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Bayesian inference of COVID-19 spreading rates in South Africa
Rendani Mbuvha,
Tshilidzi Marwala
University of the Witwatersrand
University of Johannesburg
Research output
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Contribution to journal
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Article
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peer-review
48
Citations (Scopus)
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Dive into the research topics of 'Bayesian inference of COVID-19 spreading rates in South Africa'. Together they form a unique fingerprint.
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Keyphrases
Epidemiological Model
66%
National Lockdown
33%
Imported Case
33%
Testing Program
33%
Mass Testing
33%
Travel Ban
33%
Mass Screening
33%
Confirmed Cases
33%
Private Laboratory
33%
Screening Program
33%
Imported Infections
33%
Parameter Inference
33%
Case-driven
33%
Bayesian Parameter Estimation
33%
Trajectory Prediction
33%
Medicine and Dentistry
Disease
100%
COVID-19
100%
Infection
50%
Severe Acute Respiratory Syndrome Coronavirus 2
50%
Mass Screening
50%
Monte Carlo Method
50%
Agricultural and Biological Sciences
Markov Chain
100%
Monte Carlo Method
100%
Severe Acute Respiratory Syndrome Coronavirus
100%