Bayesian Inference for Contact Networks Given Epidemic Data

Groendyke, Chris; Welch, David; Hunter, David R.

doi:10.1111/j.1467-9469.2010.00721.x

Cited by 65 publications

(82 citation statements)

References 22 publications

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“…If we have reason to believe that a particular individual is responsible for this infection, we can incorporate this information by multiplying the weight given to this individual in the prior distribution by a factor greater than 1. Groendyke, Welch, and Hunter (2011) studies the effect of various weights on the resulting posterior distribution of the transmission tree. In the cases where it is necessary to infer the exposure and/or infectious times of individuals, we use a flat (uninformative) prior distribution for these times.…”

Section: Priorsmentioning

confidence: 99%

“…The network and transmission tree are included so that the likelihood can be more easily calculated, but these parameters may not always be of interest. Full details of the implementation of the algorithm are given in Groendyke et al (2011) and Groendyke et al (2012).…”

Section: Inferencementioning

confidence: 99%

“…This algorithm is based on that described in Britton and O'Neill (2002), Groendyke et al (2011), and Groendyke et al (2012):…”

Section: Inferencementioning

confidence: 99%

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epinet: An R Package to Analyze Epidemics Spread across Contact Networks

Groendyke¹,

Welch²

2018

J. Stat. Soft.

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We present the R package epinet, which provides tools for analyzing the spread of epidemics through populations. We assume that the relationships among individuals in a population are modeled by a contact network described by an exponential-family random graph model and that the disease being studied spreads across the edges of this network from infectious to susceptible individuals. We use a susceptible-exposed-infectiousremoved compartmental model to describe the progress of the disease within each host. We describe the functionality of the package, which consists of routines that perform simulation, plotting, and inference. The main inference routine utilizes a Bayesian approach and a Markov chain Monte Carlo algorithm. We demonstrate the use of the package through two examples, one involving simulated data and one using data from an actual measles outbreak.

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Section: Priorsmentioning

confidence: 99%

Section: Inferencementioning

confidence: 99%

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epinet: An R Package to Analyze Epidemics Spread across Contact Networks

Groendyke¹,

Welch²

2018

J. Stat. Soft.

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“…Some interesting previous work is: a hierarchical network model for the epidemic of A/H1N1 virus spreading in Romania [17], a network model to study the RSV spread in the population of the Spanish region of Valencia [15,18], a scale-free network modeling dengue in Singapore [19], a complete graph network modeling the social obesity epidemic [20], a social network to investigate the 2007 outbreak of equine influenza in Australia [21], a network study of a measles outbreak in Hagelloch, Germany, in 1861 consisting of 188 affected individuals [22] and a network model that describes the empirical data of the 2000/2001 cholera epidemic which took place in the Kwa Zulu-Natal Province, South Africa [23]. In [24] authors developed an interesting agent-based model, in which each individual is explicitly represented and vector populations are linked to precipitation estimates.…”

Section: Introductionmentioning

confidence: 99%

Modelling influenza A(H1N1) 2009 epidemics using a random network in a distributed computing environment

et al. 2015

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In this paper we propose the use of a random network model for simulating and understanding the epidemics of influenza A(H1N1). The proposed model is used to simulate the transmission process of influenza A(H1N1) in a community region of Venezuela using distributed computing in order to accomplish many realizations of the underlying random process. These large scale epidemic simulations have recently become an important application of high-performance computing. The network model proposed performs better than the traditional epidemic model based on ordinary differential equations since it adjusts better to the irregularity of the real world data. In addition, the network model allows the consideration of many possibilities regarding the spread of influenza at the population level. The results presented here show how well the SEIR model fits the data for the AH1N1 time series despite the irregularity of the data and returns parameter values that are in good agreement with the medical data regarding AH1N1 influenza virus. This versatile network model approach may be applied to the simulation of the transmission dynamics of several epidemics in human networks. In addition, the simulation can provide useful information for the understanding, prediction and control of the transmission of influenza A(H1N1) epidemics.

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“…Since then, various approaches have been proposed for influenza detection and prediction (Groendyke et al, 2011;Moreno et al, 2002;Mugglin et al, 2002).…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the ACL-IJCNLP 2015 Student Research Workshop

Chen¹,

Ivanova

Pavlick

et al. 2015

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ISBN 978-1-941643-74-7 ii Introduction Welcome to the ACJ-IJCNLP 2015 Student Research Workshop! Following the tradition of the previous years' workshops, we have two tracks: research papers and thesis proposals. The research papers track is as a venue for Ph.D. students, masters students, and advanced undergraduates to describe completed work or work-in-progress along with preliminary results. The thesis proposal track is offered for advanced Ph.D. students who have decided on a thesis topic and are interested in feedback about their proposal and ideas about future directions for their work.We received in total 18 submissions: 5 thesis proposals and 13 research papers. Of these, we accepted 3 thesis proposals and 4 research papers, giving an acceptance rate of 39% overall: 60% for thesis proposals and 31% for research papers.This year, all of the accepted papers will be presented as posters alongside the main conference short paper posters on the second day of the conference. In addition, authors will each have a chance to give a short oral presentation to advertise their work. The oral session will be held on immediately prior to the poster session.Mentoring programs are a central part of the SRW. This year, students had the opportunity to participate in a pre-submission mentoring program prior to the submission deadline. The mentoring offers students a chance to receive comments from an experienced researcher in the field, in order to improve the quality of the writing and presentation before making their final submission. Fourteen authors participated in the pre-submission mentoring, more than twice as many as participated last year.In addition, authors of accepted papers are matched with mentors who will meet with the students in person during the workshop. This year, each research paper is assigned two mentors and each thesis proposal is assigned one mentor. Each mentor will prepare in-depth comments and questions prior to the student's presentation, and will provide discussion and feedback during the workshop.We are very grateful for the generous financial support from BAOBAG Language Solutions, Google, the Don and Betty Walker Scholarship Fund, and the National Science Foundation. The support of our sponsors allows the SRW to cover the travel and lodging expenses of the authors, keeping the workshop accessible to all students.We would also like to thank our program committee members for their constructive reviews for each paper and all of our mentors for donating their time to work one-on-one with our student authors. Thank you to our faculty advisers for their advice and guidance, and to the ACL-IJCNLP 2015 organizing committee for their constant support. Finally, a huge thank you to all students for their submissions and their participation in this year's SRW. Looking forward to a wonderful workshop! AbstractSpoken dialogue systems (SDS) are rapidly appearing in various smart devices (smartphone, smart-TV, in-car navigating system, etc). The key role in a successful SDS is a spoken language under...

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Bayesian Inference for Contact Networks Given Epidemic Data

Cited by 65 publications

References 22 publications

epinet: An R Package to Analyze Epidemics Spread across Contact Networks

epinet: An R Package to Analyze Epidemics Spread across Contact Networks

Modelling influenza A(H1N1) 2009 epidemics using a random network in a distributed computing environment

Proceedings of the ACL-IJCNLP 2015 Student Research Workshop

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