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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

The English version of a Viral Haemorrhagic Fever (VHF) Case Investigation form used to collect case data which was then entered into an electronic system. This was based on forms used in past epidemics. This version of the form was used throughout most of 2014 in Sierra Leone and Liberia.

International audience; Towards an integrated ecosystem of R packages for the analysis of population genetic data

Genetic sequences of multiple genes are becoming increasingly common for a wide range of organisms including viruses, bacteria, and Eucaryotes. While such data may sometimes be treated as a single locus, in practice a number of biological and statistical phenomena can lead to phylogenetic incongruence. In such cases different loci should, at least as a preliminary step, be examined and analysed separately. The R software has become a popular platform for phylogenetics, with several packages implementing distance-based, parsimony, and likelihood-based phylogenetic reconstruction, and an even greater number of packages implementing phylogenetic comparative methods. Unfortunately, basic data structures and tools for analysing multiple genes have so far been lacking, thereby limiting potential for investigating phylogenetic incongruence. In this paper, we introduce the new R package apex to fill this gap. apex implements new object classes which extend existing standards for storing DNA and amino-acid sequences, and provides a number of convenient tools for handling, visualizing, and analyzing these data. In this paper, we introduce the main features of the package and illustrate its functionalities through the analysis of a simple dataset. This article is protected by copyright. All rights reserved.

Electronic Supplementary Material 3 for Garske et al, 2016, Heterogeneities in the Case Fatality Ratio in the West African Ebola Outbreak 2013 - 2016, Phil. Trans. R. Soc. B. doi: 10.1098/rstb.2016.0308.

The authors acknowledge financial support from the UKCRC Translational Infection Research (TIR) Initiative and the Medical Research Council (Grant number G1000803), with contributions to the grant from the Biotechnology and Biological Sciences Research Council, the National Institute for Health Research on behalf of the Department of Health, and the Chief Scientist Office of the Scottish Government Health Directorate (to Professor Peacock); from Wellcome Trust grant number 098051 awarded to the Wellcome Trust Sanger Institute; and the NIHR Cambridge Biomedical Research Centre (to Professor Peacock). S.Y.C.T. is an Australian National Health and Medical Research Council Career Development Fellow (1065736). Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of nosocomial infection. Whole-genome sequencing of MRSA has been used to define phylogeny and transmission in well-resourced healthcare settings, yet the greatest burden of nosocomial infection occurs in resource-restricted settings where barriers to transmission are lower. Here, we study the flux and genetic diversity of MRSA on ward and individual patient levels in a hospital where transmission was common. We repeatedly screened all patients on two intensive care units for MRSA carriage over a 3-mo period. All MRSA belonged to multilocus sequence type 239 (ST 239). We defined the population structure and charted the spread of MRSA by sequencing 79 isolates from 46 patients and five members of staff, including the first MRSA-positive screen isolates and up to two repeat isolates where available. Phylogenetic analysis identified a flux of distinct ST 239 clades over time in each intensive care unit. In total, five main clades were identified, which varied in the carriage of plasmids encoding antiseptic and antimicrobial resistance determinants. Sequence data confirmed intra- and interwards transmission events and identified individual patients who were colonized by more than one clade. One patient on each unit was the source of numerous transmission events, and deep sampling of one of these cases demonstrated colonization with a "cloud" of related MRSA variants. The application of whole-genome sequencing and analysis provides novel insights into the transmission of MRSA in under-resourced healthcare settings and has relevance to wider global health. Publisher PDF

BACKGROUND\ud \ud On 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."\ud \ud METHODS\ud \ud By 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.\ud \ud RESULTS\ud \ud The 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 (R-0) 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.\ud \ud CONCLUSIONS\ud \ud These data indicate that without drastic improvements in control measures, the numbers of cases of and deaths from EVD are expected to continue increasing from hundreds to thousands per week in the coming months.

Recent technological advances in molecular biology have given rise to numerous large-scale datasets whose analysis imposes serious methodological challenges mainly relating to the size and complex structure of the data. Considerable experience in analyzing such data has been gained over the past decade, mainly in genetics, from the Genome-Wide Association Study era, and more recently in transcriptomics and metabolomics. Building upon the corresponding literature, we provide here a nontechnical overview of well-established methods used to analyze OMICS data within three main types of regression-based approaches: univariate models including multiple testing correction strategies, dimension reduction techniques, and variable selection models. Our methodological description focuses on methods for which ready-to-use implementations are available. We describe the main underlying assumptions, the main features, and advantages and limitations of each of the models. This descriptive summary constitutes a useful tool for driving methodological choices while analyzing OMICS data, especially in environmental epidemiology, where the emergence of the exposome concept clearly calls for unified methods to analyze marginally and jointly complex exposure and OMICS datasets.

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 case line-list. Linking cases to the contacts who potentially infected them provided information on the transmission network. This revealed a high degree of heterogeneity in inferred transmissions, with only 20% of cases accounting for at least 73% of new infections, a phenomenon often called super-spreading. Multivariable regression models allowed us to identify predictors of being named as a potential source contact. These were similar for funeral and non-funeral contacts: severe symptoms, death, non-hospitalisation, older age, and travelling prior to symptom onset. Non-funeral exposures were strongly peaked around the death of the contact. There was evidence that hospitalisation reduced but did not eliminate onward exposures. We found that Ebola treatment units were better than other health care facilities at preventing exposure from hospitalised and deceased individuals. The principal limitation of our analysis is limited data quality, with cases not being entered into the database, cases not reporting exposures, or data being entered incorrectly (especially dates, and possible misclassifications). Conclusions Achieving elimination of Ebola is challenging, partly because of super-spreading. Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. Continued real-time data capture, reporting, and analysis are vital to track transmission patterns, inform resource deployment, and thus hasten and maintain elimination of the virus from the human population., In this retrospective observational study, Christophe Fraser and colleagues analyze the exposure patterns associated with Ebola transmission during the 2013-2016 epidemic in West Africa., Author Summary Why Was This Study Done? Knowing how and from whom individuals acquire infection can help inform the response to limit the impact of an epidemic; this study presents updated versions of analyses initially performed to assist the international response during the 2013–2016 Ebola epidemic in West Africa. Over 19,000 individuals with confirmed or probable Ebola (“cases”) were reported in West Africa by 4 May 2015. Cases were asked whether they had exposure to potential Ebola cases (“potential source contacts”) in a funeral or non-funeral context prior to becoming ill. What Did the Researchers Do and Find? 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. Non-funeral exposures were strongly peaked around the time of death of the contact. There was evidence of super-spreading, with only 20% of cases accounting for at least 73% of new infections. What Do These Findings Mean? Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. The data are highly detailed despite the challenging circumstances in the three countries; however, the analyses were limited by data quality, mostly missing data and incorrect entries. In light of viral persistence in reservoirs, it is vital to maintain active surveillance and analysis of Ebola outbreaks to avoid and contain future outbreaks.

SUMMARYSince April 2015, whole genome sequencing (WGS) has been the routine test forSalmonellaidentification, surveillance and outbreak investigation at the national reference laboratory in England and Wales. In May 2015, an outbreak ofSalmonellaEnteritidis cases was detected using WGS data and investigated. UK cases were interviewed to obtain a food history and links between suppliers were mapped to produce a food chain network for chicken eggs. The association between the food chain network and the phylogeny was explored using a network comparison approach. Food and environmental samples were taken from premises linked to cases and tested forSalmonella. Within the outbreak single nucleotide polymorphism defined cluster, 136 cases were identified in the UK and 18 in Spain. One isolate from a food containing chicken eggs was within the outbreak cluster. There was a significant association between the chicken egg food chain of UK cases and phylogeny of outbreak isolates. This is the first publishedSalmonellaoutbreak to be prospectively detected using WGS. This outbreak in the UK was linked with contemporaneous cases in Spain by WGS. We conclude that UK and Spanish cases were exposed to a common source ofSalmonella-contaminated chicken eggs.

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. This article is part of the themed issue ‘Tackling emerging fungal threats to animal health, food security and ecosystem resilience’.

Between December 2013 and April 2016, the largest epidemic of Ebola virus disease (EVD) to date generated more than 28,000 cases and more than 11,000 deaths in the large, mobile populations of Guinea, Liberia, and Sierra Leone. Tracking the rapid rise and slower decline of the West African epidemic has reinforced some common understandings about the epidemiology and control of EVD but has also generated new insights. Despite having more information about the geographic distribution of the disease, the risk of human infection from animals and from survivors of EVD remains unpredictable over a wide area of equatorial Africa. Until human exposure to infection can be anticipated or avoided, future outbreaks will have to be managed with the classic approach to EVD control — extensive surveillance, rapid detection and diagnosis, comprehensive tracing of contacts, prompt patient isolation, supportive clinical care, rigorous efforts to prevent and control infection, safe and dignified burial, and engagement of the community. Empirical and modeling studies conducted during the West African epidemic have shown that large epidemics of EVD are preventable — a rapid response can interrupt transmission and restrict the size of outbreaks, even in densely populated cities. The critical question now is how to ensure that populations and their health services are ready for the next outbreak, wherever it may occur. Health security across Africa and beyond depends on committing resources to both strengthen national health systems and sustain investment in the next generation of vaccines, drugs, and diagnostics.

International audience; 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.

With more than 1,700 laboratory-confirmed infections, Middle East respiratory syndrome coronavirus (MERS-CoV) remains a significant threat for public health. However, the lack of detailed data on modes of transmission from the animal reservoir and between humans means that the drivers of MERS-CoV epidemics remain poorly characterized. Here, we develop a statistical framework to provide a comprehensive analysis of the transmission patterns underlying the 681 MERS-CoV cases detected in the Kingdom of Saudi Arabia (KSA) between January 2013 and July 2014. We assess how infections from the animal reservoir, the different levels of mixing, and heterogeneities in transmission have contributed to the buildup of MERS-CoV epidemics in KSA. We estimate that 12% [95% credible interval (CI): 9%, 15%] of cases were infected from the reservoir, the rest via human-to-human transmission in clusters (60%; CI: 57%, 63%), within (23%; CI: 20%, 27%), or between (5%; CI: 2%, 8%) regions. The reproduction number at the start of a cluster was 0.45 (CI: 0.33, 0.58) on average, but with large SD (0.53; CI: 0.35, 0.78). It was >1 in 12% (CI: 6%, 18%) of clusters but fell by approximately one-half (47% CI: 34%, 63%) its original value after 10 cases on average. The ongoing exposure of humans to MERS-CoV from the reservoir is of major concern, given the continued risk of substantial outbreaks in health care systems. The approach we present allows the study of infectious disease transmission when data linking cases to each other remain limited and uncertain.

International audience; 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 3153bp 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.

International audience; 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 single-nucleotide polymorphism data, handling and reading variant call format 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.

One of the UN sustainable development goals is to achieve universal access to safe and affordable drinking water by 2030. It is locations like Kathmandu, Nepal, a densely populated city in South Asia with endemic typhoid fever, where this goal is most pertinent. Aiming to understand the public health implications of water quality in Kathmandu we subjected weekly water samples from 10 sources for one year to a range of chemical and bacteriological analyses. We additionally aimed to detect the etiological agents of typhoid fever and longitudinally assess microbial diversity by 16S rRNA gene surveying. We found that the majority of water sources exhibited chemical and bacterial contamination exceeding WHO guidelines. Further analysis of the chemical and bacterial data indicated site-specific pollution, symptomatic of highly localized fecal contamination. Rainfall was found to be a key driver of this fecal contamination, correlating with nitrates and evidence of S. Typhi and S. Paratyphi A, for which DNA was detectable in 333 (77%) and 303 (70%) of 432 water samples, respectively. 16S rRNA gene surveying outlined a spectrum of fecal bacteria in the contaminated water, forming complex communities again displaying location-specific temporal signatures. Our data signify that the municipal water in Kathmandu is a predominant vehicle for the transmission of S. Typhi and S. Paratyphi A. This study represents the first extensive spatiotemporal investigation of water pollution in an endemic typhoid fever setting and implicates highly localized human waste as the major contributor to poor water quality in the Kathmandu Valley., Author Summary Aiming to understand the ecology of municipal drinking water and measure the potential exposure to pathogens that cause typhoid fever (Salmonella Typhi and Salmonella Paratyphi A) in Kathmandu, Nepal, we collected water samples from 10 water sources weekly for one year and subjected them to comprehensive chemical, bacteriological and molecular analyses. We found that Kathmandu drinking water exhibits longitudinal fecal contamination in excess of WHO guidelines. The chemical composition of water indicated site-specific pollution profiles, which were likely driven by localized contamination with human fecal material. We additionally found that Salmonella Typhi and Salmonella Paratyphi A could be detected throughout the year in every water sampling location, but specifically peaked after the monsoons. A microbiota analysis (a method for studying bacterial diversity in biological samples) revealed the water to be contaminated by complex populations of fecal bacteria, which again exhibited a unique profile by both location and time. This study shows that Salmonella Typhi and Salmonella Paratyphi A can be longitudinally detected in drinking water in Kathmandu and represents the first major investigation of the spatiotemporal dynamics of drinking water pollution in an endemic typhoid setting.

There is an ongoing discussion in the literature on whether human mitochondrial DNA (mtDNA) evolves neutrally. There have been previous claims for natural selection on human mtDNA based on an excess of non-synonymous mutations and higher evolutionary persistence of specific mitochondrial mutations in Arctic populations. However, these findings were not supported by the reanalysis of larger datasets. Using a geographical framework, we perform the first direct test of the relative extent to which climate and past demography have shaped the current spatial distribution of mtDNA sequences worldwide. We show that populations living in colder environments have lower mitochondrial diversity and that the genetic differentiation between pairs of populations correlates with difference in temperature. These associations were unique to mtDNA; we could not find a similar pattern in any other genetic marker. We were able to identify two correlated non-synonymous point mutations in the ND3 and ATP6 genes characterized by a clear association with temperature, which appear to be plausible targets of natural selection producing the association with climate. The same mutations have been previously shown to be associated with variation in mitochondrial pH and calcium dynamics. Our results indicate that natural selection mediated by climate has contributed to shape the current distribution of mtDNA sequences in humans.

Urban stray cat populations have previously been shown to be organized in moderately to strongly genetically differentiated colonies. However, the spatial pattern of this genetic differentiation and the possibility that some cryptic genetic structure occurs have not been investigated. Firstly, we combine a pairwise Fst method, a fully Bayesian clustering method and a multivariate analysis to show that the spatial structure of 17 urban stray cat colonies (Nancy, France) does not match the underlying genetic structure. These colonies are structured in two clusters at the uppermost hierarchical level. Additionally, geographic proximity between colonies does not explain their genetic homogeneity. The spatial pattern in genetic differentiation seems more to be a mixture between low global structure and some highly localized structure, comprising isolated colonies. Variations in the local ecological and social constraints on immigration between colonies may explain this pattern. Secondly, we show that the overall current immigration rate is low (8%). Our results suggest that dispersal does not play a major role in the process of homogenization of allelic frequencies. Our study provides a case-study on the use of Bayesian clustering and assignment methods on a real, small data set with numerous fragmented populations; a population structure that is of high relevance in conservation biology.

Increasing attention is being devoted to taking landscape information into account in genetic studies. Among landscape variables, space is often considered as one of the most important. To reveal spatial patterns, a statistical method should be spatially explicit, that is, it should directly take spatial information into account as a component of the adjusted model or of the optimized criterion. In this paper we propose a new spatially explicit multivariate method, spatial principal component analysis (sPCA), to investigate the spatial pattern of genetic variability using allelic frequency data of individuals or populations. This analysis does not require data to meet Hardy-Weinberg expectations or linkage equilibrium to exist between loci. The sPCA yields scores summarizing both the genetic variability and the spatial structure among individuals (or populations). Global structures (patches, clines and intermediates) are disentangled from local ones (strong genetic differences between neighbors) and from random noise. Two statistical tests are proposed to detect the existence of both types of patterns. As an illustration, the results of principal component analysis (PCA) and sPCA are compared using simulated datasets and real georeferenced microsatellite data of Scandinavian brown bear individuals (Ursus arctos). sPCA performed better than PCA to reveal spatial genetic patterns. The proposed methodology is implemented in the adegenet package of the free software R.

Graphical abstract, Highlights • We provide a well defined standard for the transmission of epidemiological data. • All standard computer primitive data-types are unambiguously handled. • Temporal and spatial data are stored in a standards compliant manner with case data, which may include other arbitrary data. • Data files and systems producing such files may be trivially validated. • An R package, repijson, provides easy conversion to and from this new format., Epidemiology relies on data but the divergent ways data are recorded and transferred, both within and between outbreaks, and the expanding range of data-types are creating an increasingly complex problem for the discipline. There is a need for a consistent, interpretable and precise way to transfer data while maintaining its fidelity. We introduce ‘EpiJSON’, a new, flexible, and standards-compliant format for the interchange of epidemiological data using JavaScript Object Notation. This format is designed to enable the widest range of epidemiological data to be unambiguously held and transferred between people, software and institutions. In this paper, we provide a full description of the format and a discussion of the design decisions made. We introduce a schema enabling automatic checks of the validity of data stored as EpiJSON, which can serve as a basis for the development of additional tools. In addition, we also present the R package ‘repijson’ which provides conversion tools between this format, line-list data and pre-existing analysis tools. An example is given to illustrate how EpiJSON can be used to store line list data. EpiJSON, designed around modern standards for interchange of information on the internet, is simple to implement, read and check. As such, it provides an ideal new standard for epidemiological, and other, data transfer to the fast-growing open-source platform for the analysis of disease outbreaks.

Ebola emerged in West Africa around December 2013 and swept through Guinea, Sierra Leone and Liberia, giving rise to 27,748 confirmed, probable and suspected cases reported by 29 July 2015. Case diagnoses during the epidemic have relied on polymerase chain reaction-based tests. Owing to limited laboratory capacity and local transport infrastructure, the delays from sample collection to test results being available have often been 2 days or more. Point-of-care rapid diagnostic tests offer the potential to substantially reduce these delays. We review Ebola rapid diagnostic tests approved by the World Health Organization and those currently in development. Such rapid diagnostic tests could allow early triaging of patients, thereby reducing the potential for nosocomial transmission. In addition, despite the lower test accuracy, rapid diagnostic test-based diagnosis may be beneficial in some contexts because of the reduced time spent by uninfected individuals in health-care settings where they may be at increased risk of infection; this also frees up hospital beds. We use mathematical modelling to explore the potential benefits of diagnostic testing strategies involving rapid diagnostic tests alone and in combination with polymerase chain reaction testing. Our analysis indicates that the use of rapid diagnostic tests with sensitivity and specificity comparable with those currently under development always enhances control, whether evaluated at a health-care-unit or population level. If such tests had been available throughout the recent epidemic, we estimate, for Sierra Leone, that their use in combination with confirmatory polymerase chain-reaction testing might have reduced the scale of the epidemic by over a third.

Ebola virus has caused substantial illness in West Africa in the past year. In this report, the burden of illness in children is described.

Despite efforts to control the outbreak of Ebola virus disease in West Africa, substantial transmission continues. As we reach the 1-year anniversary of the first known case from this outbreak, the WHO team updates the progress in controlling the epidemic.

In Mekong Delta farms (Vietnam), antimicrobials are extensively used, but limited data are available on levels of antimicrobial resistance (AMR) among Escherichia coli isolates. We performed a structured survey of AMR in E. coli isolates ( n = 434) from 90 pig, chicken, and duck farms. The results were compared with AMR among E. coli isolates ( n = 234) from 66 small wild animals (rats and shrews) trapped on farms and in forests and rice fields. The isolates were susceptibility tested against eight antimicrobials. E. coli isolates from farmed animals were resistant to a median of 4 (interquartile range [IQR], 3 to 6) antimicrobials versus 1 (IQR, 1 to 2) among wild mammal isolates ( P < 0.001). The prevalences of AMR among farmed species isolates (versus wild animals) were as follows: tetracycline, 84.7% (versus 25.6%); ampicillin, 78.9% (versus 85.9%); trimethoprim-sulfamethoxazole, 52.1% (versus 18.8%); chloramphenicol, 39.9% (versus 22.5%); amoxicillin-clavulanic acid, 36.6% (versus 34.5%); and ciprofloxacin, 24.9% (versus 7.3%). The prevalence of multidrug resistance (MDR) (resistance against three or more antimicrobial classes) among pig isolates was 86.7% compared to 66.9 to 72.7% among poultry isolates. After adjusting for host species, MDR was ∼8 times greater among isolates from wild mammals trapped on farms than among those trapped in forests/rice fields ( P < 0.001). Isolates were assigned to unique profiles representing their combinations of susceptibility results. Multivariable analysis of variance indicated that AMR profiles from wild mammals trapped on farms and those from domestic animals were more alike ( R 2 range, 0.14 to 0.30) than E. coli isolates from domestic animals and mammals trapped in the wild ( R 2 range, 0.25 to 0.45). The results strongly suggest that AMR on farms is a key driver of environmental AMR in the Mekong Delta.

Multidrug-resistant (MDR) Klebsiella pneumoniae has become a leading cause of nosocomial infections worldwide. Despite its prominence, little is known about the genetic diversity of K. pneumoniae in resource-poor hospital settings. Through whole-genome sequencing (WGS), we reconstructed an outbreak of MDR K. pneumoniae occurring on high-dependency wards in a hospital in Kathmandu during 2012 with a case-fatality rate of 75%. The WGS analysis permitted the identification of two MDR K. pneumoniae lineages causing distinct outbreaks within the complex endemic K. pneumoniae. Using phylogenetic reconstruction and lineage-specific PCR, our data predicted a scenario in which K. pneumoniae, circulating for 6 months before the outbreak, underwent a series of ward-specific clonal expansions after the acquisition of genes facilitating virulence and MDR. We suggest that the early detection of a specific NDM-1 containing lineage in 2011 would have alerted the high-dependency ward staff to intervene. We argue that some form of real-time genetic characterisation, alongside clade-specific PCR during an outbreak, should be factored into future healthcare infection control practices in both high- and low-income settings.

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. We are thankful to Tanja Stadler and two anonymous referees for their thorough reviews and useful comments. We thank Sourceforge (http://sourceforge.net/) for providing a great platform for software development. OutbreakTools has been created during "Hackout: a hackathon for the analysis of disease outbreaks in R" (http://sites.google.com/site/hackoutwiki/), an event funded by the Medical Research Council. T. Jombart and C. Collins are funded by the Medical Research Council and MIDAS. D. Aanensen is funded by the Wellcome Trust (grant 099202/Z/12/Z). M. Baguelin and A. Camacho are supported by the Wellcome Trust (project grant WR/094527). A. Camacho is also funded by the Medical Research Council (fellowship MR/J01432X/1). A. Cori thanks the NIH for funding through the NIAID cooperative agreement UM1 A1068619. M.A. Suchard is supported through National Science Foundation grants DMS 126153 and IIS 1251151. N. Hens acknowledges support from the University of Antwerp scientific chair in Evidence-Based Vaccinology, financed in 2009-2014 by a gift from Pfizer. O. Ratmann is supported by the Wellcome Trust (fellowship WR092311MF). C. Colijn acknowledges support from the Engineering and Physical Sciences Research Council (EP/I031626/1; EP/K026003/1). S. Frost is supported in part by the Royal Society, the ERSC (ES/J011266/1), and the MRC (MR/J013862/1).

Phenotypic variation in hybridizing species or subspecies is a prerequisite for allowing conservation ecologists and wildlife managers to identify parental populations and their hybrids in the field. We assessed the reliability of a set of eight morphological (body size and pelage characters) and four anatomical criteria (skull and intestine morphometric measurements) to distinguish between 302 French specimens classified as wildcat, domestic cat or hybrid on the basis of a Bayesian analysis (STRUCTURE) of their multilocus microsatellite genotypes. This aim was achieved by performing a set of multivariate analyses on morphological, anatomical and genetic data sets (Hill and Smith's analysis, co-inertia analysis and discriminant analysis of principal components). Wildcats and domestic cats were very satisfactorily distinguished, even when using simple non-invasive morphological criteria easily usable in the field like the morphology of the tail, dorsal line or flank stripes. Using anatomical instead of morphological characters slightly increased the discriminating power. Many more difficulties arose when we tried to distinguish hybrid specimens from both wildcat and domestic ones. Anatomical characters performed better than morphological ones in recognizing hybrids, but the assignment success rate remained very low, about 31.6% and 1.5%, respectively. Overall, the most discriminating characters were two continuous, derived anatomical characters: the cranial index followed by the intestinal index. Classification of specimens in three classes based on their microsatellite genotypes appeared to be inadequate for identifying hybrid specimens, as hybrid specimens seemed to be distributed along an anatomical continuum. With this observation in mind, we assessed the linear relationships between a proxy of the individual level of hybridization (qik) and the cranial and intestinal indices, respectively. Both relationships were highly significant. The greatest correlation was found with the cranial index (R² = 60.4%). Altogether, our results suggest that future work should be geared towards enhancing the measure of hybridization using more discriminating molecular markers and improving morphometric skull measurements through the use of modern geometric morphometric methods, using landmarks rather than skull dimension.

The geographical distribution of single nucleotide polymorphism (SNP) in the mitochondrial genome of the human malaria parasite Plasmodium falciparum was investigated. We identified 88 SNPs in 516 isolates from seven parasite populations in Africa, Southeast Asia and Oceania. Analysis of the SNPs postulated a sub-Saharan African origin and recovered a strong negative correlation between within-population SNP diversity and geographic distance from the putative African origin over Southeast Asia and Oceania. These results are consistent with those previously obtained for nuclear genome-encoded housekeeping genes, indicating that the pattern of inheritance does not substantially affect the geographical distribution of SNPs.

The Senegal River Basin (SRB) experienced a major epidemic of intestinal schistosomiasis in the early nineties, after the construction of a dam for irrigation purposes. Exceptionally low cure rates following praziquantel (PZQ) treatment at the onset of the epidemic raised concerns about PZQ resistant strains of Schistosoma mansoni, although they could also be attributed to the intense transmission at that time. A field study in the same region more than 15 years later found cure rates for S. mansoni still to be low, whereas Schistosoma haematobium responded well to treatment. We collected S. mansoni miracidia from children at base-line prior to treatment, six months after two PZQ treatments and two years after the start of the study when they had received a total of five PZQ treatments. In total, 434 miracidia from 12 children were successfully genotyped with at least six out of nine DNA microsatellite loci. We found no significant differences in the genetic diversity of, and genetic differentiation between parasite populations before and after repeated treatment, suggesting that PZQ treatment does not have an impact on the neutral evolution of the parasite. This is in stark contrast with a similar study in Tanzania where a significant decrease in genetic diversity was observed in S. mansoni miracidia after a single round of PZQ treatment. We argue that PZQ resistance might play a role in our study area, although rapid re-infection cannot be excluded. It is important to monitor this situation carefully and conduct larger field studies with short-term follow-up after treatment. Since PZQ is the only general schistosomicide available, the possibility of PZQ resistance is of great concern both for disease control and for curative use in clinical practice. © 2013 Elsevier B.V.

The genetic diversity of Yersinia pestis , the etiologic agent of plague, is extremely limited because of its recent origin coupled with a slow clock rate. Here we identified 2,326 SNPs from 133 genomes of Y. pestis strains that were isolated in China and elsewhere. These SNPs define the genealogy of Y. pestis since its most recent common ancestor. All but 28 of these SNPs represented mutations that happened only once within the genealogy, and they were distributed essentially at random among individual genes. Only seven genes contained a significant excess of nonsynonymous SNP, suggesting that the fixation of SNPs mainly arises via neutral processes, such as genetic drift, rather than Darwinian selection. However, the rate of fixation varies dramatically over the genealogy: the number of SNPs accumulated by different lineages was highly variable and the genealogy contains multiple polytomies, one of which resulted in four branches near the time of the Black Death. We suggest that demographic changes can affect the speed of evolution in epidemic pathogens even in the absence of natural selection, and hypothesize that neutral SNPs are fixed rapidly during intermittent epidemics and outbreaks.

Populations of Plasmodium falciparum , the most virulent human malaria parasite, are diverse owing to wide levels of transmission and endemicity of infection. Genetic diversity of P. falciparum antigens, within and between parasite populations, remains a confounding factor in malaria pathogenesis as well as clinical trials of vaccine candidates. Variation of target antigens in parasite populations may arise from immune pressure depending on the levels of acquired immunity. Alternatively, similar to our study in housekeeping genes [Tanabe et al. Curr Biol 2010;70:1–7], within-population genetic diversity of vaccine candidate antigens may also be determined by geographical distance from a postulated origin in Central sub-Saharan Africa. To address this question, we obtained full-length sequences of P. falciparum genes, apical membrane antigen 1 ( ama1 ) ( n = 459), circumsporozoite protein ( csp ) ( n = 472) and merozoite surface protein 1 ( msp1 ) ( n = 389) from seven geographically diverse parasite populations in Africa, Southeast Asia and Oceania; and, together with previously determined sequences ( n = 13 and 15 for csp and msp1 , respectively) analyzed within-population single nucleotide polymorphism (SNP) diversity. The three antigen genes showed SNP diversity that supports a model of isolation-by-distance. The standardized number of polymorphic sites per site, expressed as θ S , indicates that 77–83% can be attributed by geographic distance from the African origin, suggesting that geographic distance plays a significant role in variation in target vaccine candidate antigens. Furthermore, we observed that a large proportion of SNPs in the antigen genes were shared between African and non-African parasite populations, demonstrating long term persistence of those SNPs. Our results provide important implications for developing effective malaria vaccines and better understanding of acquired immunity against falciparum malaria.

Species spatial distributions are the result of population demography, behavioral traits, and species interactions in spatially heterogeneous environmental conditions. Hence the composition of species assemblages is an integrative response variable, and its variability can be explained by the complex interplay among several structuring factors. The thorough analysis of spatial variation in species assemblages may help infer processes shaping ecological communities. We suggest that ecological studies would benefit from the combined use of the classical statistical models of community composition data, such as constrained or unconstrained multivariate analyses of site‐by‐species abundance tables, with rapidly emerging and diversifying methods of spatial pattern analysis. Doing so allows one to deal with spatially explicit ecological models of beta diversity in a biogeographic context through the multiscale analysis of spatial patterns in original species data tables, including spatial characterization of fitted or residual variation from environmental models. We summarize here the recent progress for specifying spatial features through spatial weighting matrices and spatial eigenfunctions in order to define spatially constrained or scale‐explicit multivariate analyses. Through a worked example on tropical tree communities, we also show the potential of the overall approach to identify significant residual spatial patterns that could arise from the omission of important unmeasured explanatory variables or processes. This contribution originated from the SEDAR workshop partially funded by the University Lyon 1, the PRABI, and the LBBE, UMR CNRS 5558. This research was supported by grants ANR 07 BDIV 014‐03 to S. Dray, NSERC Discovery Grants to M. J. Fortin, P. Legendre, P. Peres‐Neto, and H. H. Wagner, MEDD 05 EcoTrop (OSDA) 0000223 to R. Pélissier, P. Couteron, and F. Munoz, and a Canada Research Chair to P. Peres‐Neto. F. G. Blanchet was supported by an NSERC Discovery Grant to Fangliang He.

The origin of Plasmodium falciparum in South America is controversial. Some studies suggest a recent introduction during the European colonizations and the transatlantic slave trade. Other evidence—archeological and genetic—suggests a much older origin. We collected and analyzed P. falciparum isolates from different regions of the world, encompassing the distribution range of the parasite, including populations from sub-Saharan Africa, the Middle East, Southeast Asia, and South America. Analyses of microsatellite and SNP polymorphisms show that the populations of P. falciparum in South America are subdivided in two main genetic clusters (northern and southern). Phylogenetic analyses, as well as Approximate Bayesian Computation methods suggest independent introductions of the two clusters from African sources. Our estimates of divergence time between the South American populations and their likely sources favor a likely introduction from Africa during the transatlantic slave trade.

Summary: While the R software is becoming a standard for the analysis of genetic data, classical population genetics tools are being challenged by the increasing availability of genomic sequences. Dedicated tools are needed for harnessing the large amount of information generated by next-generation sequencing technologies. We introduce new tools implemented in the adegenet 1.3-1 package for handling and analyzing genome-wide single nucleotide polymorphism (SNP) data. Using a bit-level coding scheme for SNP data and parallelized computation, adegenet enables the analysis of large genome-wide SNPs datasets using standard personal computers. Availability: adegenet 1.3-1 is available from CRAN: http://cran.r-project.org/web/packages/adegenet/. Information and support including a dedicated forum of discussion can be found on the adegenet website: http://adegenet.r-forge.r-project.org/. adegenet is released with a manual and four tutorials totalling over 300 pages of documentation, and distributed under the GNU General Public Licence (≥2). Contact: t.jombart@imperial.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online.

Standard multivariate analysis methods aim to identify and summarize the main structures in large data sets containing the description of a number of observations by several variables. In many cases, spatial information is also available for each observation, so that a map can be associated to the multivariate data set. Two main objectives are relevant in the analysis of spatial multivariate data: summarizing covariation structures and identifying spatial patterns. In practice, achieving both goals simultaneously is a statistical challenge, and a range of methods have been developed that offer trade-offs between these two objectives. In an applied context, this methodological question has been and remains a major issue in community ecology, where species assemblages (i.e., covariation between species abundances) are often driven by spatial processes (and thus exhibit spatial patterns). In this paper we review a variety of methods developed in community ecology to investigate multivariate spatial patterns. We present different ways of incorporating spatial constraints in multivariate analysis and illustrate these different approaches using the famous data set on moral statistics in France published by Andr\'{e}-Michel Guerry in 1833. We discuss and compare the properties of these different approaches both from a practical and theoretical viewpoint., Comment: Published in at http://dx.doi.org/10.1214/10-AOAS356 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Epidemiology and public health planning will increasingly rely on the analysis of genetic sequence data. In particular, genetic data coupled with dates and locations of sampled isolates can be used to reconstruct the spatiotemporal dynamics of pathogens during outbreaks. Thus far, phylogenetic methods have been used to tackle this issue. Although these approaches have proved useful for informing on the spread of pathogens, they do not aim at directly reconstructing the underlying transmission tree. Instead, phylogenetic models infer most recent common ancestors between pairs of isolates, which can be inadequate for densely sampled recent outbreaks, where the sample includes ancestral and descendent isolates. In this paper, we introduce a novel method based on a graph approach to reconstruct transmission trees directly from genetic data. Using simulated data, we show that our approach can efficiently reconstruct genealogies of isolates in situations where classical phylogenetic approaches fail to do so. We then illustrate our method by analyzing data from the early stages of the swine-origin A/H1N1 influenza pandemic. Using 433 isolates sequenced at both the hemagglutinin and neuraminidase genes, we reconstruct the likely history of the worldwide spread of this new influenza strain. The presented methodology opens new perspectives for the analysis of genetic data in the context of disease outbreaks.

Summary: adephylo is a package for the R software dedicated to the analysis of comparative evolutionary data. Phylogenetic comparative methods initially aimed at accounting for or removing the effects of phylogenetic signal in the analysis of biological traits. However, recent approaches have shown that considerable information can be gathered from the study of the phylogenetic signal. In particular, close examination of phylogenetic structures can unveil interesting evolutionary patterns. For this purpose, we developed the package adephylo that provides tools for quantifying and describing the phylogenetic structures of biological traits. adephylo implements tests of phylogenetic signal, phylogenetic distances and proximities, and novel methods for describing further univariate and multivariate phylogenetic structures. These tools open up new perspectives in the analysis of evolutionary comparative data. Availability: The stable version is available from CRAN: http:/cran.r-project.org/web/packages/adephylo/. The development version is hosted by R-Forge: http://r-forge.r-project.org/projects/adephylo/. Both versions can be installed directly from R. adephylo is distributed under the GNU General Public Licence (≥2). Contact: t.jombart@imperial.ac.uk; dray@biomserv.univ-lyon1.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Phylogenetic comparative methods have long considered phylogenetic signal as a source of statistical bias in the correlative analysis of biological traits. However, the main life-history strategies existing in a set of taxa are often combinations of life history traits that are inherently phylogenetically structured. In this paper, we present a method for identifying evolutionary strategies from large sets of biological traits, using phylogeny as a source of meaningful historical and ecological information. Our methodology extends a multivariate method developed for the analysis of spatial patterns, and relies on finding combinations of traits that are phylogenetically autocorrelated. Using extensive simulations, we show that our method efficiently uncovers phylogenetic structures with respect to various tree topologies, and remains powerful in cases where a large majority of traits are not phylogenetically structured. Our methodology is illustrated using empirical data, and implemented in the adephylo package for the free software R.

Due to the lack of fossil evidence, the timescales of bacterial evolution are largely unknown. The speed with which genetic change accumulates in populations of pathogenic bacteria, however, is a key parameter that is crucial for understanding the emergence of traits such as increased virulence or antibiotic resistance, together with the forces driving pathogen spread. Methicillin-resistant Staphylococcus aureus (MRSA) is a common cause of hospital-acquired infections. We have investigated an MRSA strain (ST225) that is highly prevalent in hospitals in Central Europe. By using mutation discovery at 269 genetic loci (118,804 basepairs) within an international isolate collection, we ascertained extremely low diversity among European ST225 isolates, indicating that a recent population bottleneck had preceded the expansion of this clone. In contrast, US isolates were more divergent, suggesting they represent the ancestral population. While diversity was low, however, our results demonstrate that the short-term evolutionary rate in this natural population of MRSA resulted in the accumulation of measurable DNA sequence variation within two decades, which we could exploit to reconstruct its recent demographic history and the spatiotemporal dynamics of spread. By applying Bayesian coalescent methods on DNA sequences serially sampled through time, we estimated that ST225 had diverged since approximately 1990 (1987 to 1994), and that expansion of the European clade began in 1995 (1991 to 1999), several years before the new clone was recognized. Demographic analysis based on DNA sequence variation indicated a sharp increase of bacterial population size from 2001 to 2004, which is concordant with the reported prevalence of this strain in several European countries. A detailed ancestry-based reconstruction of the spatiotemporal dispersal dynamics suggested a pattern of frequent transmission of the ST225 clone among hospitals within Central Europe. In addition, comparative genomics indicated complex bacteriophage dynamics., Author Summary Because fossils of bacteria do not exist or are morphologically indeterminate, the timescales of bacterial evolution are widely unknown. We have investigated the short-term evolution of a particular strain of methicillin-resistant Staphylococcus aureus (MRSA), a notorious cause of hospital-associated infections. By comparing 118 kilobases of DNA from MRSA isolates that had been collected at different points in time, we demonstrate that this strain has accumulated measurable DNA sequence variation within two decades. Further, we exploited this sequence diversity to estimate the short-term evolutionary rate and to date divergence times without paleontological calibration, and to reconstruct the recent demographic expansion and spatial spread of this MRSA.

International audience; Plague is a pandemic human invasive disease caused by the bacterial agent Yersinia pestis. We here report a comparison of 17 whole genomes of Y. pestis isolates from global sources. We also screened a global collection of 286 Y. pestis isolates for 933 SNPs using Sequenom MassArray SNP typing. We conducted phylogenetic analyses on this sequence variation dataset, assigned isolates to populations based on maximum parsimony and, from these results, made inferences regarding historical transmission routes. Our phylogenetic analysis suggests that Y. pestis evolved in or near China and spread through multiple radiations to Europe, South America, Africa and Southeast Asia, leading to country-specific lineages that can be traced by lineage-specific SNPs. All 626 current isolates from the United States reflect one radiation, and 82 isolates from Madagascar represent a second radiation. Subsequent local microevolution of Y. pestis is marked by sequential, geographically specific SNPs.

A novel influenza A (H1N1) virus has spread rapidly across the globe. Judging its pandemic potential is difficult with limited data, but nevertheless essential to inform appropriate health responses. By analyzing the outbreak in Mexico, early data on international spread, and viral genetic diversity, we make an early assessment of transmissibility and severity. Our estimates suggest that 23,000 (range 6000 to 32,000) individuals had been infected in Mexico by late April, giving an estimated case fatality ratio (CFR) of 0.4% (range: 0.3 to 1.8%) based on confirmed and suspected deaths reported to that time. In a community outbreak in the small community of La Gloria, Veracruz, no deaths were attributed to infection, giving an upper 95% bound on CFR of 0.6%. Thus, although substantial uncertainty remains, clinical severity appears less than that seen in the 1918 influenza pandemic but comparable with that seen in the 1957 pandemic. Clinical attack rates in children in La Gloria were twice that in adults (= 15 years: 29%). Three different epidemiological analyses gave basic reproduction number (R-0) estimates in the range of 1.4 to 1.6, whereas a genetic analysis gave a central estimate of 1.2. This range of values is consistent with 14 to 73 generations of human-to-human transmission having occurred in Mexico to late April. Transmissibility is therefore substantially higher than that of seasonal flu, and comparable with lower estimates of R0 obtained from previous influenza pandemics.

Multivariate analyses such as principal component analysis were among the first statistical methods employed to extract information from genetic markers. From their early applications to current innovations, these approaches have proven to be efficient for the analysis of the genetic variability in various contexts such as human genetics, conservation and adaptation studies. However, because multivariate analysis is a wide and diversified area of statistics, choosing a method appropriate to both the data and to the question being asked can be difficult. Moreover, some particularities of genetic markers need to be taken into account when using multivariate methods. As a consequence, multivariate analyses are often used as black boxes, which results in frequent mistakes in the literature. In this review, we provide a critical analysis of the application of multivariate methods to genetic markers, using a general framework that unifies all these methods for the sake of clarity. First, we focus on some common mistakes in these applications and ways to avoid these pitfalls. We then detail the most critical particularities of allele frequencies that demand adaptations of multivariate methods, and we propose solutions to the subsequent problems. Finally, we tackle several questions of interest in which multivariate analysis has a great role to play, such as the study of the typological coherence of different genetic markers, or the investigation of spatial genetic patterns.

The identification of spatial structures is a key step in understanding the ecological processes structuring the distribution of organisms. Spatial patterns in species distributions result from a combination of several processes occuring at different scales: identifying these scales is thus a crucial issue. Recent studies have proposed a new family of spatial predictors (PCNM: principal coordinates of neighbours matrices; MEMs: Moran's eigenvectors maps) that allow for modelling of spatial variation on different scales. To assess the multi-scale spatial patterns in multivariate data, these variables are often used as predictors in constrained ordination methods. However, the selection of the appropriate spatial predictors is still troublesome, and the identification of the main scales of spatial variation remains an open question. This paper presents a new statistical tool to tackle this issue: the multi-scale pattern analysis (MSPA). This ordination method uses MEMs to decompose ecological variability into several spatial scales and then summarizes this decomposition using graphical representations. A canonical form of MSPA can also be used to assess the spatial scales of the species-environment relationships. MSPA is compared to constrained ordination using simulated data, and illustrated using the famous oribatid mites dataset. The method is implemented in the free software R.

Working with weakly congruent markers means that consensus genetic structuring of populations requires methods explicitly devoted to this purpose. The method, which is presented here, belongs to the multivariate analyses. This method consists of different steps. First, single-marker analyses were performed using a version of principal component analysis, which is designed for allelic frequencies (%PCA). Drawing confidence ellipses around the population positions enhances %PCA plots. Second, a multiple co-inertia analysis (MCOA) was performed, which reveals the common features of single-marker analyses, builds a reference structure and makes it possible to compare single-marker structures with this reference through graphical tools. Finally, a typological value is provided for each marker. The typological value measures the efficiency of a marker to structure populations in the same way as other markers. In this study, we evaluate the interest and the efficiency of this method applied to a European and African bovine microsatellite data set. The typological value differs among markers, indicating that some markers are more efficient in displaying a consensus typology than others. Moreover, efficient markers in one collection of populations do not remain efficient in others. The number of markers used in a study is not a sufficient criterion to judge its reliability. "Quantity is not quality".

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.