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Epidemiologal investigation of an Acinetobacter baumannii outbreak using Core Genome Multilocus Sequence Typing

Carbapenem-resistant (CR) Acinetobacter baumannii is a serious nosocomial pathogen able to cause a variety of serious, often life-threatening infections and outbreaks. We aimed to investigate the molecular epidemiology of clinical isolates of CR-A. baumannii from an outbreak occurred in the intensive care unit (ICU) of our hospital.\n\nFrom January to April 2017, 13 CR-A. baumannii isolates were collected at the \"L. Spallanzani\" hospital, Rome, Italy; typing was performed by repetitive extragenetic palindromic-based (rep)- PCR-based DiversiLab system and WGS data were used for in silico analysis of traditional MLST types, for identifying resistance genes and for core genome multi locus sequence typing (cgMLST) analysis. Epidemiological data were obtained from hospital records.\n\nStrains were cultured from 7 patients treated in the ICU of our hospital; all isolates showed a multi-drug resistant (MDR) profile, carrying the blaOXA-23 carbapenemase. Typing performed by rep-PCR and MLST showed that the isolates clustered into one group while the cgMLST approach, which uses 2690 gene targets to characterize the gene-by-gene allelic profile of A. baumannii, highlighted the presence of two cluster types. These results allowed us to identify two patients who entered the ICU already colonized by two different strains of CR-A. baumannii; we hypothesize that these two patients could be the source of two separate transmission chains.\n\nOur results show that whole-genome-DNA sequencing by cgMLST is a valuable tool, better suited for prompt epidemiological investigations than traditional typing methods because of its higher discriminatory ability in determining clonal relatedness.

molecular biology

Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks

Genomic data is increasingly being used to understand infectious disease epidemiology. Isolates from a given outbreak are sequenced, and the patterns of shared variation are used to infer which isolates within the outbreak are most closely related to each other. Unfortunately, the phylogenetic trees typically used to represent this variation are not directly informative about who infected whom - a phylogenetic tree is not a transmission tree. However, a transmission tree can be inferred from a phylogeny while accounting for within-host genetic diversity by colouring the branches of a phylogeny according to which host those branches were in. Here we extend this approach and show that it can be applied to partially sampled and ongoing outbreaks. This requires computing the correct probability of an observed transmission tree and we herein demonstrate how to do this for a large class of epidemiological models. We also demonstrate how the branch colouring approach can incorporate a variable number of unique colours to represent unsampled intermediates in transmission chains. The resulting algorithm is a reversible jump Monte-Carlo Markov Chain, which we apply to both simulated data and real data from an outbreak of tuberculosis. By accounting for unsampled cases and an outbreak which may not have reached its end, our method is uniquely suited to use in a public health environment during real-time outbreak investigations. We implemented our technique in an R package called TransPhylo, which is freely available from https://github.com/xavierdidelot/TransPhylo.

Epidemiology

Combining Ensemble Learning Techniques and G-Computation to Investigate Chemical Mixtures in Environmental Epidemiology Studies

BackgroundAlthough biomonitoring studies demonstrate that the general population experiences exposure to multiple chemicals, most environmental epidemiology studies consider each chemical separately when assessing adverse effects of environmental exposures. Hence, the critical need for novel approaches to handle multiple correlated exposures.\n\nMethodsWe propose a novel approach using the G-formula, a maximum likelihood-based substitution estimator, combined with an ensemble learning technique (i.e. SuperLearner) to infer causal effect estimates for a multi-pollutant mixture. We simulated four continuous outcomes from real data on 5 correlated exposures under four exposure-response relationships with increasing complexity and 500 replications. The first simulated exposure-response was generated as a linear function depending on two exposures; the second was based on a univariate nonlinear exposure-response relationship; the third was generated as a linear exposure-response relationship depending on two exposures and their interaction; the fourth simulation was based on a non-linear exposure-response relationship with an effect modification by sex and a linear relationship with a second exposure. We assessed the method based on its predictive performance (Minimum Square error [MSE]), its ability to detect the true predictors and interactions (i.e. false discovery proportion, sensitivity), and its bias. We compared the method with generalized linear and additive models, elastic net, random forests, and Extreme gradient boosting. Finally, we reconstructed the exposure-response relationships and developed a toolbox for interactions visualization using individual conditional expectations.\n\nResultsThe proposed method yielded the best average MSE across all the scenarios, and was therefore able to adapt to the true underlying structure of the data. The method succeeded to detect the true predictors and interactions, and was less biased in all the scenarios. Finally, we could correctly reconstruct the exposure-response relationships in all the simulations.\n\nConclusionsThis is the first approach combining ensemble learning techniques and causal inference to unravel the effects of chemical mixtures and their interactions in epidemiological studies. Additional developments including high dimensional exposure data, and testing for detection of low to moderate associations will be carried out in future developments.

epidemiology

Zika virus outbreak in Cabo Verde Islands, West Africa: early epidemiological findings

IntroductionThe Zika virus (ZIKV) outbreak in the island nation of Cabo Verde was of unprecedented magnitude in Africa and the first to be associated with microcephaly in the continent.\n\nMethodsUsing a simple mathematical framework we present a first epidemiological assessment of attack and observation rates from 7,580 ZIKV notified cases and 18 microcephaly reports between July 2015 and May 2016.\n\nResultsIn line with observations from the Americas and elsewhere, the single-wave Cabo Verdean ZIKV epidemic was characterized by a basic reproductive number of 1.85 (95% CI, 1.5 -2.2), with overall the attack rate of 51.1% (range 42.1 - 61.1) and observation rate of 2.7% (range 2.29 - 3.33).\n\nConclusionCurrent herd-immunity may not be sufficient to prevent future small-to-medium epidemics in Cabo Verde. Together with a small observation rate, these results highlight the need for rapid and integrated epidemiological, molecular and genomic surveillance to tackle forthcoming outbreaks of ZIKV and other arboviruses.

epidemiology

Causal inference in cancer epidemiology: what is the role of Mendelian randomization?

Observational epidemiological studies are prone to confounding, measurement error, and reverse causation, undermining their ability to generate reliable causal estimates of the effect of risk factors to inform cancer prevention and treatment strategies. Mendelian randomization (MR) is an analytical approach that uses genetic variants to proxy potentially modifiable exposures (e.g. environmental factors, biological traits, and druggable pathways) to permit robust causal inference of the effects of these exposures on diseases and their outcomes. MR has seen widespread adoption within population health research in cardio-metabolic disease, but also holds much promise for identifying possible interventions (e.g., dietary, behavioural, or pharmacological) for cancer prevention and treatment. However, some methodological and conceptual challenges in the implementation of MR are particularly pertinent when applying this method to cancer aetiology and prognosis, including reverse causation arising from disease latency and selection bias in studies of cancer progression. These issues must be carefully considered to ensure appropriate design, analysis, and interpretation of such studies.\n\nIn this review, we provide an overview of the key principles and assumptions of MR focusing on applications of this method to the study of cancer aetiology and prognosis. We summarize recent studies in the cancer literature that have adopted a MR framework to highlight strengths of this approach compared to conventional epidemiological studies. Lastly, limitations of MR and recent methodological developments to address them are discussed, along with the translational opportunities they present to inform public health and clinical interventions in cancer.

epidemiology

Can we use routinely collected hospital and GP data for epidemiological study of common hand conditions? A UK Biobank based validation project

ObjectiveRoutine health records can be of great value in epidemiological and genetic studies if they are able to reliably identify true disease cases, especially when linked to large cohort studies. Little research has been undertaken into whether coding within UK electronic health records (EHR) is able to accurately identify clinical disease cases of common hand conditions. There is therefore a relative paucity of hand surgical research using EHRs due to concerns that cases cannot be accurately identified.\n\nThe aim of this study was to investigate the accuracy of hospital and primary care coding of routine EHRs for carpal tunnel syndrome (CTS) and base of thumb osteoarthritis (BTOA). Self-reported disease state as recorded in UK Biobank, a large prospective cohort study was also investigated.\n\nMethodsCode lists for each condition were generated by a team of clinicians, clinical coders and epidemiologists. All patients recruited to UK Biobank in one geographical region (Lothian, Scotland) where linked primary and secondary care coded datasets available were included. A decision- making algorithm was designed to define an administratively-confirmed or a clinically confirmed disease case. Patient electronic medical records (EMRs) were independently interrogated by two clinicians and inter-observer reliability calculated.\n\nResultsOf the 17,201 Biobank participants in NHS Lothian, 268 had at least one code for CTS and 82 for BTOA. For CTS, 159 cases were confirmed, 100 cases had insufficient information and 9 cases were refuted. Excluding missing data, the positive predictive value (PPV) for true clinical disease cases was 96% for incident disease (90% for prevalent disease; overall 94%).\n\nFor BTOA, 27 cases were confirmed, 46 cases had insufficient information, and 9 cases were refuted. Excluding missing data, PPV for incident disease was 81% (prevalent disease 56%, overall PPV 75%). Interrogation of the disease cases with insufficient information noted a large proportion arising from primary care and self-report coding systems.\n\nAnalyzing code combinations revealed that secondary care codes had the highest PPV for CTS and BTOA, emphasizing a more robust evaluation of PPV for patients requiring hospital based care. Overall, inter-observer reliability was good, with agreement in 90% of cases (Cohens kappa of 0.79) for clinical disease cases in CTS and agreement of 98%, (kappa 0.96) for BTOA.\n\nConclusionsWe have demonstrated that coding within UK Biobank is of sufficient quality to enable use of the resource for epidemiological and genetic research into common hand conditions, and that EMRs can be used for manual validation of UK health coding systems. Further work is needed to consider potential regional and interdisciplinary differences in coding practice, in strategies for dealing with missing data in EHRs, and to validate coding of common hand conditions in primary care.

epidemiology

Mapping malaria by combining parasite genomic and epidemiologic data

Recent global progress in scaling up malaria control interventions has revived the goal of complete elimination in many countries. Decreasing transmission intensity generally leads to increasingly patchy spatial patterns of malaria transmission, however, and control programs must accurately identify remaining foci in order to target interventions efficiently. In particular, mosquito control interventions like bed nets and insecticide spraying are best targeted to transmission hotspots, and the role of connectivity between different pockets of local transmission becomes increasingly important since humans are able to move parasites beyond the limits of mosquito dispersal and re-introduce parasites to previously malaria-free regions. Quantifying the connectivity between regions due to human travel, measuring malaria transmission intensity in different areas, and monitoring parasite spatial spread are therefore key issues for policy-makers because they underpin the feasibility of elimination and inform the path to its attainment. To this end, recent efforts have been made to develop new approaches to incorporating human mobility into spatial epidemiological models, for example using mobile phone data, and there has been a surge of interest in collecting spatially informative parasite samples to measure the genomic signatures of parasite connectivity. Due to their complicated life-cycles, Plasmodium parasites pose unique challenges to researchers in this respect and new methods that move beyond traditional phylogenetic and population genetic tools must be developed to harness genetic information effectively. Here, we discuss the spatial epidemiology of malaria in the context of transmission-reduction interventions, and the challenges and promising directions for the development of integrated mapping, modeling, and genomic approaches that leverage disparate data sets to measure both connectivity and transmission.

epidemiology

Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: an overview of the data from the epidemiological studies within the IMI DIRECT Consortium

Abstract/SummaryO_ST_ABSBackground and aimsC_ST_ABSUnderstanding the aetiology, clinical presentation and prognosis of type 2 diabetes (T2D) and optimizing its treatment might be facilitated by biomarkers that help predict a persons susceptibility to the risk factors that cause diabetes or its complications, or response to treatment. The IMI DIRECT (Diabetes Research on Patient Stratification) Study is a European Union (EU) Innovative Medicines Initiative (IMI) project that seeks to test these hypotheses in two recently established epidemiological cohorts. Here, we describe the characteristics of these cohorts at baseline and at the first main follow-up examination (18-months).\n\nMaterials and methodsFrom a sampling-frame of 24,682 European-ancestry adults in whom detailed health information was available, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm and enrolled into a prospective cohort study (n=2127) undertaken at four study centres across Europe (Cohort 1: prediabetes). We also recruited people from clinical registries with recently diagnosed T2D (n=789) into a second cohort study (Cohort 2: diabetes). The two cohorts were studied in parallel with matched protocols. Endogenous insulin secretion and insulin sensitivity were modelled from frequently sampled 75g oral glucose tolerance (OGTT) in Cohort 1 and with mixed-meal tolerance tests (MMTT) in Cohort 2. Additional metabolic biochemistry was determined using blood samples taken when fasted and during the tolerance tests. Body composition was assessed using MRI and lifestyle measures through self-report and objective methods.\n\nResultsUsing ADA-2011 glycaemic categories, 33% (n=693) of Cohort 1 (prediabetes) had normal glucose regulation (NGR), and 67% (n=1419) had impaired glucose regulation (IGR). 76% of the cohort was male, age=62(6.2) years; BMI=27.9(4.0) kg/m2; fasting glucose=5.7(0.6) mmol/l; 2-hr glucose=5.9(1.6) mmol/l [mean(SD)]. At follow-up, 18.6(1.4) months after baseline, fasting glucose=5.8(0.6) mmol/l; 2-hr OGTT glucose=6.1(1.7) mmol/l [mean(SD)]. In Cohort 2 (diabetes): 65% (n=508) were lifestyle treated (LS) and 35% (n=271) were lifestyle + metformin treated (LS+MET). 58% of the cohort was male, age=62(8.1) years; BMI=30.5(5.0) kg/m2; fasting glucose=7.2(1.4)mmol/l; 2-hr glucose=8.6(2.8) mmol/l [mean(SD)]. At follow-up, 18.2(0.6) months after baseline, fasting glucose=7.8(1.8) mmol/l; 2-hr MMTT glucose=9.5(3.3) mmol/l [mean(SD)].\n\nConclusionThe epidemiological IMI DIRECT cohorts are the most intensely characterised prospective studies of glycaemic deterioration to date. Data from these cohorts help illustrate the heterogeneous characteristics of people at risk of or with T2D, highlighting the rationale for biomarker stratification of the disease - the primary objective of the IMI DIRECT consortium.\n\nAbbreviations

epidemiology

A mechanistic hydro-epidemiological model of liver fluke risk

The majority of existing models for predicting disease risk in response to climate change are empirical. These models exploit correlations between historical data, rather than explicitly describing relationships between cause and response variables. Therefore, they are unsuitable for capturing impacts beyond historically observed variability and cannot be employed to assess interventions. In this study, we integrate environmental and epidemiological processes into a new mechanistic model, taking the widespread parasitic disease of fasciolosis as an example. The model simulates environmental suitability for disease transmission, explicitly linking the parasite life-cycle to key weather-water-environment conditions. First, using epidemiological data, we show that the model can reproduce observed infection levels in time and space over two case studies in the UK. Second, to overcome data limitations, we propose a calibration approach based on Monte Carlo sampling and expert opinion, which allows constraint of the model in a process-based way, including a quantification of uncertainty. Finally, comparison with information from the literature and a widely-used empirical risk index shows that the simulated disease dynamics agree with what has been traditionally observed, and that the new model gives better insight into the time-space patterns of infection, which will be valuable for decision support.

epidemiology

A Generative Bayesian Approach for Incorporating Biosurveillance Sources into Epidemiological Models

Biosurveillance \"systematically collects and analyzes data for the purpose of detecting cases of disease, [and] outbreaks of disease.\" (Wagner, Moore and Aryel, 2006) This typically involves using a set of known sources of epidemiological data, instead of opportunistically using the data sources which become available over time. This work attempts to partially remedy that limitation by using an easily adapted generative Bayesian econometric model to allow incorporation of novel data sources. This is done by building a generative model of the information sources, then using Bayesian Markov-chain Monte-Carlo to find the relationships between data and actual caseloads to use in an epidemiological model 1. While the application presented is limited to three data sources for a single disease (influenza), the methodology is potentially widely applicable, and enables rapid incorporation of a variety of sources and source types.

epidemiology

Epidemiology of a bubonic plague outbreak in Glasgow, Scotland in 1900

On August 3, 1900, bubonic plague (Yersinia pestis) broke out in Glasgow for the first time during the Third Pandemic. The local sanitary authorities rigorously tracked the spread of the disease and they found that nearly all of the 35 cases could be linked by contact with a previous case. Despite trapping hundreds of rats in the area, there was no evidence of a rat epizootic and the investigators speculated that the outbreak could be due to human-to-human transmission of bubonic plague. Here we use a likelihood-based method to reconstruct transmission trees for the outbreak. From the description of the outbreak and the reconstructed trees, we infer several epidemiological parameters. We found that the estimated mean serial interval was 7.4 days and the mean effective reproduction number dropped below 1 after implementation of control measures. We also found that there was a high rate of secondary transmissions within households and observations of transmissions from individuals who were not terminally septicemic. Our results provide important insights into the epidemiology of plague that are useful for modeling current and historic plague epidemics.

epidemiology

The Clinical and Molecular Epidemiology of CTX-M-9 Group Producing Enterobacteriaceae infections in children

BackgroundThe pandemic of extended-spectrum-beta-lactamase (ESBL)-producing-Enterobacteriaceae (Ent) is strongly linked to the dissemination of CTX-M-type-ESBL-Ent. We sought to define the epidemiology of infections in children due to an emerging resistance type, CTX-M-9-group-producing-Ent (CTX-M-9-grp-Ent).\n\nMethodsA multi-centered case-control analysis of Chicago children with CTX-M-9-grp-Ent infections was performed. Cases were defined as children possessing extended-spectrum-cephalosporin-resistant (ESC-R) infections. PCR and DNA analysis assessed beta-lactamase (bla) genes, multi-locus sequence types (MLST) and phylogenetic grouping of E. coli. Controls were children with ESC-susceptible (ESC-S)-Ent infections matched 3:1 by age, source, and hospital. The clinical-epidemiologic predictors of CTX-M-9-grp-Ent infection were assessed.\n\nResultsOf 356 ESC-R-Ent isolates from children (median age 4.1 years), CTX-M-9-group was the solely detected bla gene in 44(12.4%). The predominant species was E. coli (91%) of virulent phylogroups D(60%) and B2(40%). MLST revealed multiple strain types. On multivariable analysis, CTX-M-9-grp-Ent occurred more often in E. coli (OR 7.0), children of non-black-white-Hispanic race (OR 6.5), and outpatients (OR 4.5) which was a very unexpected finding for infections due to antibiotic-resistant bacteria. Residents of South Chicago were 6.7 times more likely to have CTX-M-9-grp-Ent infections than those in the reference region (West), while residence in Northwestern Chicago was associated with an 81% decreased risk. Other demographic, comorbidity, invasive-device, and antibiotic use differences were not found.\n\nConclusionsCTX-M-9-grp-Ent infection is strikingly associated with patient residence and is occurring in children without traditional in-patient exposure risk factors. This suggests that among children, the community environment may be a key contributor in the spread of these resistant pathogens.

epidemiology

Asthma-Neoplasms Relationships: New Insights Using Machine Inference, Epidemiological Reasoning, And Big Data

BackgroundA relationship between asthma and the risk of having cancer has been identified in several studies. However, these studies have used different methodologies, been primarily cross-sectional in nature, and the results have been contradictory. Population-level analyses are required to determine if a relationship truly exists. MethodsWe developed a novel machine learning tool to infer associations, Causal Inference using the Composition of Transactions (CICT). Two all payers claim datasets of over two hundred million hospitalization encounters from the US-based Healthcare Cost and Utilization Project (HCUP) were used for discovery and validation. Associations between asthma and neoplasms were discovered in data from the State of Florida. Validation was conducted on eight cohorts of patients with asthma, and seven subtypes of asthma and COPD using datasets from the State of California. Control groups were matched by gender, age, race, and history of tobacco use. Odds ratio analysis with Bonferroni-Holm correction measured the association of asthma and COPD with 26 different benign and malignant neoplasms. ICD9CM codes were used to identify exposures and outcomes. FindingsCICT identified 17 associations between asthma and the risk of neoplasia in the discovery dataset. In the validation studies, 208 case-control analyses were conducted between subtypes of Asthma (N= 999,370, male= 33%, age= 50) and COPD (N=715,971, male = 50%, age=69) with the corresponding matched control groups (N=8,400,004, male= 42%, age= 47). Allergic asthma was associated with benign neoplasms of the meninges, salivary, pituitary, parathyroid, and thyroid glands (OR:1.52 to 2.52), and malignant neoplasms of the breast, intrahepatic biliary system, hematopoietic, and lymphatic system (OR: 1.45 to 2.05). COPD was associated with malignant neoplasms in the lung, bladder, and hematopoietic systems. InterpretationThe combined use of machine learning methods for knowledge discovery and epidemiological methods shows that allergic asthma is associated with the development of neoplasia, including in glandular organs, ductal tissues, and hematopoietic systems. Also, our findings differentiate the pattern of neoplasms between allergic asthma and obstructive asthma. This suggests that inflammatory pathways that are active in asthma also contribute to neoplastic transformation in specific organ systems such as secretory organs. FundingNone At a Glance CommentaryOver the past three decades, studies have suggested that asthma could increase the risk of developing cancer, but a consensus has not been reached. The debate persists because the current evidence has been derived using cross-sectional statistical designs, limited datasets, and small cohorts and conflicting results. In addition, the mechanism by which allergic airway inflammation contributes to neoplastic transformation is postulated but not proven. Here, we present the largest study to date on this association in patients with asthma or COPD. A knowledge discovery method was used for hypothesis generation that, when combined with epidemiological reasoning tools, identified associations between airway disease and neoplasia. The results reveal novel relationships between allergic asthma and benign glandular tumors and confirm the well-known connections between COPD and lung cancer. Further, we identified a novel association between COPD and asthma with hematological malignancies. These findings rectify contradictory results from other studies and demonstrate more specifically that the types of neoplasms associated with asthma compared to COPD that infers mechanistic plausibility.

epidemiology

SimpactCyan 1.0: An Open-source Simulator for Individual-Based Models in HIV Epidemiology with R and Python Interfaces

SimpactCyan is an open-source simulator for individual-based models in HIV epidemiology. Its core algorithm is written in C++ for computational efficiency, while the R and Python interfaces aim to make the tool accessible to the fast-growing community of R and Python users. Transmission, treatment and prevention of HIV infections in dynamic sexual networks are simulated by discrete events. A generic "intervention" event allows model parameters to be changed over time, and can be used to model medical and behavioural HIV prevention programmes. First, we describe a more efficient variant of the modified Next Reaction Method that drives our continuous-time simulator. Next, we outline key built-in features and assumptions of individual-based models formulated in SimpactCyan, and provide code snippets for how to formulate, execute and analyse models in SimpactCyan through its R and Python interfaces. Lastly, we give two examples of applications in HIV epidemiology: the first demonstrates how the software can be used to estimate the impact of progressive changes to the eligibility criteria for HIV treatment on HIV incidence. The second example illustrates the use of SimpactCyan as a data-generating tool for assessing the performance of a phylodynamic inference framework.

epidemiology

Using digital epidemiology methods to monitor influenza-like illness in the Netherlands in real-time: the 2017-2018 season

IntroductionDespite the early development of Google Flu Trends in 2009, digital epidemiology methods have not been adopted widely, with most research focusing on the USA. In this article we demonstrate the prediction of real-time trends in influenza-like illness (ILI) in the Netherlands using search engine query data.\n\nMethodsWe used flu-related search query data from Google Trends in combination with traditional surveillance data from 40 general sentinel practices to build our predictive models. We introduced an artificial 4-week delay in the use of GP data in the models, in order to test the predictive performance of the search engine data.\n\nSimulating the weekly use of a prediction model across the 2017/2018 flu season we used lasso regression to fit 52 prediction models (one for each week) for weekly ILI incidence. We used rolling forecast cross-validation for lambda optimization in each model, minimizing the maximum absolute error.\n\nResultsThe models accurately predicted the number of ILI cases during the 2017/18 ILI epidemic in real time with a mean absolute error of 1.40 (per 10,000 population) and a maximum absolute error of 6.36. The model would also have identified the onset, peak, and end of the epidemic with reasonable accuracy\n\nThe number of predictors that were retained in the prediction models was small, ranging from 3 to 5, with a single keyword ( Griep = Flu) having by far the most weight in all models.\n\nDiscussionThis study demonstrates the feasibility of accurate real-time ILI incidence predictions in the Netherlands using internet search query data. Digital ILI monitoring strategies may be useful in countries with poor surveillance systems, or for monitoring emergent diseases, including influenza pandemics. We hope that this transparent and accessible case study inspires and supports further developments in field of digital epidemiology in Europe and beyond.

epidemiology

Neanderthal Genomics Suggests a Pleistocene Time Frame for the First Epidemiologic Transition

High quality Altai Neanderthal and Denisovan genomes are revealing which regions of archaic hominin DNA have persisted in the modern human genome. A number of these regions are associated with response to infection and immunity, with a suggestion that derived Neanderthal alleles found in modern Europeans and East Asians may be associated with autoimmunity. Independent sources of DNA-based evidence allow a re-evaluation of the nature and timing of the first epidemiologic transition. By combining skeletal, archaeological and genetic evidence we question whether the first epidemiologic transition in Eurasia was as tightly tied to the onset of the Holocene as has previously been assumed. There clear evidence to suggest that this transition began before the appearance of agriculture and occurred over a timescale of tens of thousands of years. The transfer of pathogens between human species may also have played a role in the extinction of the Neanderthals.

Genetics

Epidemiological and ecological modelling reveal diversity in microbial population structures from a cross-sectional community swabbing study

Respiratory tract infections (RTI) are responsible for over 4 million deaths per year worldwide with pathobiont carriage a required precursor to infection. Through a cross-sectional community-based nasal self-swabbing study we sought to determine carriage epidemiology for respiratory pathogens amongst bacteria (Streptococcus pneumoniae, Haemophilus influenza, Moraxella catarrhalis, Staphylococcus aureus, Pseudomonas aeruginosa and Neisseria meningitidis) and viruses (RSV, Influenza viruses A and B, Rhinovirus/Enterovirus, Coronavirus, Parainfluenza viruses 1-3 and Adenovirus (ADV)). Carriage of bacterial and viral species was shown to vary with participant age, recent RTI and the presence of other species. The spatial structure of microbial respiratory communities was less nested (more disordered) in the young (0-4 years) and those with recent RTI. Species frequency distributions were flatter than random expectation in young individuals (X2 = 20.42, p = 0.002), indicating spatial clumping of species consistent with facilitative relationships amongst them. Deviations from a neutral model of ecological niches were observed for samples collected in the summer and from older individuals (those aged 5-17, 18-64 and [≥]65 years) but not in samples collected from winter, younger individuals (those aged 0-4 years), individuals with recent RTI and individuals without recent RTI, demonstrating the importance of both neutral and niche processes in respiratory community assembly. The application of epidemiological methods and ecological theory to sets of respiratory tract samples has yielded novel insights into the factors that drive microbial community composition, such as seasonality and age, as well as species patterns and interactions within the nose.

microbiology

An method for systematically surveying data visualizations in infectiousdisease genomic epidemiology

MotivationData visualization is an important tool for exploring and communicating findings from genomic and healthcare datasets. Yet, without a systematic way of organizing and describing the design space of data visualizations, researchers may not be aware of the breadth of possible visualization design choices or how to distinguish between good and bad options.\n\nResultsWe have developed a method that systematically surveys data visualizations using the analysis of both text and images. Our method supports the construction of a visualization design space that is explorable along two axes: why the visualization was created and how it was constructed. We applied our method to a corpus of scientific research articles from infectious disease genomic epidemiology and derived a Genomic Epidemiology Visualization Typology (GEViT) that describes how visualizations were created from a series of chart types, combinations, and enhancements. We have also implemented an online gallery that allows others to explore our resulting design space of visualizations. Our results have important implications for visualization design and for researchers intending to develop or use data visualization tools. Finally, the method that we introduce is extensible to constructing visualizations design spaces across other research areas.\n\nAvailabilityOur browsable gallery is available at http://gevit.net and all project code can be found at https://github.com/amcrisan/gevitAnalysisRelease

bioinformatics