Publications
157 results found
Agua-Agum J, Ariyarajah A, Aylward B, et al., 2015, West African Ebola epidemic after one year - slowing but not yet under control, New England Journal of Medicine, Vol: 372, Pages: 584-587, ISSN: 1533-4406
Nhung NT, Cuong NV, Campbell J, et al., 2015, High Levels of Antimicrobial Resistance among <i>Escherichia coli</i> Isolates from Livestock Farms and Synanthropic Rats and Shrews in the Mekong Delta of Vietnam, APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Vol: 81, Pages: 812-820, ISSN: 0099-2240
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- Citations: 52
Dye C, 2015, Goal-Directed Resuscitation in Septic Shock, NEW ENGLAND JOURNAL OF MEDICINE, Vol: 372, Pages: 189-189, ISSN: 0028-4793
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- Citations: 11
Tong SYC, Holden MTG, Nickerson EK, et al., 2015, Genome sequencing defines phylogeny and spread of methicillin-resistant <i>Staphylococcus aureus</i> in a high transmission setting, GENOME RESEARCH, Vol: 25, Pages: 111-118, ISSN: 1088-9051
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- Citations: 72
WHO Ebola Response Team, 2014, Ebola virus disease in West Africa — The first 9 months of the epidemic and forward projections, New England Journal of Medicine, Vol: 371, Pages: 1481-1495, ISSN: 0028-4793
BACKGROUNDOn March 23, 2014, the World Health Organization (WHO) was notified of an outbreak of Ebola virus disease (EVD) in Guinea. On August 8, the WHO declared the epidemic to be a “public health emergency of international concern.”METHODSBy September 14, 2014, a total of 4507 probable and confirmed cases, including 2296 deaths from EVD (Zaire species) had been reported from five countries in West Africa — Guinea, Liberia, Nigeria, Senegal, and Sierra Leone. We analyzed a detailed subset of data on 3343 confirmed and 667 probable Ebola cases collected in Guinea, Liberia, Nigeria, and Sierra Leone as of September 14.RESULTSThe majority of patients are 15 to 44 years of age (49.9% male), and we estimate that the case fatality rate is 70.8% (95% confidence interval [CI], 69 to 73) among persons with known clinical outcome of infection. The course of infection, including signs and symptoms, incubation period (11.4 days), and serial interval (15.3 days), is similar to that reported in previous outbreaks of EVD. On the basis of the initial periods of exponential growth, the estimated basic reproduction numbers (R0) are 1.71 (95% CI, 1.44 to 2.01) for Guinea, 1.83 (95% CI, 1.72 to 1.94) for Liberia, and 2.02 (95% CI, 1.79 to 2.26) for Sierra Leone. The estimated current reproduction numbers (R) are 1.81 (95% CI, 1.60 to 2.03) for Guinea, 1.51 (95% CI, 1.41 to 1.60) for Liberia, and 1.38 (95% CI, 1.27 to 1.51) for Sierra Leone; the corresponding doubling times are 15.7 days (95% CI, 12.9 to 20.3) for Guinea, 23.6 days (95% CI, 20.2 to 28.2) for Liberia, and 30.2 days (95% CI, 23.6 to 42.3) for Sierra Leone. Assuming no change in the control measures for this epidemic, by November 2, 2014, the cumulative reported numbers of confirmed and probable cases are predicted to be 5740 in Guinea, 9890 in Liberia, and 5000 in Sierra Leone, exceeding 20,000 in total.CONCLUSIONSThese data indicate that without drastic improvements in control measures, the numbers of
Devillard S, Jombart T, Leger F, et al., 2014, How reliable are morphological and anatomical characters to distinguish European wildcats, domestic cats and their hybrids in France?, JOURNAL OF ZOOLOGICAL SYSTEMATICS AND EVOLUTIONARY RESEARCH, Vol: 52, Pages: 154-162, ISSN: 0947-5745
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- Citations: 22
Jombart T, Aanensen DM, Baguelin M, et al., 2014, OutbreakTools: A new platform for disease outbreak analysis using the R software, Epidemics, Vol: 7, Pages: 28-34, ISSN: 1755-4365
The investigation of infectious disease outbreaks relies on the analysis of increasingly complex and diverse data, which offer new prospects for gaining insights into disease transmission processes and informing public health policies. However, the potential of such data can only be harnessed using a number of different, complementary approaches and tools, and a unified platform for the analysis of disease outbreaks is still lacking. In this paper, we present the new R package OutbreakTools, which aims to provide a basis for outbreak data management and analysis in R. OutbreakTools is developed by a community of epidemiologists, statisticians, modellers and bioinformaticians, and implements classes and methods for storing, handling and visualizing outbreak data. It includes real and simulated outbreak datasets. Together with a number of tools for infectious disease epidemiology recently made available in R, OutbreakTools contributes to the emergence of a new, free and open-source platform for the analysis of disease outbreaks.
Jombart T, Cori A, Didelot X, et al., 2014, Bayesian Reconstruction of Disease Outbreaks by Combining Epidemiologic and Genomic Data, PLOS COMPUTATIONAL BIOLOGY, Vol: 10, ISSN: 1553-734X
Tanabe K, Jombart T, Horibe S, et al., 2013, <i>Plasmodium falciparum</i> mitochondrial genetic diversity exhibits isolation-by-distance patterns supporting a sub-Saharan African origin, MITOCHONDRION, Vol: 13, Pages: 630-636, ISSN: 1567-7249
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- Citations: 14
Huyse T, Van den Broeck F, Jombart T, et al., 2013, Regular treatments of praziquantel do not impact on the genetic make-up of <i>Schistosoma mansoni</i> in Northern Senegal, INFECTION GENETICS AND EVOLUTION, Vol: 18, Pages: 100-105, ISSN: 1567-1348
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- Citations: 16
Chadeau-Hyam M, Campanella G, Jombart T, et al., 2013, Deciphering the complex: Methodological overview of statistical models to derive OMICS-based biomarkers, ENVIRONMENTAL AND MOLECULAR MUTAGENESIS, Vol: 54, Pages: 542-557, ISSN: 0893-6692
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- Citations: 90
Tanabe K, Mita T, Palacpac NMQ, et al., 2013, Within-population genetic diversity of <i>Plasmodium falciparum</i> vaccine candidate antigens reveals geographic distance from a Central sub-Saharan African origin, VACCINE, Vol: 31, Pages: 1334-1339, ISSN: 0264-410X
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- Citations: 26
Cui Y, Yu C, Yan Y, et al., 2013, Historical variations in mutation rate in an epidemic pathogen, <i>Yersinia pestis</i>, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 110, Pages: 577-582, ISSN: 0027-8424
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- Citations: 278
Muenkemueller T, Lavergne S, Bzeznik B, et al., 2012, How to measure and test phylogenetic signal, METHODS IN ECOLOGY AND EVOLUTION, Vol: 3, Pages: 743-756, ISSN: 2041-210X
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- Citations: 641
Dray S, Pelissier R, Couteron P, et al., 2012, Community ecology in the age of multivariate multiscale spatial analysis, ECOLOGICAL MONOGRAPHS, Vol: 82, Pages: 257-275, ISSN: 0012-9615
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- Citations: 435
Yalcindag E, Elguero E, Arnathau C, et al., 2012, Multiple independent introductions of <i>Plasmodium falciparum</i> in South America, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 109, Pages: 511-516, ISSN: 0027-8424
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- Citations: 75
Dray S, Jombart T, 2011, REVISITING GUERRY'S DATA: INTRODUCING SPATIAL CONSTRAINTS IN MULTIVARIATE ANALYSIS, ANNALS OF APPLIED STATISTICS, Vol: 5, Pages: 2278-2299, ISSN: 1932-6157
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- Citations: 31
Jombart T, Ahmed I, 2011, <i>adegenet 1.3-1</i>: new tools for the analysis of genome-wide SNP data, BIOINFORMATICS, Vol: 27, Pages: 3070-3071, ISSN: 1367-4803
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- Citations: 1916
Rasmussen M, Guo X, Wang Y, et al., 2011, An Aboriginal Australian Genome Reveals Separate Human Dispersals into Asia, SCIENCE, Vol: 334, Pages: 94-98, ISSN: 0036-8075
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- Citations: 449
Jombart T, Eggo RM, Dodd PJ, et al., 2011, Reconstructing disease outbreaks from genetic data: a graph approach, HEREDITY, Vol: 106, Pages: 383-390, ISSN: 0018-067X
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- Citations: 102
Morelli G, Song Y, Mazzoni CJ, et al., 2010, <i>Yersinia pestis</i> genome sequencing identifies patterns of global phylogenetic diversity, NATURE GENETICS, Vol: 42, Pages: 1140-+, ISSN: 1061-4036
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- Citations: 382
Jombart T, Devillard S, Balloux F, 2010, Discriminant analysis of principal components: a new method for the analysis of genetically structured populations, BMC GENETICS, Vol: 11, ISSN: 1471-2156
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- Citations: 2073
Jombart T, Balloux F, Dray S, 2010, <i>adephylo</i>: new tools for investigating the phylogenetic signal in biological traits, BIOINFORMATICS, Vol: 26, Pages: 1907-1909, ISSN: 1367-4803
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- Citations: 279
Tanabe K, Mita T, Jombart T, et al., 2010, <i>Plasmodium falciparum</i> Accompanied the Human Expansion out of Africa, CURRENT BIOLOGY, Vol: 20, Pages: 1283-1289, ISSN: 0960-9822
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- Citations: 107
Jombart T, Pavoine S, Devillard S, et al., 2010, Putting phylogeny into the analysis of biological traits: A methodological approach, JOURNAL OF THEORETICAL BIOLOGY, Vol: 264, Pages: 693-701, ISSN: 0022-5193
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- Citations: 49
Nuebel U, Dordel J, Kurt K, et al., 2010, A Timescale for Evolution, Population Expansion, and Spatial Spread of an Emerging Clone of Methicillin-Resistant <i>Staphylococcus aureus</i>, PLOS PATHOGENS, Vol: 6, ISSN: 1553-7366
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- Citations: 132
Balloux F, Handley L-JL, Jombart T, et al., 2009, Climate shaped the worldwide distribution of human mitochondrial DNA sequence variation, PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 276, Pages: 3447-3455, ISSN: 0962-8452
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- Citations: 104
Jombart T, Eggo RM, Dodd P, et al., 2009, Spatiotemporal dynamics in the early stages of the 2009 A/H1N1 influenza pandemic., PLoS Curr, Vol: 1
Epidemiology and public health planning will increasingly rely on the analysis of genetic sequence data. The ongoing influenza A/H1N1 pandemic may represent a tipping point in this trend, with A/H1N1 being the first human pathogen routinely genotyped from the beginning of its spread. To take full advantage of this genetic information, we introduce a novel method to reconstruct the spatiotemporal dynamics of outbreaks from sequence data. The approach is based on a new paradigm were ancestries are inferred directly rather than through the reconstruction of most recent common ancestors (MRCAs) as in phylogenetics. Using 279 A/H1N1 hemagglutinin (HA) sequences, we confirm the emergence of the 2009 flu pandemic in Mexico. The virus initially spread to the US, and then to the rest of the world with both Mexico and the US acting as the main sources. While compatible with current epidemiological understanding of the 2009 H1N1 pandemic, our results provide a much finer picture of the spatiotemporal dynamics. The results also highlight how much additional epidemiological information can be gathered from genetic monitoring of a disease outbreak.
Fraser C, Donnelly CA, Cauchemez S, et al., 2009, Influenza: Making Privileged Data Public Response, SCIENCE, Vol: 325, Pages: 1072-1073, ISSN: 0036-8075
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- Citations: 2
Fraser C, Donnelly CA, Cauchemez S, et al., 2009, Pandemic Potential of a Strain of Influenza A (H1N1): Early Findings, SCIENCE, Vol: 324, Pages: 1557-1561, ISSN: 0036-8075
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- Citations: 1393
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