19 research outputs found

    Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences

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    Mycobacterium tuberculosis drug resistance (DR) challenges effective tuberculosis disease control. Current molecular tests examine limited numbers of mutations, and although whole genome sequencing approaches could fully characterise DR, data complexity has restricted their clinical application. A library (1,325 mutations) predictive of DR for 15 anti-tuberculosis drugs was compiled and validated for 11 of them using genomic-phenotypic data from 792 strains. A rapid online ‘TB-Profiler’ tool was developed to report DR and strain-type profiles directly from raw sequences. Using our DR mutation library, in silico diagnostic accuracy was superior to some commercial diagnostics and alternative databases. The library will facilitate sequence-based drug-susceptibility testing

    On plexus representation of dissimilarities

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    Correspondence analysis has found widespread application in analysing vegetation gradients. However, it is not clear how it is robust to situations where structures other than a simple gradient exist. The introduction of instrumental variables in canonical correspondence analysis does not avoid these difficulties. In this paper I propose to examine some simple methods based on the notion of the plexus (sensu McIntosh) where graphs or networks are used to display some of the structure of the data so that an informed choice of models is possible. I showthat two different classes of plexus model are available. These classes are distinguished by the use in one case of a global Euclidean model to obtain well-separated pair decomposition (WSPD) of a set of points which implicitly involves all dissimilarities, while in the other a Riemannian view is taken and emphasis is placed locally, i.e., on small dissimilarities. I showan example of each of these classes applied to vegetation data

    Estimating the health impact of vaccination against 10 pathogens in 98 low and middle income countries from 2000 to 2030

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    Background The last two decades have seen substantial expansion of childhood vaccination programmes in low and middle income countries (LMICs). Here we quantify the health impact of these programmes by estimating the deaths and disability-adjusted life years (DALYs) averted by vaccination with ten antigens in 98 LMICs between 2000 and 2030. Methods Independent research groups provided model-based disease burden estimates under a range of vaccination coverage scenarios for ten pathogens: hepatitis B (HepB), Haemophilus influenzae type b (Hib), human papillomavirus (HPV), Japanese encephalitis (JE), measles, Neisseria meningitidis serogroup A (MenA), Streptococcus pneumoniae, rotavirus, rubella, yellow fever. Using standardized demographic data and vaccine coverage estimates for routine and supplementary immunization activities, the impact of vaccination programmes on deaths and DALYs was determined by comparing model estimates from the no vaccination counterfactual scenario with those from a default coverage scenario. We present results in two forms: deaths/DALYs averted in a particular calendar year, and in a particular annual birth cohort. Findings We estimate that vaccination will have averted 70 (2.5-97.5% quantile range 54-80) million deaths between 2000 and 2030 across the 98 countries and ten pathogens considered, 35 (30-40) million of these between 2000-2018. From 2000-2018, this represents a 41% (36-44%) reduction in deaths due to the ten pathogens relative to the no vaccination counterfactual. Most (95% (93-99%)) of this impact is in under-five age mortality, notably from measles. Over the lifetime of birth cohorts born between 2000 and 2030, we predict that 121 (102-136) million deaths will be averted by vaccination, of which 58 (31-82) and 38 (10-81) million are due to measles and Hepatitis B vaccination, respectively. We estimate that recent increases in vaccine coverage and introductions of additional vaccines will result in a 72% (64-75%) reduction in lifetime mortality caused by these 10 pathogens in the 2018 birth cohort. Interpretation Increases in vaccine coverage and the introduction of new vaccines into LMICs over the last two decades have had a major impact in reducing mortality. These public health gains are predicted to increase in coming decades if progress in increasing coverage is sustained

    Lives saved with vaccination for 10 pathogens across 112 countries in a pre-COVID-19 world

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    Background: Vaccination is one of the most effective public health interventions. We investigate the impact of vaccination activities for Haemophilus influenzae type b, hepatitis B, human papillomavirus, Japanese encephalitis, measles, Neisseria meningitidis serogroup A, rotavirus, rubella, Streptococcus pneumoniae, and yellow fever over the years 2000-2030 across 112 countries. Methods: Twenty-one mathematical models estimated disease burden using standardised demographic and immunisation data. Impact was attributed to the year of vaccination through vaccine-activity-stratified impact ratios. Results: We estimate 97 (95%CrI[80, 120]) million deaths would be averted due to vaccination activities over 2000-2030, with 50 (95%CrI[41, 62]) million deaths averted by activities between 2000 and 2019. For children under-5 born between 2000 and 2030, we estimate 52 (95%CrI[41, 69]) million more deaths would occur over their lifetimes without vaccination against these diseases. Conclusions: This study represents the largest assessment of vaccine impact before COVID-19-related disruptions and provides motivation for sustaining and improving global vaccination coverage in the future. Funding: VIMC is jointly funded by Gavi, the Vaccine Alliance, and the Bill and Melinda Gates Foundation (BMGF) (BMGF grant number: OPP1157270 / INV-009125). Funding from Gavi is channelled via VIMC to the Consortium's modelling groups (VIMC-funded institutions represented in this paper: Imperial College London, London School of Hygiene and Tropical Medicine, Oxford University Clinical Research Unit, Public Health England, Johns Hopkins University, The Pennsylvania State University, Center for Disease Analysis Foundation, Kaiser Permanente Washington, University of Cambridge, University of Notre Dame, Harvard University, Conservatoire National des Arts et Métiers, Emory University, National University of Singapore). Funding from BMGF was used for salaries of the Consortium secretariat (authors represented here: TBH, MJ, XL, SE-L, JT, KW, NMF, KAMG); and channelled via VIMC for travel and subsistence costs of all Consortium members (all authors). We also acknowledge funding from the UK Medical Research Council and Department for International Development, which supported aspects of VIMC's work (MRC grant number: MR/R015600/1).JHH acknowledges funding from National Science Foundation Graduate Research Fellowship; Richard and Peggy Notebaert Premier Fellowship from the University of Notre Dame. BAL acknowledges funding from NIH/NIGMS (grant number R01 GM124280) and NIH/NIAID (grant number R01 AI112970). The Lives Saved Tool (LiST) receives funding support from the Bill and Melinda Gates Foundation.This paper was compiled by all coauthors, including two coauthors from Gavi. Other funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication
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