genome sequences informative of transmission events?, PLoS Pathogens, Vol: 14, ISSN: 1553-7366

Recent years have seen the development of numerous methodologies for reconstructing transmission trees in infectious disease outbreaks from densely sampled whole genome sequence data. However, a fundamental and as of yet poorly addressed limitation of such approaches is the requirement for genetic diversity to arise on epidemiological timescales. Specifically, the position of infected individuals in a transmission tree can only be resolved by genetic data if mutations have accumulated between the sampled pathogen genomes. To quantify and compare the useful genetic diversity expected from genetic data in different pathogen outbreaks, we introduce here the concept of ‘transmission divergence’, defined as the number of mutations separating whole genome sequences sampled from transmission pairs. Using parameter values obtained by literature review, we simulate outbreak scenarios alongside sequence evolution using two models described in the literature to describe transmission divergence of ten major outbreak-causing pathogens. We find that while mean values vary significantly between the pathogens considered, their transmission divergence is generally very low, with many outbreaks characterised by large numbers of genetically identical transmission pairs. We describe the impact of transmission divergence on our ability to reconstruct outbreaks using two outbreak reconstruction tools, the R packages outbreaker and phybreak, and demonstrate that, in agreement with previous observations, genetic sequence data of rapidly evolving pathogens such as RNA viruses can provide valuable information on individual transmission events. Conversely, sequence data of pathogens with lower mean transmission divergence, including Streptococcus pneumoniae, Shigella sonnei and Clostridium difficile, provide little to no information about individual transmission events. Our results highlight the informational limitations of genetic sequence data in certain outbreak scenarios, and

Journal article

Nagraj VP, Randhawa N, Campbell F, Crellen T, Sudre B, Jombart Tet al., 2018, epicontacts: Handling, visualisation and analysis of epidemiological contacts., F1000Research, Vol: 7, ISSN: 2046-1402

Epidemiological outbreak data is often captured in line list and contact format to facilitate contact tracing for outbreak control. epicontacts is an R package that provides a unique data structure for combining these data into a single object in order to facilitate more efficient visualisation and analysis. The package incorporates interactive visualisation functionality as well as network analysis techniques. Originally developed as part of the Hackout3 event, it is now developed, maintained and featured as part of the R Epidemics Consortium (RECON). The package is available for download from the Comprehensive R Archive Network (CRAN) and GitHub.

Journal article

Paradis E, Gosselin T, Grunwald NJ, Jombart T, Manel S, Lapp Het al., 2017, Towards an integrated ecosystem of R packages for the analysis of population genetic data, MOLECULAR ECOLOGY RESOURCES, Vol: 17, Pages: 1-4, ISSN: 1755-098X

Journal article

Montano V, Jombart T, 2017, An Eigenvalue Test for spatial Principal Component Analysis, BMC Bioinformatics, Vol: 18, ISSN: 1471-2105

BackgroundThe spatial Principal Component Analysis (sPCA, Jombart (Heredity 101:92-103, 2008) is designed to investigate non-random spatial distributions of genetic variation. Unfortunately, the associated tests used for assessing the existence of spatial patterns (global and local test; (Heredity 101:92-103, 2008) lack statistical power and may fail to reveal existing spatial patterns. Here, we present a non-parametric test for the significance of specific patterns recovered by sPCA.ResultsWe compared the performance of this new test to the original global and local tests using datasets simulated under classical population genetic models. Results show that our test outperforms the original global and local tests, exhibiting improved statistical power while retaining similar, and reliable type I errors. Moreover, by allowing to test various sets of axes, it can be used to guide the selection of retained sPCA components.ConclusionsAs such, our test represents a valuable complement to the original analysis, and should prove useful for the investigation of spatial genetic patterns.

Journal article

Jombart T, Kendall M, Almagro-Garcia J, Colijn Cet al., 2017, Treespace: statistical exploration of landscapes of phylogenetic trees, Molecular Ecology Resources, Vol: 17, Pages: 1385-1392, ISSN: 1755-0998

The increasing availability of large genomic data sets as well as the advent of Bayesian phylogenetics facilitates the investigation of phylogenetic incongruence, which can result in the impossibility of representing phylogenetic relationships using a single tree. While sometimes considered as a nuisance, phylogenetic incongruence can also reflect meaningful biological processes as well as relevant statistical uncertainty, both of which can yield valuable insights in evolutionary studies. We introduce a new tool for investigating phylogenetic incongruence through the exploration of phylogenetic tree landscapes. Our approach, implemented in the R package treespace, combines tree metrics and multivariate analysis to provide low-dimensional representations of the topological variability in a set of trees, which can be used for identifying clusters of similar trees and group-specific consensus phylogenies. treespace also provides a user-friendly web interface for interactive data analysis and is integrated alongside existing standards for phylogenetics. It fills a gap in the current phylogenetics toolbox in R and will facilitate the investigation of phylogenetic results.

Journal article

Cori A, Donnelly CA, dorigatti, ferguson NM, fraser, garske, jombart, Nedjati-Gilani G, Nouvellet, Riley, Van Kerkhove, Mills, Blake IMet al., 2017, Key data for outbreak evaluation: building on the Ebola experience, Philosophical Transactions of the Royal Society B: Biological Sciences, Vol: 372, ISSN: 1471-2970

Following the detection of an infectious disease outbreak, rapid epidemiological assessmentis critical to guidean effectivepublic health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained inthe West AfricanEbolaepidemic and prior emerging infectious disease outbreaksto set out a checklist of data needed to: 1) quantify severity and transmissibility;2) characterise heterogeneities in transmission and their determinants;and 3) assess the effectiveness of different interventions.We differentiate data needs into individual-leveldata (e.g. a detailed list of reported cases), exposure data(e.g.identifying where / howcases may have been infected) and populationlevel data (e.g.size/demographicsof the population(s)affected andwhen/where interventions were implemented). A remarkable amount of individual-level and exposuredata was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However,gaps in population-level data (particularly around which interventions were applied whenand where)posed challenges to the assessment of (3).Herewehighlight recurrent data issues, give practical suggestions for addressingthese issues and discuss priorities for improvements in data collection in future outbreaks.

Journal article

Garske T, Cori A, Ariyarajah A, Blake I, Dorigatti I, Eckmanns T, Fraser C, Hinsley W, Jombart T, Mills H, Nedjati-Gilani G, Newton E, Nouvellet P, Perkins D, Riley S, Schumacher D, Shah A, Van Kerkhove M, Dye C, Ferguson N, Donnelly Cet al., 2017, Heterogeneities in the case fatality ratio in the West African Ebola outbreak 2013 – 2016, Philosophical Transactions of the Royal Society B: Biological Sciences, Vol: 372, ISSN: 1471-2970

The 2013–2016 Ebola outbreak in West Africa is the largest on record with 28 616 confirmed, probable and suspected cases and 11 310 deaths officially recorded by 10 June 2016, the true burden probably considerably higher. The case fatality ratio (CFR: proportion of cases that are fatal) is a key indicator of disease severity useful for gauging the appropriate public health response and for evaluating treatment benefits, if estimated accurately. We analysed individual-level clinical outcome data from Guinea, Liberia and Sierra Leone officially reported to the World Health Organization. The overall mean CFR was 62.9% (95% CI: 61.9% to 64.0%) among confirmed cases with recorded clinical outcomes. Age was the most important modifier of survival probabilities, but country, stage of the epidemic and whether patients were hospitalized also played roles. We developed a statistical analysis to detect outliers in CFR between districts of residence and treatment centres (TCs), adjusting for known factors influencing survival and identified eight districts and three TCs with a CFR significantly different from the average. From the current dataset, we cannot determine whether the observed variation in CFR seen by district or treatment centre reflects real differences in survival, related to the quality of care or other factors or was caused by differences in reporting practices or case ascertainment.

Journal article

Nouvellet P, Cori A, Garske T, Blake IM, Dorigatti I, Hinsley W, Jombart T, Mills HL, Nedjati-Gilani G, Van Kerkhove MD, Fraser C, Donnelly CA, Ferguson NM, Riley Set al., 2017, A simple approach to measure transmissibility and forecast incidence, Epidemics, Vol: 22, Pages: 29-35, ISSN: 1755-4365

Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investments in reactive interventions, with consequent implementation plans sometimes revised on a weekly basis. Therefore, short-term forecasts of incidence are often of high priority. In light of the recent Ebola epidemic in West Africa, a forecasting exercise was convened by a network of infectious disease modellers. The challenge was to forecast unseen “future” simulated data for four different scenarios at five different time points. In a similar method to that used during the recent Ebola epidemic, we estimated current levels of transmissibility, over variable time-windows chosen in an ad hoc way. Current estimated transmissibility was then used to forecast near-future incidence. We performed well within the challenge and often produced accurate forecasts. A retrospective analysis showed that our subjective method for deciding on the window of time with which to estimate transmissibility often resulted in the optimal choice. However, when near-future trends deviated substantially from exponential patterns, the accuracy of our forecasts was reduced. This exercise highlights the urgent need for infectious disease modellers to develop more robust descriptions of processes – other than the widespread depletion of susceptible individuals – that produce non-exponential patterns of incidence.

Journal article

Bertranpetit E, Jombart T, Paradis E, Pena H, Dubey J, Su C, Mercier A, Devillard S, Ajzenberg Det al., 2016, Phylogeography of Toxoplasma gondii points to a South American origin, Infection, Genetics and Evolution, Vol: 48, Pages: 150-155, ISSN: 1567-1348

Toxoplasma gondii, a protozoan found ubiquitously in mammals and birds, is the etiologic agent of toxoplasmosis, a disease causing substantial public health burden worldwide, including about 200,000 new cases of congenital toxoplasmosis each year. Clinical severity has been shown to vary across geographical regions, with South America exhibiting the highest burden. Unfortunately, the drivers of these heterogeneities are still poorly understood, and the geographical origin and historical spread of the pathogen worldwide are currently uncertain. A worldwide sample of 168 T. gondii isolates gathered in 13 populations was sequenced for five fragments of genes (140 single nucleotide polymorphisms from 3153 bp per isolate). Phylogeny based on Maximum likelihood methods with estimation of the time to the most recent common ancestor (TMRCA) and geostatistical analyses were performed for inferring the putative origin of T. gondii. We show that extant strains of the pathogen likely evolved from a South American ancestor, around 1.5 million years ago, and reconstruct the subsequent spread of the pathogen worldwide. This emergence is much more recent than the appearance of ancestral T. gondii, believed to have taken place about 11 My ago, and follows the arrival of felids in this part of the world. We posit that an ancestral lineage of T. gondii likely arrived in South America with felids and that the evolution of oral infectivity through carnivorism and the radiation of felids in this region enabled a new strain to outcompete the ancestral lineage and undergo a pandemic radiation.

Journal article

International Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Bawo L, Bilivogui P, Blake IM, Brennan RJ, Cawthorne A, Cleary E, Clement P, Conteh R, Cori A, Dafae F, Dahl B, Dangou JM, Diallo B, Donnelly CA, Dorigatti I, Dye C, Eckmanns T, Fallah M, Ferguson NM, Fiebig L, Fraser C, Garske T, Gonzalez L, Hamblion E, Hamid N, Hersey S, Hinsley W, Jambei A, Jombart T, Kargbo D, Keita S, Kinzer M, George FK, Godefroy B, Gutierrez G, Kannangarage N, Mills HL, Moller T, Meijers S, Mohamed Y, Morgan O, Nedjati-Gilani G, Newton E, Nouvellet P, Nyenswah T, Perea W, Perkins D, Riley S, Rodier G, Rondy M, Sagrado M, Savulescu C, Schafer IJ, Schumacher D, Seyler T, Shah A, Van Kerkhove MD, Wesseh CS, Yoti Zet al., 2016, Exposure patterns driving Ebola transmissions in West Africa: a retrospective observational study, PLOS Medicine, Vol: 13, ISSN: 1549-1277

BACKGROUND: The ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved.METHODS AND FINDINGS: Over 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola ("cases") were asked if they had exposure to other potential Ebola cases ("potential source contacts") in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO's response during the epidemic, and have been updated for publication. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p < 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = -0.37, p < 0.001) between R and the district proportion of hospitalised cases admitted within ≤4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the

Journal article

Inns T, Ashton PM, Herrera-Leon S, Lighthill J, Foulkes S, Jombart T, Rehman Y, Fox A, Dallman T, De Pinna E, Browning L, Coia JE, Edeghere O, Vivancos Ret al., 2016, Prospective use of whole genome sequencing (WGS) detected a multi-country outbreak of Salmonella Enteritidis, EPIDEMIOLOGY AND INFECTION, Vol: 145, Pages: 289-298, ISSN: 0950-2688

Journal article

Clare FC, Halder JB, Daniel O, Bielby J, Semenov MA, Jombart T, Loyau A, Schmeller DS, Cunningham AA, Rowcliffe M, Garner TWJ, Bosch J, Fisher Met al., 2016, Climate forcing of an emerging pathogenic fungus across a montane multi-host community, Philosophical Transactions of the Royal Society B: Biological Sciences, Vol: 371, ISSN: 1471-2970

Changes in the timings of seasonality as a result of anthropogenic climate change are predicted to occur over the coming decades. While this is expected to have widespread impacts on the dynamics of infectious disease through environmental forcing, empirical data are lacking. Here, we investigated whether seasonality, specifically the timing of spring ice-thaw, affected susceptibility to infection by the emerging pathogenic fungus Batrachochytrium dendrobatidis (Bd) across a montane community of amphibians that are suffering declines and extirpations as a consequence of this infection. We found a robust temporal association between the timing of the spring thaw and Bd infection in two host species, where we show that an early onset of spring forced high prevalences of infection. A third highly susceptible species (the midwife toad, Alytes obstetricans) maintained a high prevalence of infection independent of time of spring thaw. Our data show that perennially overwintering midwife toad larvae may act as a year-round reservoir of infection with variation in time of spring thaw determining the extent to which infection spills over into sympatric species. We used future temperature projections based on global climate models to demonstrate that the timing of spring thaw in this region will advance markedly by the 2050s, indicating that climate change will further force the severity of infection. Our findings on the effect of annual variability on multi-host infection dynamics show that the community-level impact of fungal infectious disease on biodiversity will need to be re-evaluated in the face of climate change.

Journal article

Paradis E, Gosselin T, Goudet J, Jombart T, Schliep Ket al., 2016, Linking genomics and population genetics with R, Molecular Ecology Resources, Vol: 17, Pages: 54-66, ISSN: 1755-0998

Population genetics and genomics have developed and been treated as independent fields of study despite having common roots. The continuous progress of sequencing technologies is contributing to (re-)connect these two disciplines. We review the challenges faced by data analysts and software developers when handling very big genetic data sets collected on many individuals. We then expose how R, as a computing language and development environment, proposes some solutions to meet these challenges. We focus on some specific issues that are often encountered in practice: handling and analysing SNP data, handling and reading VCF files, analysing haplotypes and linkage disequilibrium, and performing multivariate analyses. We illustrate these implementations with some analyses of three recently published data sets that contain between 60,000 and 1,000,000 loci. We conclude with some perspectives on future developments of R software for population genomics. This article is protected by copyright. All rights reserved.

Journal article

Dallman T, Inns T, Jombart T, Ashton P, Loman N, Chatt C, Messelhaeusser U, Rabsch W, Simon S, Nikisins S, Bernard H, le Hello S, Jourdan da-Silva N, Kornschober C, Mossong J, Hawkey P, de Pinna E, Grant K, Cleary Pet al., 2016, Phylogenetic structure of European Salmonella Enteritidis outbreak correlates with national and international egg distribution network, Microbial Genomics, Vol: 2, Pages: e000070-e000070, ISSN: 2057-5858

Outbreaks of Salmonella Enteritidis have long been associated with contaminated poultry and eggs. In the summer of 2014 a large multi-national outbreak of Salmonella Enteritidis phage type 14b occurred with over 350 cases reported in the United Kingdom, Germany, Austria, France and Luxembourg. Egg supply network investigation and microbiological sampling identified the source to be a Bavarian egg producer. As part of the international investigation into the outbreak, over 400 isolates were sequenced including isolates from cases, implicated UK premises and eggs from the suspected source producer. We were able to show a clear statistical correlation between the topology of the UK egg distribution network and the phylogenetic network of outbreak isolates. This correlation can most plausibly be explained by different parts of the egg distribution network being supplied by eggs solely from independent premises of the Bavarian egg producer (Company X). Microbiological sampling from the source premises, traceback information and information on the interventions carried out at the egg production premises all supported this conclusion. The level of insight into the outbreak epidemiology provided by whole-genome sequencing (WGS) would not have been possible using traditional microbial typing methods.

Journal article

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