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bioRxiv2017

An outbreak of Pseudomonas aeruginosa infection linked to a Black Friday piercing event

BackgroundOutbreaks linked to cosmetic piercing are rare, but can cause significant illness. We report the investigation and management of a point-source outbreak that occurred during a \"Black Friday\" event in North West England.\n\nMethodsOutbreak investigation was led by Public Health England, and included active case finding among individuals pierced at a piercing premises between 25/11/2016 (\"Black Friday\") and 7/12/2016. Detailed epidemiological, environmental (including inspection and sampling), and microbiological investigation was undertaken.\n\nResultsDuring the \"Black Friday\" event (25/11/2016), 45 people were pierced (13 by a newly-appointed practitioner). Eleven cases were identified (7 microbiologically-confirmed, 2 probable, and 2 possible). All cases had clinical signs of infection around piercing sites, and five required surgical intervention, with varying degrees of post-operative disfigurement. All confirmed and probable cases had a \"scaffold piercing\" placed with a guide bar by the newly-appointed practitioner. Pseudomonas aeruginosa, indistinguishable at nine-locus variable-number tandem repeat loci, was isolated from four of the confirmed cases, and from pre- and post-flush samples from five separate water taps (three sinks) in the premises. Water samples taken after remedial plumbing work confirmed elimination of Pseudomonas contamination.\n\nConclusionsAlthough high levels of Pseudomonas water contamination and some poor infection control procedures were identified, infection appeared to require additional exposure to an inexperienced practitioner, and the more invasive scaffold piercing. A proactive collaborative approach between piercers and health and environmental officials is required to reduce outbreak risk, particularly when unusually large events are planned.

epidemiology↗

bioRxiv2017

Trade-off between intra- and interannual scales in the evolution of aggressiveness in a local plant pathogen population

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100039). The efficiency of plant resistance to fungal pathogen populations is expected to decrease over time, due to its evolution with an increase in the frequency of virulent or highly aggressive strains. This dynamics may differ depending on the scale investigated (annual or pluriannual), particularly for annual crop pathogens with both sexual and asexual reproduction cycles. We assessed this time-scale effect, by comparing aggressiveness changes in a local Zymoseptoria tritici population over an eight-month cropping season and a six-year period of wheat monoculture. We collected two pairs of subpopulations to represent the annual and pluriannual scales: from leaf lesions at the beginning and end of a single annual epidemic, and from crop debris at the beginning and end of a six-year period. We assessed two aggressiveness traits - latent period and lesion size - on sympatric and allopatric host varieties. A trend toward decreased latent period concomitant with a significant loss of variability was established during the course of the annual epidemic, but not over the six-year period. Furthermore, a significant cultivar effect (sympatric vs. allopatric) on the average aggressiveness of the isolates revealed host adaptation, arguing that the observed patterns could result from selection. We thus provide an experimental body of evidence of an epidemiological trade-off between the intra- and inter-annual scales in the evolution of aggressiveness in a local plant pathogen population. More aggressive isolates were collected from upper leaves, on which disease severity is usually lower than on the lower part of the plants left in the field as crop debris after harvest. We suggest that these isolates play little role in sexual reproduction, due to an Allee effect (difficulty finding mates at low pathogen densities), particularly as the upper parts of the plant are removed from the field, explaining the lack of transmission of increases in aggressiveness between epidemics.

epidemiology↗

bioRxiv2017

Mitochondrial genome of Plasmodium vivax/simium detected in an endemic region for malaria in the Atlantic Forest of Espirito Santo state, Brazil: do mosquitoes, simians and humans harbor the same parasite?

BackgroundThe transmission of malaria in the extra-Amazonian regions of Brazil, although interrupted in the 1960s, has persisted to the present time in some areas of dense Atlantic Forest, with reports of cases characterized by particular transmission cycles and clinical presentations. Bromeliad-malaria, as it is named, is particularly frequent in the state of Espirito Santo, with Plasmodium vivax being the parasite commonly recognized as the etiologic agent of human infections. With regard to the spatial and temporal distances between cases reported in this region, the transmission cycle does not fit the traditional malaria cycle. The existence of a zoonosis, with infected simians participating in the epidemiology, is therefore hypothesized. In the present study, zoonotic transmission of bromeliad-malaria in Espirito Santo is investigated, based on the complete mitochondrial genome of DNA extracted from isolates of Plasmodium species which had infected humans, a simian from the genus Allouata, and Anopheles mosquitoes. Plasmodium vivax/simium was identified in the samples by both nested-PCR and real-time PCR. After amplification, the mitochondrial genome was completely sequenced and compared in a haplotype network, including all sequences of P. vivax/simium mitochondrial genomes sampled from humans and simians from all regions in Brazil.\n\nResultsThe haplotype network demonstrates that humans and simians from the Atlantic Forest share the same haplotype, but some isolates from humans are not identical to the simian isolate. In addition, the plasmodial DNA extracted from mosquitoes revealed sequences different from those obtained from simians, but similar to two isolates from humans.\n\nConclusionsThese findings reinforce the hypothesis that in the Atlantic Forest, and especially in the state with the highest frequency of bromeliad-malaria in Brazil, the same parasite species is shared by humans and simians, at least in part. The difference between the sequences of mosquitoes and simians raises two hypotheses: (1) two distinct transmission cycles for human malaria exist in the study area, one of them involving simians and the other exclusive to human hosts, or (2) there is only one transmission cycle involving humans and simians, but the identification of variations among simians was not possible due to a lack of other samples.

epidemiology↗

bioRxiv2017

Improving the accuracy of two-sample summary data Mendelian randomization: moving beyond the NOME assumption

BackgroundTwo-sample summary data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the instrumental variable (IV) and necessary modelling assumptions, then their individual ratio estimates of causal effect should be homogeneous. Observed heterogeneity signals that one or more of these assumptions could have been violated.\n\nMethodsCausal estimation and heterogeneity assessment in MR requires an approximation for the variance, or equivalently the inverse-variance weight, of each ratio estimate. We show that the most popular 1st order weights can lead to an inflation in the chances of detecting heterogeneity when in fact it is not present. Conversely, ostensibly more accurate 2nd order weights can dramatically increase the chances of failing to detect heterogeneity, when it is truly present. We derive modified weights to mitigate both of these adverse effects.\n\nResultsUsing Monte Carlo simulations, we show that the modified weights outperform 1st and 2nd order weights in terms of heterogeneity quantification. Modified weights are also shown to remove the phenomenon of regression dilution bias in MR estimates obtained from weak instruments, unlike those obtained using 1st and 2nd order weights. However, with small numbers of weak instruments, this comes at the cost of a reduction in estimate precision and power to detect a causal effect compared to 1st order weighting. Moreover, 1st order weights always furnish unbiased estimates and preserve the type I error rate under the causal null. We illustrate the utility of the new method using data from a recent two-sample summary data MR analysis to assess the causal role of systolic blood pressure on coronary heart disease risk.\n\nConclusionsWe propose the use of modified weights within two-sample summary data MR studies for accurately quantifying heterogeneity and detecting outliers in the presence of weak instruments. Modified weights also have an important role to play in terms of causal estimation (in tandem with 1st order weights) but further research is required to understand their strengths and weaknesses in specific settings.

epidemiology↗

bioRxiv2017

Pediatric hospitalizations associated with respiratory syncytial virus (RSV) and influenza, and the frequency of asthma as a secondary diagnosis

BackgroundThere is uncertainty about the burden of hospitalization associated with RSV and influenza in children, including those with underlying medical conditions. MethodsWe applied previously developed methodology (Goldstein et al., Epidemiology 2012) to HealthCare Cost and Utilization Project (HCUP) hospitalization data and additional data related to asthma diagnosis/previous history in hospitalized children to estimate RSV and influenza-associated hospitalization rates in different subpopulations of US children between 2003-2010. ResultsThe estimated average annual rates (per 100,000 children) of RSV-associated hospitalization with a respiratory cause (ICD-9 codes 460-519) present anywhere in the discharge diagnosis were 2381 (95% CI(2252,2515)) in age <1y; 710.6(609.1,809.2) (age 1y); 395(327.7,462.4) (age 2y); 211.3(154.6,266.8) (age 3y); 111.1(62.4,160.1) (age 4y); 72.3(29.3,116.4) (ages 5-6y); 35.6(9.9,62.2) (ages 7-11y); and 39(17.5,60.6) (ages 12-17y). The corresponding rates of influenza-associated hospitalization were lower, ranging from 181(142.5,220.3) in age <1y to 17.9(11.7,24.2) in ages 12-17y. The relative risks for RSV-related hospitalization associated with a prior diagnosis of asthma in age groups under 5y ranged between 3.1(2.1,4.7) (age <1y) to 6.7(4.2,11.8) (age 2y); the corresponding risks for influenza-related hospitalization ranged from 2.8(2.1,4) (age <1y) to 4.9(3.8,6.4) (age 3y). ConclusionsRSV-associated hospitalization rates in young children are high and decline rapidly with age. Young children with an asthma diagnosis should be target groups for RSV and influenza-related mitigation efforts, possibly including RSV prophylaxis for the youngest children.

epidemiology↗

bioRxiv2017

Determining whether a class of random graphs is consistent with an observed contact network

We demonstrate a general method to analyze the sensitivity of attack rate in a network model of infectious disease epidemiology to the structure of the network. We use Moore and Shannons \"network reliability\" statistic to measure the epidemic potential of a network. A number of networks are generated using exponential random graph models based on the properties of the contact network structure of one of the Add Health surveys. The expected number of infections on the original Add Health network is significantly different from that on any of the models derived from it. Because individual-level transmissibility and network structure are not separately identifiable parameters given population-level attack rate data it is possible to re-calibrate the transmissibility to fix this difference. However, the temporal behavior of the outbreak remains significantly different. Hence any estimates of the effectiveness of time dependent interventions on one network are unlikely to generalize to the other. Moreover, we show that in one case even a small perturbation to the network spoils the re-calibration. Unfortunately, the set of sufficient statistics for specifying a contact network model is not yet known. Until it is, estimates of the outcome of a dynamical process on a particular network obtained from simulations on a different network are not reliable.

epidemiology↗

bioRxiv2017

MERS-CoV spillover at the camel-human interface

Middle East respiratory syndrome coronavirus (MERS-CoV) is a zoonotic virus from camels causing significant mortality and morbidity in humans in the Arabian Peninsula. The epidemiology of the virus remains poorly understood, and while case-based and seroepidemiological studies have been employed extensively throughout the epidemic, viral sequence data have not been utilised to their full potential. Here we use existing MERS-CoV sequence data to explore its phylodynamics in two of its known major hosts, humans and camels. We employ structured coalescent models to show that long-term MERS-CoV evolution occurs exclusively in camels, whereas humans act as a transient, and ultimately terminal host. By analysing the distribution of human outbreak cluster sizes and zoonotic introduction times we show that human outbreaks in the Arabian peninsula are driven by seasonally varying zoonotic transfer of viruses from camels. Without heretofore unseen evolution of host tropism, MERS-CoV is unlikely to become endemic in humans.

epidemiology↗

bioRxiv2017

Toward Capturing the Exposome: Exposure Biomarker Variability and Co-Exposure Patterns in the Shared Environment

BACKGROUNDAlong with time, variation in the exposome is dependent on the location and sex of study participants. One specific factor that may influence exposure co-variations is a shared household environment.\n\nOBJECTIVESTo examine the influence of shared household and partners sex in relation to the variation in 128 endocrine disrupting chemical (EDC) exposures among couples.\n\nMETHODSIn a cohort comprising 501 couples trying for pregnancy, we measured 128 (13 chemical classes) persistent and non-persistent EDCs and estimated 1) sex-specific differences; 2) variance explained by shared household; and 3) Spearmans rank correlation coefficients (rs) for females, males, and couples exposures.\n\nRESULTSSex was correlated with 8 EDCs including polyfluoroalkyl substances (PFASs) (p < 0.05). Shared household explained 43% and 41% of the total variance for PFASs and blood metals, respectively, but less than 20% for the remaining 11 EDC classes. Co-exposure patterns of the exposome were similar between females and males, with within-class rs higher for persistent and lower for non-persistent chemicals. Median rss of polybrominated compounds and urine metalloids were 0.45 and 0.09, respectively, for females (0.41 and 0.08 for males), whereas lower rss for these 2 classes were found for couples (0.21 and 0.04).\n\nCONCLUSIONSOverall, sex did not significantly affect EDC levels in couples. Individual, rather than shared environment, could be a major factor influencing the co-variation of 128 markers of the exposome. Correlations between exposures are lower in couples than in individual partners and have important analytical and sampling implications for epidemiological study.

epidemiology↗

bioRxiv2017

Vaccine waning and mumps re-emergence in the United States

Following decades of declining mumps incidence amid widespread vaccination, the United States and other high-income countries have experienced a resurgence in mumps cases over the last decade. Outbreaks affecting vaccinated individuals--and communities with high vaccine coverage--have prompted concerns about the effectiveness of the live attenuated vaccine currently in use: it is unclear if immune protection wanes, or if the vaccine protects inadequately against mumps virus lineages currently circulating. Synthesizing data from epidemiological studies, we estimate that vaccine-derived protection wanes at a timescale of 27 (95%CI: 16 to 51) years. After accounting for this waning, we identify no evidence of changes in vaccine effectiveness over time associated with the emergence of heterologous virus genotypes. Moreover, a mathematical model of mumps transmission validates our findings about the central role of vaccine waning in the re-emergence of cases: outbreaks from 2006 to the present among young adults, and outbreaks occurring in the late 1980s and early 1990s among adolescents, align with peaks in the susceptibility of these age groups attributable to loss of vaccine-derived protection. In contrast, evolution of mumps virus strains escaping pressure would be expected to cause a higher proportion of cases among children. Routine use of a third dose at age 18y, or booster dosing throughout adulthood, may enable mumps elimination and should be assessed in clinical trials.\n\nOne Sentence SummaryThe estimated waning rate of vaccine-conferred immunity against mumps predicts observed changes in the age distribution of mumps cases in the United States since 1967.

epidemiology↗

bioRxiv2017

Detecting and correcting for bias in Mendelian randomization analyses using gene-by-environment interactions

BackgroundMendelian randomization has developed into an established method for strengthening causal inference and estimating causal effects, largely due to the proliferation of genome-wide association studies. However, genetic instruments remain controversial as pleiotropic effects can introduce bias into causal estimates. Recent work has highlighted the potential of gene-environment interactions in detecting and correcting for pleiotropic bias in Mendelian randomization analyses.\n\nMethodsWe introduce MR using Gene-by-Environment interactions (MRGxE) as a framework capable of identifying and correcting for pleiotropic bias, drawing upon developments in econometrics and epidemiology. If an instrument-covariate interaction induces variation in the association between a genetic instrument and exposure, it is possible to identify and correct for pleiotropic effects. The interpretation of MRGxE is similar to conventional summary Mendelian randomization approaches, with a particular advantage of MRGxE being the ability to assess the validity of an individual instrument.\n\nResultsWe investigate the effect of BMI upon systolic blood pressure (SBP) using data from the UK Biobank and the GIANT consortium using a single instrument (a weighted allelic score). We find MRGxE produces findings in agreement with MR Egger regression in a two-sample summary MR setting, however, association estimates obtained across all methods differ considerably when excluding related participants or individuals of non-European ancestry. This could be a consequence of selection bias, though there is also potential for introducing bias by using a mixed ancestry population. Further, we assess the performance of MRGxE with respect to identifying and correcting for horizontal pleiotropy in a simulation setting, highlighting the utility of the approach even when the MRGxE assumptions are violated.\n\nConclusionsBy utilising instrument-covariate interactions within a linear regression framework, it is possible to identify and correct for pleiotropic bias, provided the average magnitude of pleiotropy is constant across interaction covariate subgroups.\n\nO_TEXTBOXKey MessagesO_LIInstrument-covariate interactions can be used to identify pleiotropic bias in Mendelian randomization analyses, provided they induce sufficient variation in the association between the genetic instrument and exposure.\nC_LIO_LIBy regressing the gene-outcome association upon the gene-exposure association across interaction covariate subgroups, it is possible to obtain an estimate of the average pleiotropic effect and a causal effect estimate.\nC_LIO_LIThe interpretation of MRGxE is analogous to that of MR-Egger regression.\nC_LIO_LIThe approach serves as a valuable test for directional pleiotropy and can be used to inform instrument selection.\nC_LI\n\nC_TEXTBOX

epidemiology↗

bioRxiv2017

Modeling Epidemics: A Primer and Numerus Software Implementation

Epidemiological models are dominated by SEIR (Susceptible, Exposed, Infected and Removed) dynamical systems formulations and their elaborations. These formulations can be continuous or discrete, deterministic or stochastic, or spatially homogeneous or heterogeneous, the latter often embracing a network formulation. Here we review the continuous and discrete deterministic and discrete stochastic formulations of the SEIR dynamical systems models, and we outline how they can be easily and rapidly constructed using the Numerus Model Builder, a graphically-driven coding platform. We also demonstrate how to extend these models to a metapopulation setting using both the Numerus Model Builder network and geographical mapping tools.

epidemiology↗

bioRxiv2017

Selection bias in instrumental variable analyses

Participants in epidemiological and genetic studies are rarely true random samples of the populations they are intended to represent, and both known and unknown factors can influence participation in a study (known as selection into a study). The circumstances in which selection causes bias in an instrumental variable (IV) analysis are not widely understood by practitioners of IV analyses. We use directed acyclic graphs (DAGs) to depict assumptions about the selection mechanism (factors affecting selection) and show how DAGs can be used to determine when a two-stage least squares (2SLS) IV analysis is biased by different selection mechanisms. Via simulations, we show that selection can result in a biased IV estimate with substantial confidence interval undercoverage, and the level of bias can differ between instrument strengths, a linear and nonlinear exposure-instrument association, and a causal and noncausal exposure effect. We present an application from the UK Biobank study, which is known to be a selected sample of the general population. Of interest was the causal effect of education on the decision to smoke. The 2SLS exposure estimates were very different between the IV analysis ignoring selection and the IV analysis which adjusted for selection (e.g., 1.8 [95% confidence interval -1.5, 5.0] and -4.5 [-6.6, -2.4], respectively). We conclude that selection bias can have a major effect on an IV analysis and that statistical methods for estimating causal effects using data from nonrandom samples are needed.

epidemiology↗

bioRxiv2017

Quantifying ethical tradeoffs for vaccine efficacy trials during severe epidemics

BackgroundDuring emerging epidemics of highly fatal diseases, rapid development and testing of new vaccines may be critical to curbing transmission and saving lives. However, the design of vaccine efficacy trials in such contexts may face considerable logistical, epidemiological, or ethical impediments. Three different vaccine efficacy trials were conducted during the 2014-2016 Ebola virus epidemic in West Africa, each with different designs. At the time, there was vigorous debate on the tradeoff between a trials ability to yield information of scientific and societal value versus the perceived ethical dilemma of withholding potentially life-saving vaccines from control participants. Whereas the scientific value of a trial is often estimated in terms of statistical power, speed, and rigor, we lack similar metrics for the ethical costs of withholding interventions.\n\nMethods and FindingsHere, we introduce a conceptual framework that fills this gap and allows quantitative assessment of both the scientific value of a study and the risks incurred by trial participants. We show that even untested vaccines against severe diseases may be probabilistically beneficial--i.e. after accounting for realistic uncertainty in their safety and efficacy, trial participants are expected to be better off vaccinated than not. While accounting for this uncertainty, we estimate trial participant risk under a hypothetical, idealized vaccine rollout scenario and compare it to risk under various candidate trial designs, in order to elucidate specific quantitative tradeoffs between cumulative risk to trial participants and information gained. Through an illustrative simulation example, we highlight specific trial-design modifications that allow for conscientious balance between minimizing participant risk and acquiring information of societal value. These include modifications that affect the speed with which a trial would detect an efficacious vaccine (greater sample size or enrollment rate, interim analyses, or risk-prioritized vaccine rollout), which leads to earlier vaccination of control participants should the vaccine be efficacious, and those that systematically limit the risk \"spent\" by unvaccinated individuals (e.g., providing vaccination to controls after a delay, or presumptive vaccination of subjects above a risk threshold).\n\nConclusionWe advocate this conceptual approach as a means of clarifying the tensions between opposing viewpoints and facilitating transparent discussion to aid ethical and efficient responses to future emerging epidemics.

epidemiology↗

bioRxiv2017

Simulations for Designing and Interpreting Intervention Trials in Infectious Diseases

Here we urge the adoption of a new paradigm for the design and interpretation of intervention trials in infectious diseases, particularly in emerging infectious disease, that more accurately reflects the dynamics of the transmission process. Interventions in infectious diseases can have indirect effects on those not receiving the intervention as well as direct effects on those receiving the intervention. Combinations of interventions can have complex interactions at the population level. These often cannot be adequately addressed with standard study designs and analytic methods. Simulations can help to accurately represent transmission dynamics in an increasingly complex world which is critical for proper trial design and interpretation. Some ethical aspects of a trial can also be quantified using simulations. After a trial has been conducted, simulations can be used to explore possible explanations for the observed effects. A great deal is to be gained through a multidisciplinary approach that builds collaborations among experts in infectious disease dynamics, epidemiology, statistical science, economics, simulation methods and the conduct of clinical trials.

epidemiology↗

bioRxiv2017

Application of benchmark analysis for mixed contaminant exposures: Mutual adjustment of two perfluoroalkylate substances associated with immunotoxicity

BackgroundDevelopmental exposure to perfluorinated substances is associated with deficient IgG antibody responses to childhood vaccines as an indication of depressed immune system functions. As this outcome may represent a critical effect of these substances, calculation of benchmark dose (BMD) results would be useful for standards setting to protect exposed populations against adverse effects. However, in the mixed exposure setting of most epidemiological evidence, the two major and inter-related substances associated with this adverse effect have shown similar benchmark results that raise concerns about possible confounding.\n\nMethodsWith the aim of better characterizing the immunotoxicity impact of the two major perfluorinated substances in the mixed exposures, we carried out BMD calculations on prospective data from two prospective birth cohort studies from the Faroe Islands with a total of 1,146 children. Exposure data included serum concentrations of perfluorooctane sulfonate and perfluorooctanoate at birth and at age 5 years and, as outcome parameters, the serum concentrations of specific IgG antibodies against tetanus and diphtheria at ages 5 and 7. We calculated the BMDs and their lower confidence bounds (BMDLs) and included mutual adjustment for the two compounds.\n\nResultsThe BMDLs for the two immunotoxicants were of similar magnitude before and after adjustment. Both substances showed lower results for a logarithmic dose-response model, which also provided a slightly better fit than a linear dose model for both antibodies. We also used a broken curve shape that allowed a different slope below the median exposure. Postnatal exposure as represented by the age 5 serum concentration, showed a stronger association with the antibody outcomes than the prenatal exposure. Due to the correlation between the two immunotoxicants, the mutual adjustment resulted in elevated BMD results and p values. However, the BMDL values were virtually unchanged.\n\nConclusionsAdjustment for co-exposure to another immunotoxicant increased the variance and the BMD values, but affected the BMDL values only to a negligible extent. These calculations are in accordance with an interpretation that, when two toxicants appear to affect an outcome to an almost equal degree and none of them is known to be solely responsible, the exposures should both be considered responsible and attract equal regulatory attention until further evidence shows otherwise.

epidemiology↗

bioRxiv2017

Dynamics and Control of Infections on Social Networks of Population Types

Random mixing in host populations has been a convenient simplifying assumption in the study of epidemics, but neglects important differences in contact rates within and between population groups. For HIV/AIDS, the assumption of random mixing is inappropriate for epidemics that are concentrated in groups of people at high risk, including female sex workers (FSW) and their male clients (MCF), injecting drug users (IDU) and men who have sex with men (MSM). To find out who transmits infection to whom and how that affects the spread and containment of infection remains a major empirical challenge in the epidemiology of HIV/AIDS. Here we develop a technique, based on the routine sampling of infection in linked population groups (a social network of population types), which shows how an HIV/AIDS epidemic in Can Tho Province of Vietnam began in FSW, was propagated mainly by IDU, and ultimately generated most cases among the female partners of MCF (FPM). Calculation of the case reproduction numbers within and between groups, and for the whole network, provides insights into control that cannot be deduced simply from observations on the prevalence of infection. Specifically, the per capita rate of HIV transmission was highest from FSW to MCF, and most HIV infections occurred in FPM, but the number of infections in the whole network is best reduced by interrupting transmission to and from IDU. This analysis can be used to guide HIV/AIDS interventions using needle and syringe exchange, condom distribution and antiretroviral therapy. The method requires only routine data and could be applied to infections in other populations.

epidemiology↗

bioRxiv2017

Rapid genome sequencing for outbreak analysis of the emerging human fungal pathogen Candida auris

BackgroundCandida auris was first described in 2009, and has since caused nosocomial outbreaks, invasive infections and fungaemia across 11 countries in five continents. An outbreak of C. auris occurred in a specialised cardiothoracic London hospital between April 2015 and November 2016, which to date has been the largest outbreak reported worldwide, involving a total of 72 patients.\n\nMethodsTo understand the epidemiology of C. auris infection within this hospital, we sequenced the genomes of outbreak isolates using Oxford Nanopore Technologies and Illumina in order to type antifungal resistance alleles and to explore the outbreak within its local and global context.\n\nFindingsPhylogenomic analysis placed the UK outbreak in the India/Pakistan clade, demonstrating an Asian origin. The outbreak showed similar diversity to that of the entire clade and limited local spatiotemporal clustering was observed. One isolate displayed resistance to both echinocandins and 5-flucytosine; the former was associated with a serine to tyrosine amino acid substitution in the gene FKS1, and the latter was associated with a phenylalanine to isoleucine substitution in the gene FUR1. These mutations are novel for this pathogen.\n\nInterpretationMultiple differential episodic selection of antifungal resistant genotypes has occurred within a genetically heterogenous population across this outbreak, creating a resilient pathogen and making it difficult to define local-scale patterns of transmission as well as implementing outbreak control measures.\n\nFundingAntimicrobial Research Collaborative, Imperial College London

epidemiology↗

bioRxiv2017

Influenza Interaction with Cocirculating Pathogens, and Its Impact on Surveillance, Pathogenesis and Epidemic Profile: A Key Role for Mathematical Modeling

Evidence is mounting that influenza virus, a major contributor to the global disease burden, interacts with other pathogens infecting the human respiratory tract. Taking into account interactions with other pathogens may be critical to determining the real influenza burden and the full impact of public health policies targeting influenza. That necessity is particularly true for mathematical modeling studies, which have become critical in public health decision-making, despite their usually focusing on lone influenza virus acquisition and infection, thereby making broad oversimplifications regarding pathogen ecology. Herein, we review evidence of influenza virus interaction with bacteria and viruses, and the modeling studies that incorporated some of these. Despite the many studies examining possible associations between influenza and Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Neisseria meningitides, respiratory syncytial virus, human rhinoviruses, human parainfluenza viruses, etc., very few mathematical models have integrated other pathogens alongside influenza. A notable exception is the recent modeling of the pneumococcus-influenza interaction, which highlighted potential influenza-related increased pneumococcal transmission and pathogenicity. That example demonstrates the power of dynamic modeling as an approach to test biological hypotheses concerning interaction mechanisms and estimate the strength of those interactions. We explore how different interference mechanisms may lead to unexpected incidence trends and misinterpretations. Using simple transmission models, we illustrate how existing interactions might impact public health surveillance systems and demonstrate that the development of multipathogen models is essential to assess the true public health burden of influenza, and help improve planning and evaluation of control measures. Finally, we identify the public health needs, surveillance, modeling and biological challenges, and propose avenues of research for the coming years.\n\nAuthor SummaryInfluenza is a major pathogen responsible for important morbidity and mortality burdens worldwide. Mathematical models of influenza virus acquisition have been critical to understanding its epidemiology and planning public health strategies of infection control. It is increasingly clear that microbes do not act in isolation but potentially interact within the host. Hence, studying influenza alone may lead to masking effects or misunderstanding information on its transmission and severity. Herein, we review the literature on bacterial and viral species that interact with the influenza virus, interaction mechanisms, and mathematical modeling studies integrating interactions. We report evidence that, beyond the classic secondary bacterial infections, many pathogenic bacteria and viruses probably interact with influenza. Public health relevance of pathogen interactions is detailed, showing how potential misreading or a narrow outlook might lead to mistaken public health decisionmaking. We describe the role of mechanistic transmission models in investigating this complex system and obtaining insight into interactions between influenza and other pathogens. Finally, we highlight benefits and challenges in modeling, and speculate on new opportunities made possible by taking a broader view: including basic science, clinical relevance and public health.

epidemiology↗