Reconstructing contact network parameters from viral phylogenies

AbstractModels of the spread of disease in a population often make the simplifying assumption that the population is homogeneously mixed, or is divided into homogeneously mixed compartments. However, human populations have complex structures formed by social contacts, which can have a significant influence on the rate of epidemic spread. Contact network models capture this structure by explicitly representing each contact which could possibly lead to a transmission. We developed a method based on kernel approximate Bayesian computation (kernel-ABC) for estimating structural parameters of the contact network underlying an observed viral phylogeny. The method combines adaptive sequential Monte Carlo for ABC, Gillespie simulation for propagating epidemics though networks, and a kernel-based tree similarity score. We used the method to fit the Barabási-Albert network model to simulated transmission trees, and also applied it to viral phylogenies estimated from five published HIV sequence datasets. On simulated data, we found that the preferential attachment power and the number of infected nodes in the network can often be accurately estimated. On the other hand, the mean degree of the network, as well as the total number of nodes, were not estimable with kernel-ABC. We observed substantial heterogeneity in the parameter estimates on real datasets, with point estimates for the preferential attachment power ranging from 0.06 to 1.05. These results underscore the importance of considering contact structures when performing phylodynamic inference. Our method offers the potential to quantitatively investigate the contact network structure underlying viral epidemics.

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Network Models: An Underutilized Tool in Wildlife Epidemiology?

Interdisciplinary Perspectives on Infectious Diseases ◽

10.1155/2011/676949 ◽

2011 ◽

Vol 2011 ◽

pp. 1-12 ◽

Cited By ~ 83

Author(s):

Meggan E. Craft ◽

Damien Caillaud

Keyword(s):

Disease Outbreaks ◽

Network Models ◽

Contact Network ◽

Human Populations ◽

Network Approach ◽

Future Directions ◽

Network Epidemiology ◽

Wildlife Epidemiology ◽

Wildlife Populations ◽

Made In

Although the approach of contact network epidemiology has been increasing in popularity for studying transmission of infectious diseases in human populations, it has generally been an underutilized approach for investigating disease outbreaks in wildlife populations. In this paper we explore the differences between the type of data that can be collected on human and wildlife populations, provide an update on recent advances that have been made in wildlife epidemiology by using a network approach, and discuss why networks might have been underutilized and why networks could and should be used more in the future. We conclude with ideas for future directions and a call for field biologists and network modelers to engage in more cross-disciplinary collaboration.

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Inferring linguistic transmission between generations at the scale of individuals

10.1101/441246 ◽

2018 ◽

Cited By ~ 1

Author(s):

Valentin Thouzeau ◽

Antonin Affholder ◽

Philippe Mennecier ◽

Paul Verdu ◽

Frédéric Austerlitz

Keyword(s):

Linguistic Diversity ◽

Simulated Data ◽

Human Populations ◽

Linguistic Data ◽

Demographic Models ◽

History Of ◽

Best Fitting ◽

Approximate Bayesian ◽

Individual Scale ◽

The Individual

AbstractHistorical linguistics highly benefited from recent methodological advances inspired by phylogenetics. Nevertheless, no currently available method uses contemporaneous within-population linguistic diversity to reconstruct the history of human populations. Here, we develop an approach inspired from population genetics to perform historical linguistic inferences from linguistic data sampled at the individual scale, within a population. We built four demographic models of linguistic transmission at this scale, each model differing by the number of teachers involved during the language acquisition, and the relative roles of these teachers. We then compared the simulated data obtained with these models with real contemporaneous linguistic data sampled in Tajik speakers in Central Asia, an area known for its high within-population linguistic diversity, using approximate Bayesian computation methods. With these statistical methods, we were able to select the models that best explained the data, and inferred the best-fitting parameters under these selected models, demonstrating the feasibility of using contemporaneous within-population linguistic diversity to infer historical features of human cultural evolution.

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Inference of Transmission Network Structure from HIV Phylogenetic Trees

10.1101/059865 ◽

2016 ◽

Author(s):

Federica Giardina ◽

Ethan Obie Romero-Severson ◽

Jan Albert ◽

Tom Britton ◽

Thomas Leitner

Keyword(s):

Phylogenetic Trees ◽

Evolutionary Process ◽

Evolutionary Model ◽

Network Models ◽

Patient Specific ◽

Contact Network ◽

Contact Networks ◽

Different Types ◽

Approximate Bayesian ◽

Hiv 1

AbstractPhylogenetic inference is an attractive mean to reconstruct transmission histories and epidemics. As the interest lies in how HIV-1 spread in a human population, many previous studies have ignored details about the evolutionary process of the pathogen. Because phylogenetics investigates the evolutionary history of the pathogen rather than the spread between hosts per se, we first investigated the effects of including a within-host evolutionary model in epidemiological simulations. In particular, we investigated if the resulting phvlogenv could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivitv across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic. Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phvlogenv (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phvlogenv reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivitv dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.

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Incorporating Contact Network Uncertainty in Individual Level Models of Infectious Disease using Approximate Bayesian Computation

The International Journal of Biostatistics ◽

10.1515/ijb-2017-0092 ◽

2019 ◽

Vol 16 (1) ◽

Cited By ~ 1

Author(s):

Waleed Almutiry ◽

Rob Deardon

Keyword(s):

Infectious Disease ◽

Monte Carlo ◽

Disease Transmission ◽

Approximate Bayesian Computation ◽

Disease Model ◽

Simulated Data ◽

Computation Time ◽

Contact Network ◽

Bayesian Computation ◽

Approximate Bayesian

AbstractInfectious disease transmission between individuals in a heterogeneous population is often best modelled through a contact network. However, such contact network data are often unobserved. Such missing data can be accounted for in a Bayesian data augmented framework using Markov chain Monte Carlo (MCMC). Unfortunately, fitting models in such a framework can be highly computationally intensive. We investigate the fitting of network-based infectious disease models with completely unknown contact networks using approximate Bayesian computation population Monte Carlo (ABC-PMC) methods. This is done in the context of both simulated data, and data from the UK 2001 foot-and-mouth disease epidemic. We show that ABC-PMC is able to obtain reasonable approximations of the underlying infectious disease model with huge savings in computation time when compared to a full Bayesian MCMC analysis.

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Approximate Bayesian Computation Algorithms for Estimating Network Model Parameters

10.1101/106450 ◽

2017 ◽

Author(s):

Ye Zheng ◽

Stéphane Aris-Brosou

Keyword(s):

Monte Carlo ◽

Approximate Bayesian Computation ◽

Sequential Monte Carlo ◽

Likelihood Function ◽

Model Parameters ◽

Human Populations ◽

Bayesian Computation ◽

Transmission Networks ◽

General Network ◽

Approximate Bayesian

AbstractStudies on Approximate Bayesian Computation (ABC) replacing the intractable likelihood function in evaluation of the posterior distribution have been developed for several years. However, their field of application has to date essentially been limited to inference in population genetics. Here, we propose to extend this approach to estimating the structure of transmission networks of viruses in human populations. In particular, we are interested in estimating the transmission parameters under four very general network structures: random, Watts-Strogatz, Barabasi-Albert and an extension that incorporates aging. Estimation was evaluated under three approaches, based on ABC, ABC-Markov chain Monte Carlo (ABC-MCMC) and ABC-Sequential Monte Carlo (ABC-SMC) samplers. We show that ABC-SMC samplers outperform both ABC and ABC-MCMC, achieving high accuracy and low variance in simulations. This approach paves the way to estimating parameters of real transmission networks of transmissible diseases.

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Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models

Journal of The Royal Society Interface ◽

10.1098/rsif.2017.0340 ◽

2017 ◽

Vol 14 (134) ◽

pp. 20170340 ◽

Cited By ~ 6

Author(s):

Aidan C. Daly ◽

Jonathan Cooper ◽

David J. Gavaghan ◽

Chris Holmes

Keyword(s):

Bayesian Inference ◽

Bayesian Methods ◽

Sequential Monte Carlo ◽

Model Parameters ◽

Biological Models ◽

Exact Inference ◽

Modelling Studies ◽

Approximate Bayesian ◽

Approximate Bayesian Inference ◽

Abc Methods

Bayesian methods are advantageous for biological modelling studies due to their ability to quantify and characterize posterior variability in model parameters. When Bayesian methods cannot be applied, due either to non-determinism in the model or limitations on system observability, approximate Bayesian computation (ABC) methods can be used to similar effect, despite producing inflated estimates of the true posterior variance. Owing to generally differing application domains, there are few studies comparing Bayesian and ABC methods, and thus there is little understanding of the properties and magnitude of this uncertainty inflation. To address this problem, we present two popular strategies for ABC sampling that we have adapted to perform exact Bayesian inference, and compare them on several model problems. We find that one sampler was impractical for exact inference due to its sensitivity to a key normalizing constant, and additionally highlight sensitivities of both samplers to various algorithmic parameters and model conditions. We conclude with a study of the O'Hara–Rudy cardiac action potential model to quantify the uncertainty amplification resulting from employing ABC using a set of clinically relevant biomarkers. We hope that this work serves to guide the implementation and comparative assessment of Bayesian and ABC sampling techniques in biological models.

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Two-Way Contact Network Modeling for Identifying the Route of COVID-19 Community Transmission

Informatics ◽

10.3390/informatics8020022 ◽

2021 ◽

Vol 8 (2) ◽

pp. 22

Author(s):

Sung Jin Lee ◽

Sang Eun Lee ◽

Ji-On Kim ◽

Gi Bum Kim

Keyword(s):

Simulated Data ◽

Contact Network ◽

Transmission Route ◽

Time Step ◽

Source Of Infection ◽

Health Authorities ◽

Control And Prevention ◽

Community Transmission ◽

Centrality Analysis ◽

Selection Of

In this study, we address the problem originated from the fact that “The Corona 19 Epidemiological Research Support System,” developed by the Korea Centers for Disease Control and Prevention, is limited to analyzing the Global Positioning System (GPS) information of the confirmed COVID-19 cases alone. Consequently, we study a method that the authority predicts the transmission route of COVID-19 between visitors in the community from a spatiotemporal perspective. This method models a contact network around the first confirmed case, allowing the health authorities to conduct tests on visitors after an outbreak of COVID-19 in the community. After securing the GPS data of community visitors, it traces back to the past from the time the first confirmed case occurred and creates contact clusters at each time step. This is different from other researches that focus on identifying the movement paths of confirmed patients by forward tracing. The proposed method creates the contact network by assigning weights to each contact cluster based on the degree of proximity between contacts. Identifying the source of infection in the contact network can make us predict the transmission route between the first confirmed case and the source of infection and classify the contacts on the transmission route. In this experiment, we used 64,073 simulated data for 100 people and extracted the transmission route and a top 10 list for centrality analysis. The contacts on the route path can be quickly designated as a priority for COVID-19 testing. In addition, it is possible for the authority to extract the subjects with high influence from the centrality theory and use them for additional COVID-19 epidemic investigation that requires urgency. This model is expected to be used in the epidemic investigation requiring the quick selection of close contacts.

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Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm

Entropy ◽

10.3390/e23040384 ◽

2021 ◽

Vol 23 (4) ◽

pp. 384

Author(s):

Rocío Hernández-Sanjaime ◽

Martín González ◽

Antonio Peñalver ◽

Jose J. López-Espín

Keyword(s):

Statistical Theory ◽

Learning Algorithm ◽

Real Life ◽

Simulated Data ◽

Simultaneous Equation ◽

Variational Bayes ◽

Parameter Estimates ◽

Step Method ◽

The One ◽

Simultaneous Equation Models

The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. This paper focuses on SEMs in which data are heterogeneous and tend to form clustering structures in the endogenous-variable dataset. Because the identification of different clusters is not straightforward, a two-step strategy that first forms groups among the endogenous observations and then uses the standard simultaneous equation scheme is provided. Methodologically, the proposed approach is based on a variational Bayes learning algorithm and does not need to be executed for varying numbers of groups in order to identify the one that adequately fits the data. We describe the statistical theory, evaluate the performance of the suggested algorithm by using simulated data, and apply the two-step method to a macroeconomic problem.

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Likelihood-based approach to discriminate mixtures of network models that vary in time

Scientific Reports ◽

10.1038/s41598-021-84085-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Naomi A. Arnold ◽

Raul J. Mondragón ◽

Richard G. Clegg

Keyword(s):

Network Model ◽

Change Point ◽

Network Science ◽

Preferential Attachment ◽

Network Models ◽

Explanatory Models ◽

Growth Mechanisms ◽

Artificial Data ◽

Network Growth ◽

Over Time

AbstractDiscriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure, with a wealth of explanatory models based on these. These models are deliberately simple, commonly with the network growing according to a constant mechanism for its lifetime, to allow for analytical results. We use a likelihood-based framework on artificial data where the network model changes at a known point in time and demonstrate that we can recover the change point from analysis of the network. We then use real datasets and demonstrate how our framework can show the changing importance of network growth mechanisms over time.

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Friendship paradox in growth networks: analytical and empirical analysis

Applied Network Science ◽

10.1007/s41109-021-00391-6 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Sergei P. Sidorov ◽

Sergei V. Mironov ◽

Alexey A. Grigoriev

Keyword(s):

Real World ◽

Preferential Attachment ◽

Empirical Studies ◽

Mean Field ◽

Network Models ◽

Average Degree ◽

Closure Model ◽

Triadic Closure ◽

Attachment Mechanism ◽

Over Time

AbstractMany empirical studies have shown that in social, citation, collaboration, and other types of networks in real world, the degree of almost every node is less than the average degree of its neighbors. This imbalance is well known in sociology as the friendship paradox and states that your friends are more popular than you on average. If we introduce a value equal to the ratio of the average degree of the neighbors for a certain node to the degree of this node (which is called the ‘friendship index’, FI), then the FI value of more than 1 for most nodes indicates the presence of the friendship paradox in the network. In this paper, we study the behavior of the FI over time for networks generated by growth network models. We will focus our analysis on two models based on the use of the preferential attachment mechanism: the Barabási–Albert model and the triadic closure model. Using the mean-field approach, we obtain differential equations describing the dynamics of changes in the FI over time, and accordingly, after obtaining their solutions, we find the expected values of this index over iterations. The results show that the values of FI are decreasing over time for all nodes in both models. However, for networks constructed in accordance with the triadic closure model, this decrease occurs at a much slower rate than for the Barabási–Albert graphs. In addition, we analyze several real-world networks and show that their FI distributions follow a power law. We show that both the Barabási–Albert and the triadic closure networks exhibit the same behavior. However, for networks based on the triadic closure model, the distributions of FI are more heavy-tailed and, in this sense, are closer to the distributions for real networks.

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