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Bayesian estimation of transmission networks for infectious diseases
Jianing Xu
, Huimin Hu
, Gregory Ellison
,
Lili Yu
, Christopher C. Whalen
, Liang Liu
Biostatistics, Epidemiology & Environmental Health Sciences
University of Georgia
Research output
:
Contribution to journal
›
Article
›
peer-review
2
Scopus citations
Overview
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Dive into the research topics of 'Bayesian estimation of transmission networks for infectious diseases'. Together they form a unique fingerprint.
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Engineering
Electric Power Transmission Networks
100%
Bayes Estimator
100%
Direct Transmission
50%
Hypothesis Test
33%
Simulation Result
16%
Critical Role
16%
Model Parameter
16%
Bayesian Approach
16%
Bayesian Model
16%
Latent Period
16%
Mathematics
Bayesian Estimation
100%
Transmission Network
100%
Bayesian
50%
Transmission Model
50%
Hypothesis Test
33%
Bayesian Model
16%
Bayesian Approach
16%
Latent Period
16%
Computer Science
Bayes Estimator
100%
Transmission Network
100%
Transmission Dynamic
33%
Hypothesis Test
33%
Bayesian Approach
16%
Temporal Data
16%
Bayesian Model
16%
Effective Population Size
16%
Biochemistry, Genetics and Molecular Biology
Dynamics
100%
Effective Population Size
50%
Mycobacterium Tuberculosis
50%
Medicine and Dentistry
Transmission Network
100%
Mycobacterium Tuberculosis
16%
Effective Population Size
16%