The Experts below are selected from a list of 8742 Experts worldwide ranked by ideXlab platform

Ira M Longini - One of the best experts on this subject based on the ideXlab platform.

  • molecular Infectious Disease Epidemiology survival analysis and algorithms linking phylogenies to transmission trees
    PLOS Computational Biology, 2016
    Co-Authors: Tom Britton, Eben Kenah, Elizabeth M Halloran, Ira M Longini
    Abstract:

    Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about Disease transmission. In a survival analysis framework, estimation of transmission parameters is based on sums or averages over the possible transmission trees. A phylogeny can increase the precision of these estimates by providing partial information about who infected whom. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. Starting from assumptions about Disease biology and epidemiologic study design, we prove that there is a one-to-one correspondence between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to enumerate these transmission trees and show these can be used to calculate likelihoods that incorporate both epidemiologic data and a phylogeny. A simulation study confirms that this leads to more efficient estimates of hazard ratios for Infectiousness and baseline hazards of Infectious contact, and we use these methods to analyze data from a foot-and-mouth Disease virus outbreak in the United Kingdom in 2001. These results demonstrate the importance of data on individuals who escape infection, which is often overlooked. The combination of survival analysis and algorithms linking phylogenies to transmission trees is a rigorous but flexible statistical foundation for molecular Infectious Disease Epidemiology.

  • algorithms linking phylogenetic and transmission trees for molecular Infectious Disease Epidemiology
    arXiv: Quantitative Methods, 2015
    Co-Authors: Eben Kenah, Tom Britton, Elizabeth M Halloran, Ira M Longini
    Abstract:

    Recent work has considered the use of densely-sampled genetic data to reconstruct the transmission trees linking infectors and infectees in outbreaks. Because transmission trees from one outbreak do not generalize to future outbreaks, scientific insights that can inform public health policy are more likely to be obtained by using genetic sequence data to estimate transmission parameters more precisely (e.g., covariate effects on Infectiousness and susceptibility). In a survival analysis framework, transmission parameter estimation is based on sums or averages over possible transmission trees. By providing partial information about who-infected-whom, a phylogeny can increase the efficiency of these estimates. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. We show that there is a one-to-one relationship between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to find the set of possible hosts at each interior node, to generate all possible transmission trees given these host sets, and to assign branching times to a phylogeny with known interior node hosts. For any possible transmission tree, there is at least one assignment of branching times in the phylogeny that is consistent with the epidemiologic data. Finally, the host set algorithm can be adapted to account for known branching times in the phylogeny. A simulation study confirms that a phylogeny substantially increases the efficiency of estimated hazard ratios for Infectiousness and susceptibility. We use these methods to analyze data from foot-and-mouth Disease virus outbreaks in the United Kingdom in 2001 and 2007.

Christophe Fraser - One of the best experts on this subject based on the ideXlab platform.

  • genomic Infectious Disease Epidemiology in partially sampled and ongoing outbreaks
    Molecular Biology and Evolution, 2017
    Co-Authors: Xavier Didelot, Christophe Fraser, Jennifer L Gardy, Caroline Colijn
    Abstract:

    Genomic data are 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 coloring 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 coloring approach can incorporate a variable number of unique colors 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 this transmission tree inference methodology in an R package called TransPhylo, which is freely available from https://github.com/xavierdidelot/TransPhylo.

  • genomic Infectious Disease Epidemiology in partially sampled and ongoing outbreaks
    bioRxiv, 2016
    Co-Authors: Xavier Didelot, Christophe Fraser, Jennifer L Gardy, Caroline Colijn
    Abstract:

    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 .

  • seasonal Infectious Disease Epidemiology
    Proceedings of The Royal Society B: Biological Sciences, 2006
    Co-Authors: Nicholas C Grassly, Christophe Fraser
    Abstract:

    formally examined. This paper examines the causes and consequences of seasonality, and in so doing derives several new results concerning vaccination strategy and the interpretation of Disease outbreak data. It begins with a brief review of published scientific studies in support of different causes of seasonality in Infectious Diseases of humans, identifying four principal mechanisms and their association with different routes of transmission. It then describes the consequences of seasonality for R0, Disease outbreaks, endemic dynamics and persistence. Finally, a mathematical analysis of routine and pulse vaccination programmes for seasonal infections is presented. The synthesis of seasonal Infectious Disease Epidemiology attempted by this paper highlights the need for further empirical and theoretical work.

Eben Kenah - One of the best experts on this subject based on the ideXlab platform.

  • molecular Infectious Disease Epidemiology survival analysis and algorithms linking phylogenies to transmission trees
    PLOS Computational Biology, 2016
    Co-Authors: Tom Britton, Eben Kenah, Elizabeth M Halloran, Ira M Longini
    Abstract:

    Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about Disease transmission. In a survival analysis framework, estimation of transmission parameters is based on sums or averages over the possible transmission trees. A phylogeny can increase the precision of these estimates by providing partial information about who infected whom. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. Starting from assumptions about Disease biology and epidemiologic study design, we prove that there is a one-to-one correspondence between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to enumerate these transmission trees and show these can be used to calculate likelihoods that incorporate both epidemiologic data and a phylogeny. A simulation study confirms that this leads to more efficient estimates of hazard ratios for Infectiousness and baseline hazards of Infectious contact, and we use these methods to analyze data from a foot-and-mouth Disease virus outbreak in the United Kingdom in 2001. These results demonstrate the importance of data on individuals who escape infection, which is often overlooked. The combination of survival analysis and algorithms linking phylogenies to transmission trees is a rigorous but flexible statistical foundation for molecular Infectious Disease Epidemiology.

  • algorithms linking phylogenetic and transmission trees for molecular Infectious Disease Epidemiology
    arXiv: Quantitative Methods, 2015
    Co-Authors: Eben Kenah, Tom Britton, Elizabeth M Halloran, Ira M Longini
    Abstract:

    Recent work has considered the use of densely-sampled genetic data to reconstruct the transmission trees linking infectors and infectees in outbreaks. Because transmission trees from one outbreak do not generalize to future outbreaks, scientific insights that can inform public health policy are more likely to be obtained by using genetic sequence data to estimate transmission parameters more precisely (e.g., covariate effects on Infectiousness and susceptibility). In a survival analysis framework, transmission parameter estimation is based on sums or averages over possible transmission trees. By providing partial information about who-infected-whom, a phylogeny can increase the efficiency of these estimates. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. We show that there is a one-to-one relationship between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to find the set of possible hosts at each interior node, to generate all possible transmission trees given these host sets, and to assign branching times to a phylogeny with known interior node hosts. For any possible transmission tree, there is at least one assignment of branching times in the phylogeny that is consistent with the epidemiologic data. Finally, the host set algorithm can be adapted to account for known branching times in the phylogeny. A simulation study confirms that a phylogeny substantially increases the efficiency of estimated hazard ratios for Infectiousness and susceptibility. We use these methods to analyze data from foot-and-mouth Disease virus outbreaks in the United Kingdom in 2001 and 2007.

Caroline Colijn - One of the best experts on this subject based on the ideXlab platform.

  • genomic Infectious Disease Epidemiology in partially sampled and ongoing outbreaks
    Molecular Biology and Evolution, 2017
    Co-Authors: Xavier Didelot, Christophe Fraser, Jennifer L Gardy, Caroline Colijn
    Abstract:

    Genomic data are 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 coloring 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 coloring approach can incorporate a variable number of unique colors 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 this transmission tree inference methodology in an R package called TransPhylo, which is freely available from https://github.com/xavierdidelot/TransPhylo.

  • genomic Infectious Disease Epidemiology in partially sampled and ongoing outbreaks
    bioRxiv, 2016
    Co-Authors: Xavier Didelot, Christophe Fraser, Jennifer L Gardy, Caroline Colijn
    Abstract:

    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 .

Justin Lessler - One of the best experts on this subject based on the ideXlab platform.

  • trends in the mechanistic and dynamic modeling of Infectious Diseases
    Current Epidemiology Reports, 2016
    Co-Authors: Justin Lessler, Henrik Salje, Kate M Grabowski, Andrew S Azman, Isabel Rodriguezbarraquer
    Abstract:

    The dynamics of Infectious Disease epidemics are driven by interactions between individuals with differing Disease status (e.g., susceptible, infected, immune). Mechanistic models that capture the dynamics of such “dependent happenings” are a fundamental tool of Infectious Disease Epidemiology. Recent methodological advances combined with access to new data sources and computational power have resulted in an explosion in the use of dynamic models in the analysis of emerging and established Infectious Diseases. Increasing use of models to inform practical public health decision making has challenged the field to develop new methods to exploit available data and appropriately characterize the uncertainty in the results. Here, we discuss recent advances and areas of active research in the mechanistic and dynamic modeling of Infectious Disease. We highlight how a growing emphasis on data and inference, novel forecasting methods, and increasing access to “big data” are changing the field of Infectious Disease dynamics. We showcase the application of these methods in phylodynamic research, which combines mechanistic models with rich sources of molecular data to tie genetic data to population-level Disease dynamics. As dynamics and mechanistic modeling methods mature and are increasingly tied to principled statistical approaches, the historic separation between the Infectious Disease dynamics and “traditional” epidemiologic methods is beginning to erode; this presents new opportunities for cross pollination between fields and novel applications.

  • measuring spatial dependence for Infectious Disease Epidemiology
    PLOS ONE, 2016
    Co-Authors: Justin Lessler, Henrik Salje, Kate M Grabowski, Derek A T Cummings
    Abstract:

    Global spatial clustering is the tendency of points, here cases of Infectious Disease, to occur closer together than expected by chance. The extent of global clustering can provide a window into the spatial scale of Disease transmission, thereby providing insights into the mechanism of spread, and informing optimal surveillance and control. Here the authors present an interpretable measure of spatial clustering, τ, which can be understood as a measure of relative risk. When biological or temporal information can be used to identify sets of potentially linked and likely unlinked cases, this measure can be estimated without knowledge of the underlying population distribution. The greater our ability to distinguish closely related (i.e., separated by few generations of transmission) from more distantly related cases, the more closely τ will track the true scale of transmission. The authors illustrate this approach using examples from the analyses of HIV, dengue and measles, and provide an R package implementing the methods described. The statistic presented, and measures of global clustering in general, can be powerful tools for analysis of spatially resolved data on Infectious Diseases.