91 results found
Plazzotta G, Kwan C, Boyd M, et al., 2016, Effects of memory on the shapes of simple outbreak trees, Scientific Reports, Vol: 6, ISSN: 2045-2322
Genomic tools, including phylogenetic trees derived from sequence data, are increasingly used to understand outbreaks of infectious diseases. One challenge is to link phylogenetic trees to patterns of transmission. Particularly in bacteria that cause chronic infections, this inference is a ected by variable infectious periods and infectivity over time. It is known that non-exponential infectious periods can have substantial e ects on pathogens' transmission dynamics. Here we ask how this non-Markovian nature of an outbreak process a ects the branching trees describing that process, with particular focus on tree shapes. We simulate Crump-Mode-Jagers branching processes and compare di erent patterns of infectivity over time. We nd that memory (non-Markovian-ness) in the process can have a pronounced e ect on the shapes of the outbreak's branching pattern. However, memory also has a pronounced e ect on the sizes of the trees, even when the duration of the simulation is xed. When the sizes of the trees are constrained to a constant value, memory in our processes has little direct e ect on tree shapes, but can bias inference of the birth rate from trees. We compare simulated branching trees to phylogenetic trees from an outbreak of tuberculosis in Canada, and discuss the relevance of memory to this dataset.
Chindelevitch L, Colijn C, Moodley P, et al., 2016, ClassTR: Classifying Within-Host Heterogeneity Based on Tandem Repeats with Application to Mycobacterium tuberculosis Infections., PLOS Computational Biology, Vol: 12, ISSN: 1553-734X
Genomic tools have revealed genetically diverse pathogens within some hosts. Within-host pathogen diversity, which we refer to as "complex infection", is increasingly recognized as a determinant of treatment outcome for infections like tuberculosis. Complex infection arises through two mechanisms: within-host mutation (which results in clonal heterogeneity) and reinfection (which results in mixed infections). Estimates of the frequency of within-host mutation and reinfection in populations are critical for understanding the natural history of disease. These estimates influence projections of disease trends and effects of interventions. The genotyping technique MLVA (multiple loci variable-number tandem repeats analysis) can identify complex infections, but the current method to distinguish clonal heterogeneity from mixed infections is based on a rather simple rule. Here we describe ClassTR, a method which leverages MLVA information from isolates collected in a population to distinguish mixed infections from clonal heterogeneity. We formulate the resolution of complex infections into their constituent strains as an optimization problem, and show its NP-completeness. We solve it efficiently by using mixed integer linear programming and graph decomposition. Once the complex infections are resolved into their constituent strains, ClassTR probabilistically classifies isolates as clonally heterogeneous or mixed by using a model of tandem repeat evolution. We first compare ClassTR with the standard rule-based classification on 100 simulated datasets. ClassTR outperforms the standard method, improving classification accuracy from 48% to 80%. We then apply ClassTR to a sample of 436 strains collected from tuberculosis patients in a South African community, of which 92 had complex infections. We find that ClassTR assigns an alternate classification to 18 of the 92 complex infections, suggesting important differences in practice. By explicitly modeling tandem repeat
Colijn C, Cohen T, 2015, Whole-genome sequencing of Mycobacterium tuberculosis for rapid diagnostics and beyond, Lancet Respiratory Medicine, Vol: 4, Pages: 6-8, ISSN: 2213-2619
Knight GM, Colijn C, Shrestha S, et al., 2015, The Distribution of Fitness Costs of Resistance-Conferring Mutations Is a Key Determinant for the Future Burden of Drug-Resistant Tuberculosis: A Model-Based Analysis, CLINICAL INFECTIOUS DISEASES, Vol: 61, Pages: S147-S154, ISSN: 1058-4838
Colijn C, Cohen T, 2015, How competition governs whether moderate or aggressive treatment minimizes antibiotic resistance., eLife, Vol: 4, ISSN: 2050-084X
Understanding how our use of antimicrobial drugs shapes future levels of drug resistance is crucial. Recently, there has been debate over whether an aggressive (i.e., high dose) or more moderate (i.e., lower dose) treatment of individuals will most limit the emergence and spread of resistant bacteria. In this study, we demonstrate how one can understand and resolve these apparently contradictory conclusions. We show that a key determinant of which treatment strategy will perform best at the individual level is the extent of effective competition between resistant and sensitive pathogens within a host. We extend our analysis to the community level, exploring the spectrum between strict inter-strain competition and strain independence. From this perspective as well, we find that the magnitude of effective competition between resistant and sensitive strains determines whether an aggressive approach or moderate approach minimizes the burden of resistance in the population.
Jombart T, Kendall ML, Almagro-Garcia J, et al., 2015, treespace
Statistical Exploration of Landscapes of Phylogenetic Trees
Kendall ML, Colijn C, 2015, Mapping phylogenetic trees to reveal distinct patterns of evolution
Crisan A, Wong HY, Johnston JC, et al., 2015, Spatio-temporal analysis of tuberculous infection risk among clients of a homeless shelter during an outbreak, INTERNATIONAL JOURNAL OF TUBERCULOSIS AND LUNG DISEASE, Vol: 19, Pages: 1033-1038, ISSN: 1027-3719
Kendall M, Colijn C, 2015, A tree metric using structure and length to capture distinct phylogenetic signals
Phylogenetic trees are a central tool in understanding evolution. They are typically inferred from sequence data, and capture evolutionary relationships through time. It is essential to be able to compare trees from different data sources (e.g. several genes from the same organisms) and different inference methods. We propose a new metric for robust, quantitative comparison of rooted, labeled trees. It enables clear visualizations of tree space, gives meaningful comparisons between trees, and can detect distinct islands of tree topologies in posterior distributions of trees. This makes it possible to select well-supported summary trees. We demonstrate our approach on Dengue fever phylogenies.
Colijn C, Ayabina D, Trotter C, et al., 2015, Competition, coinfection and strain replacement in models of Bordetella Pertussis, Theoretical Population Biology, Vol: 103, Pages: 84-92, ISSN: 1096-0325
Pertussis, or whooping cough, is an important respiratory infection causing considerable infant mortality worldwide. Recently, incidence has risen in countries with strong vaccine programmes and there are concerns about antigenic shift resulting in vaccine evasion. Interactions between pertussis and non-vaccine-preventable strains will play an important role in the evolution and population dynamics of pertussis. In particular, if we are to understand the role strain replacement plays in vaccinated settings, it will be essential to understand how strains or variants of pertussis interact. Here we explore under what conditions we would expect strain replacement to be of concern in pertussis. We develop a dynamic transmission model that allows for coinfection between Bordetella pertussis (the main causative agent of pertussis) and a strain or variant unaffected by the vaccine. We incorporate both neutrality (in the sense of ecological/population genetic neutrality) and immunity into the model, leaving the specificity of the immune response flexible. We find that strain replacement may be considerable when immunity is non-specific. This is in contrast to previous findings where neutrality was not considered. We conclude that the extent to which models reflect ecological neutrality can have a large impact on conclusions regarding strain replacement. This will likely have onward consequences for estimates of vaccine efficacy and cost-effectiveness.Keywords Bordetella pertussis; Strain replacement; Coinfection; Competition; Epidemic; Specific immunity
Kunkel A, Colijn C, Lipsitch M, et al., 2015, How could preventive therapy affect the prevalence of drug resistance? Causes and consequences, Philosophical Transactions of the Royal Society B: Biological Sciences, Vol: 370, ISSN: 1471-2970
Various forms of preventive and prophylactic antimicrobial therapies have been proposed to combat HIV (e.g. pre-exposure prophylaxis), tuberculosis (e.g. isoniazid preventive therapy) and malaria (e.g. intermittent preventive treatment). However, the potential population-level effects of preventative therapy (PT) on the prevalence of drug resistance are not well understood. PT can directly affect the rate at which resistance is acquired among those receiving PT. It can also indirectly affect resistance by altering the rate at which resistance is acquired through treatment for active disease and by modifying the level of competition between transmission of drug-resistant and drug-sensitive pathogens. We propose a general mathematical model to explore the ways in which PT can affect the long-term prevalence of drug resistance. Depending on the relative contributions of these three mechanisms, we find that increasing the level of coverage of PT may result in increases, decreases or non-monotonic changes in the overall prevalence of drug resistance. These results demonstrate the complexity of the relationship between PT and drug resistance in the population. Care should be taken when predicting population-level changes in drug resistance from small pilot studies of PT or estimates based solely on its direct effects.
Plazzotta G, Cohen T, Colijn C, 2015, Magnitude and sources of bias in the detection of mixed strain M-tuberculosis infection, JOURNAL OF THEORETICAL BIOLOGY, Vol: 368, Pages: 67-73, ISSN: 0022-5193
Farhat MR, Shapiro BJ, Sheppard SK, et al., 2014, A phylogeny-based sampling strategy and power calculator informs genome-wide associations study design for microbial pathogens, Genome Medicine, Vol: 6, ISSN: 1756-994X
Whole genome sequencing is increasingly used to study phenotypic variation among infectious pathogens and toevaluate their relative transmissibility, virulence, and immunogenicity. To date, relatively little has been published onhow and how many pathogen strains should be selected for studies associating phenotype and genotype. Thereare specific challenges when identifying genetic associations in bacteria which often comprise highly structuredpopulations. Here we consider general methodological questions related to sampling and analysis focusing onclonal to moderately recombining pathogens. We propose that a matched sampling scheme constitutes anefficient study design, and provide a power calculator based on phylogenetic convergence. We demonstrate thisapproach by applying it to genomic datasets for two microbial pathogens: Mycobacterium tuberculosis andCampylobacter species.
Mills HL, Johnson S, Hickman M, et al., 2014, Errors in reported degrees and respondent driven sampling: Implications for bias, Drug and Alcohol Dependence, Vol: 142, Pages: 120-126, ISSN: 1879-0046
BackgroundRespondent Driven Sampling (RDS) is a network or chain sampling method designed to access individuals from hard-to-reach populations such as people who inject drugs (PWID). RDS surveys are used to monitor behaviour and infection occurence over time; these estimations require adjusting to account for over-sampling of individuals with many contacts. Adjustment is done based on individuals’ reported total number of contacts, assuming these are correct.MethodsData on the number of contacts (degrees) of individuals sampled in two RDS surveys in Bristol, UK, show larger numbers of individuals reporting numbers of contacts in multiples of 5 and 10 than would be expected at random. To mimic these patterns we generate contact networks and explore different methods of mis-reporting degrees. We simulate RDS surveys and explore the sensitivity of adjusted estimates to these different methods.ResultsWe find that inaccurate reporting of degrees can cause large and variable bias in estimates of prevalence or incidence. Our simulations imply that paired RDS surveys could over- or under-estimate any change in prevalence by as much as 25%. These are particularly sensitive to inaccuracies in the degree estimates of individuals with who have low degree.ConclusionsThere is a substantial risk of bias in estimates from RDS if degrees are not correctly reported. This is particularly important when analysing consecutive RDS samples to assess trends in population prevalence and behaviour. RDS questionnaires should be refined to obtain high resolution degree information, particularly from low-degree individuals. Additionally, larger sample sizes can reduce uncertainty in estimates.
Didelot X, Gardy J, Colijn C, 2014, Bayesian Inference of Infectious Disease Transmission from Whole-Genome Sequence Data, MOLECULAR BIOLOGY AND EVOLUTION, Vol: 31, Pages: 1869-1879, ISSN: 0737-4038
Colijn C, Gardy J, 2014, Phylogenetic tree shapes resolve disease transmission patterns., Evol Med Public Health, Vol: 2014, Pages: 96-108, ISSN: 2050-6201
BACKGROUND AND OBJECTIVES: Whole-genome sequencing is becoming popular as a tool for understanding outbreaks of communicable diseases, with phylogenetic trees being used to identify individual transmission events or to characterize outbreak-level overall transmission dynamics. Existing methods to infer transmission dynamics from sequence data rely on well-characterized infectious periods, epidemiological and clinical metadata which may not always be available, and typically require computationally intensive analysis focusing on the branch lengths in phylogenetic trees. We sought to determine whether the topological structures of phylogenetic trees contain signatures of the transmission patterns underlying an outbreak. METHODOLOGY: We use simulated outbreaks to train and then test computational classifiers. We test the method on data from two real-world outbreaks. RESULTS: We show that different transmission patterns result in quantitatively different phylogenetic tree shapes. We describe topological features that summarize a phylogeny's structure and find that computational classifiers based on these are capable of predicting an outbreak's transmission dynamics. The method is robust to variations in the transmission parameters and network types, and recapitulates known epidemiology of previously characterized real-world outbreaks. CONCLUSIONS AND IMPLICATIONS: There are simple structural properties of phylogenetic trees which, when combined, can distinguish communicable disease outbreaks with a super-spreader, homogeneous transmission and chains of transmission. This is possible using genome data alone, and can be done during an outbreak. We discuss the implications for management of outbreaks.
Jombart T, Aanensen DM, Baguelin M, et al., 2014, OutbreakTools: A new platform for disease outbreak analysis using the R software, Epidemics, Vol: 7, Pages: 28-34, ISSN: 1755-4365
The investigation of infectious disease outbreaks relies on the analysis of increasingly complex and diverse data, which offer new prospects for gaining insights into disease transmission processes and informing public health policies. However, the potential of such data can only be harnessed using a number of different, complementary approaches and tools, and a unified platform for the analysis of disease outbreaks is still lacking. In this paper, we present the new R package OutbreakTools, which aims to provide a basis for outbreak data management and analysis in R. OutbreakTools is developed by a community of epidemiologists, statisticians, modellers and bioinformaticians, and implements classes and methods for storing, handling and visualizing outbreak data. It includes real and simulated outbreak datasets. Together with a number of tools for infectious disease epidemiology recently made available in R, OutbreakTools contributes to the emergence of a new, free and open-source platform for the analysis of disease outbreaks.
Mills HL, White E, Colijn C, et al., 2013, HIV transmission from drug injectors to partners who do not inject, and beyond: Modelling the potential for a generalized heterosexual epidemic in St. Petersburg, Russia, DRUG AND ALCOHOL DEPENDENCE, Vol: 133, Pages: 242-247, ISSN: 0376-8716
Mills HL, Cohen T, Colijn C, 2013, Response to Comment on "Community-Wide Isoniazid Preventive Therapy Drives Drug-Resistant Tuberculosis: A Model-Based Analysis", SCIENCE TRANSLATIONAL MEDICINE, Vol: 5, ISSN: 1946-6234
Robinson K, Fyson N, Cohen T, et al., 2013, How the Dynamics and Structure of Sexual Contact Networks Shape Pathogen Phylogenies, PLOS COMPUTATIONAL BIOLOGY, Vol: 9, ISSN: 1553-7358
Nicoli EJ, Trotter CL, Turner KME, et al., 2013, Influenza and RSV make a modest contribution to invasive pneumococcal disease incidence in the UK, JOURNAL OF INFECTION, Vol: 66, Pages: 512-520, ISSN: 0163-4453
Mills HL, Cohen T, Colijn C, 2013, Community-Wide Isoniazid Preventive Therapy Drives Drug-Resistant Tuberculosis: A Model-Based Analysis, SCIENCE TRANSLATIONAL MEDICINE, Vol: 5, ISSN: 1946-6234
Mills HL, Ganesh A, Colijn C, 2013, Pathogen spread on coupled networks: Effect of host and network properties on transmission thresholds, Journal of Theoretical Biology
Mills HL, Colijn C, Vickerman P, et al., 2012, Respondent driven sampling and community structure in a population of injecting drug users, Bristol, UK, DRUG AND ALCOHOL DEPENDENCE, Vol: 126, Pages: 324-332, ISSN: 0376-8716
Cohen T, van Helden PD, Wilson D, et al., 2012, Mixed-Strain Mycobacterium tuberculosis Infections and the Implications for Tuberculosis Treatment and Control, CLINICAL MICROBIOLOGY REVIEWS, Vol: 25, Pages: 708-+, ISSN: 0893-8512
Sergeev R, Colijn C, Murray M, et al., 2012, Only Time Will Tell, SCIENCE TRANSLATIONAL MEDICINE, Vol: 4, ISSN: 1946-6234
Sergeev R, Colijn C, Murray M, et al., 2012, Modeling the Dynamic Relationship Between HIV and the Risk of Drug-Resistant Tuberculosis, SCIENCE TRANSLATIONAL MEDICINE, Vol: 4, ISSN: 1946-6234
Brandes A, Lun DS, Ip K, et al., 2012, Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models, PLOS ONE, Vol: 7, ISSN: 1932-6203
Irving TJ, Blyuss KB, Colijn C, et al., 2012, Modelling meningococcal meningitis in the African meningitis belt, EPIDEMIOLOGY AND INFECTION, Vol: 140, Pages: 897-905, ISSN: 0950-2688
Robinson K, Cohen T, Colijn C, 2012, The dynamics of sexual contact networks: Effects on disease spread and control, THEORETICAL POPULATION BIOLOGY, Vol: 81, Pages: 89-96, ISSN: 0040-5809
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