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Worby, Colin

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Worby

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Colin

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Worby, Colin
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Now showing 1 - 9 of 9
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    Population effect of influenza vaccination under co-circulation of non-vaccine variants and the case for a bivalent A/H3N2 vaccine component
    (Elsevier BV, 2017) Worby, Colin; Wallinga, Jacco; Lipsitch, Marc; Goldstein, Edward
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    Within-Host Bacterial Diversity Hinders Accurate Reconstruction of Transmission Networks from Genomic Distance Data
    (Public Library of Science, 2014) Worby, Colin; Lipsitch, Marc; Hanage, William
    The prospect of using whole genome sequence data to investigate bacterial disease outbreaks has been keenly anticipated in many quarters, and the large-scale collection and sequencing of isolates from cases is becoming increasingly feasible. While sequence data can provide many important insights into disease spread and pathogen adaptation, it remains unclear how successfully they may be used to estimate individual routes of transmission. Several studies have attempted to reconstruct transmission routes using genomic data; however, these have typically relied upon restrictive assumptions, such as a shared topology of the phylogenetic tree and a lack of within-host diversity. In this study, we investigated the potential for bacterial genomic data to inform transmission network reconstruction. We used simulation models to investigate the origins, persistence and onward transmission of genetic diversity, and examined the impact of such diversity on our estimation of the epidemiological relationship between carriers. We used a flexible distance-based metric to provide a weighted transmission network, and used receiver-operating characteristic (ROC) curves and network entropy to assess the accuracy and uncertainty of the inferred structure. Our results suggest that sequencing a single isolate from each case is inadequate in the presence of within-host diversity, and is likely to result in misleading interpretations of transmission dynamics – under many plausible conditions, this may be little better than selecting transmission links at random. Sampling more frequently improves accuracy, but much uncertainty remains, even if all genotypes are observed. While it is possible to discriminate between clusters of carriers, individual transmission routes cannot be resolved by sequence data alone. Our study demonstrates that bacterial genomic distance data alone provide only limited information on person-to-person transmission dynamics.
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    Examining the role of different age groups, and of vaccination during the 2012 Minnesota pertussis outbreak
    (Nature Publishing Group, 2015) Worby, Colin; Kenyon, Cynthia; Lynfield, Ruth; Lipsitch, Marc; Goldstein, Edward
    There is limited information on the roles of different age groups during pertussis outbreaks. Little is known about vaccine effectiveness against pertussis infection (both clinically apparent and subclinical), which is different from effectiveness against reportable pertussis disease, with the former influencing the impact of vaccination on pertussis transmission in the community. For the 2012 pertussis outbreak in Minnesota, we estimated odds ratios for case counts in pairs of population groups before vs. after the epidemic’s peak. We found children aged 11–12y, 13–14y and 8–10y experienced the greatest rates of depletion of susceptible individuals during the outbreak’s ascent, with all ORs for each of those age groups vs. groups outside this age range significantly above 1, with the highest ORs for ages 11–12y. Receipt of the fifth dose of DTaP was associated with a decreased relative role during the outbreak’s ascent compared to non-receipt [OR 0.16 (0.01, 0.84) for children aged 5, 0.13 (0.003, 0.82) for ages 8–10y, indicating a protective effect of DTaP against pertussis infection. No analogous effect of Tdap was detected. Our results suggest that children aged 8–14y played a key role in propagating this outbreak. The impact of immunization with Tdap on pertussis infection requires further investigation.
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    The Distribution of Pairwise Genetic Distances: A Tool for Investigating Disease Transmission
    (Genetics Society of America, 2014) Worby, Colin; Chang, Hsiao-Han; Hanage, William; Lipsitch, Marc
    Whole-genome sequencing of pathogens has recently been used to investigate disease outbreaks and is likely to play a growing role in real-time epidemiological studies. Methods to analyze high-resolution genomic data in this context are still lacking, and inferring transmission dynamics from such data typically requires many assumptions. While recent studies have proposed methods to infer who infected whom based on genetic distance between isolates from different individuals, the link between epidemiological relationship and genetic distance is still not well understood. In this study, we investigated the distribution of pairwise genetic distances between samples taken from infected hosts during an outbreak. We proposed an analytically tractable approximation to this distribution, which provides a framework to evaluate the likelihood of particular transmission routes. Our method accounts for the transmission of a genetically diverse inoculum, a possibility overlooked in most analyses. We demonstrated that our approximation can provide a robust estimation of the posterior probability of transmission routes in an outbreak and may be used to rule out transmission events at a particular probability threshold. We applied our method to data collected during an outbreak of methicillin-resistant Staphylococcus aureus, ruling out several potential transmission links. Our study sheds light on the accumulation of mutations in a pathogen during an epidemic and provides tools to investigate transmission dynamics, avoiding the intensive computation necessary in many existing methods.
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    Identifying the effect of patient sharing on between-hospital genetic differentiation of methicillin-resistant Staphylococcus aureus
    (BioMed Central, 2016) Chang, Hsiao-Han; Dordel, Janina; Donker, Tjibbe; Worby, Colin; Feil, Edward J.; Hanage, William; Bentley, Stephen D.; Huang, Susan S.; Lipsitch, Marc
    Background: Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most common healthcare-associated pathogens. To examine the role of inter-hospital patient sharing on MRSA transmission, a previous study collected 2,214 samples from 30 hospitals in Orange County, California and showed by spa typing that genetic differentiation decreased significantly with increased patient sharing. In the current study, we focused on the 986 samples with spa type t008 from the same population. Methods: We used genome sequencing to determine the effect of patient sharing on genetic differentiation between hospitals. Genetic differentiation was measured by between-hospital genetic diversity, FST, and the proportion of nearly identical isolates between hospitals. Results: Surprisingly, we found very similar genetic diversity within and between hospitals, and no significant association between patient sharing and genetic differentiation measured by FST. However, in contrast to FST, there was a significant association between patient sharing and the proportion of nearly identical isolates between hospitals. We propose that the proportion of nearly identical isolates is more powerful at determining transmission dynamics than traditional estimators of genetic differentiation (FST) when gene flow between populations is high, since it is more responsive to recent transmission events. Our hypothesis was supported by the results from coalescent simulations. Conclusions: Our results suggested that there was a high level of gene flow between hospitals facilitated by patient sharing, and that the proportion of nearly identical isolates is more sensitive to population structure than FST when gene flow is high. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0274-3) contains supplementary material, which is available to authorized users.
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    'SEEDY' (Simulation of Evolutionary and Epidemiological Dynamics): An R Package to Follow Accumulation of Within-Host Mutation in Pathogens
    (Public Library of Science, 2015) Worby, Colin; Read, Timothy D.
    Genome sequencing is an increasingly common component of infectious disease outbreak investigations. However, the relationship between pathogen transmission and observed genetic data is complex, and dependent on several uncertain factors. As such, simulation of pathogen dynamics is an important tool for interpreting observed genomic data in an infectious disease outbreak setting, in order to test hypotheses and to explore the range of outcomes consistent with a given set of parameters. We introduce ‘seedy’, an R package for the simulation of evolutionary and epidemiological dynamics (http://cran.r-project.org/web/packages/seedy/). Our software implements stochastic models for the accumulation of mutations within hosts, as well as individual-level disease transmission. By allowing variables such as the transmission bottleneck size, within-host effective population size and population mixing rates to be specified by the user, our package offers a flexible framework to investigate evolutionary dynamics during disease outbreaks. Furthermore, our software provides theoretical pairwise genetic distance distributions to provide a likelihood of person-to-person transmission based on genomic observations, and using this framework, implements transmission route assessment for genomic data collected during an outbreak. Our open source software provides an accessible platform for users to explore pathogen evolution and outbreak dynamics via simulation, and offers tools to assess observed genomic data in this context.
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    Impact of mupirocin resistance on the transmission and control of healthcare-associated MRSA
    (Oxford University Press, 2015) Deeny, Sarah R.; Worby, Colin; Tosas Auguet, Olga; Cooper, Ben S.; Edgeworth, Jonathan; Cookson, Barry; Robotham, Julie V.
    Objectives: The objectives of this study were to estimate the relative transmissibility of mupirocin-resistant (MupR) and mupirocin-susceptible (MupS) MRSA strains and evaluate the long-term impact of MupR on MRSA control policies. Methods: Parameters describing MupR and MupS strains were estimated using Markov chain Monte Carlo methods applied to data from two London teaching hospitals. These estimates parameterized a model used to evaluate the long-term impact of MupR on three mupirocin usage policies: ‘clinical cases’, ‘screen and treat’ and ‘universal’. Strategies were assessed in terms of colonized and infected patient days and scenario and sensitivity analyses were performed. Results: The transmission probability of a MupS strain was 2.16 (95% CI 1.38–2.94) times that of a MupR strain in the absence of mupirocin usage. The total prevalence of MupR in colonized and infected MRSA patients after 5 years of simulation was 9.1% (95% CI 8.7%–9.6%) with the ‘screen and treat’ mupirocin policy, increasing to 21.3% (95% CI 20.9%–21.7%) with ‘universal’ mupirocin use. The prevalence of MupR increased in 50%–75% of simulations with ‘universal’ usage and >10% of simulations with ‘screen and treat’ usage in scenarios where MupS had a higher transmission probability than MupR. Conclusions: Our results provide evidence from a clinical setting of a fitness cost associated with MupR in MRSA strains. This provides a plausible explanation for the low levels of mupirocin resistance seen following ‘screen and treat’ mupirocin usage. From our simulations, even under conservative estimates of relative transmissibility, we see long-term increases in the prevalence of MupR given ‘universal’ use.
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    On the Relative Role of Different Age Groups During Epidemics Associated With Respiratory Syncytial Virus
    (Oxford University Press (OUP), 2017) Goldstein, Edward; Nguyen, Hieu H; Liu, Patrick; Viboud, Cecile; Steiner, Claudia A; Worby, Colin; Lipsitch, Marc
    Background While circulation of respiratory syncytial virus (RSV) results in high rates of hospitalization, particularly among young children and elderly individuals, little is known about the role of different age groups in propagating annual RSV epidemics. Methods We evaluate the roles played by individuals in different age groups during RSV epidemics in the United States between 2001 and 2012, using the previously defined relative risk (RR) statistic estimated from the hospitalization data from the Healthcare Cost and Utilization Project. Transmission modeling was used to examine the robustness of our inference method. Results Children aged 3–4 years and 5–6 years each had the highest RR estimate for 5 of 11 seasons included in this study, with RSV hospitalization rates in infants being generally higher during seasons when children aged 5–6 years had the highest RR estimate. Children aged 2 years had the highest RR estimate during one season. RR estimates in infants and individuals aged ≥11 years were mostly lower than in children aged 1–10 years. Highest RR values aligned with groups for which vaccination had the largest impact on epidemic dynamics in most model simulations. Conclusions Our estimates suggest the prominent relative roles of children aged ≤10 years (particularly among those aged 3–6 years) in propagating RSV epidemics. These results, combined with further modeling work, should help inform RSV vaccination policies.
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    THE REAL McCOIL: A method for the concurrent estimation of the complexity of infection and SNP allele frequency for malaria parasites
    (Public Library of Science, 2017) Chang, Hsiao-Han; Worby, Colin; Yeka, Adoke; Nankabirwa, Joaniter; Kamya, Moses R.; Staedke, Sarah G.; Dorsey, Grant; Murphy, Maxwell; Neafsey, Daniel E.; Jeffreys, Anna E.; Hubbart, Christina; Rockett, Kirk A.; Amato, Roberto; Kwiatkowski, Dominic P.; Buckee, Caroline; Greenhouse, Bryan
    As many malaria-endemic countries move towards elimination of Plasmodium falciparum, the most virulent human malaria parasite, effective tools for monitoring malaria epidemiology are urgent priorities. P. falciparum population genetic approaches offer promising tools for understanding transmission and spread of the disease, but a high prevalence of multi-clone or polygenomic infections can render estimation of even the most basic parameters, such as allele frequencies, challenging. A previous method, COIL, was developed to estimate complexity of infection (COI) from single nucleotide polymorphism (SNP) data, but relies on monogenomic infections to estimate allele frequencies or requires external allele frequency data which may not available. Estimates limited to monogenomic infections may not be representative, however, and when the average COI is high, they can be difficult or impossible to obtain. Therefore, we developed THE REAL McCOIL, Turning HEterozygous SNP data into Robust Estimates of ALelle frequency, via Markov chain Monte Carlo, and Complexity Of Infection using Likelihood, to incorporate polygenomic samples and simultaneously estimate allele frequency and COI. This approach was tested via simulations then applied to SNP data from cross-sectional surveys performed in three Ugandan sites with varying malaria transmission. We show that THE REAL McCOIL consistently outperforms COIL on simulated data, particularly when most infections are polygenomic. Using field data we show that, unlike with COIL, we can distinguish epidemiologically relevant differences in COI between and within these sites. Surprisingly, for example, we estimated high average COI in a peri-urban subregion with lower transmission intensity, suggesting that many of these cases were imported from surrounding regions with higher transmission intensity. THE REAL McCOIL therefore provides a robust tool for understanding the molecular epidemiology of malaria across transmission settings.