| 1c Branching process models; compartmental SIR model Method1 #714687
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+Citations (1)
- CitationsAjouter une citationList by: CiterankMapLink[1] A contact tracing SIR model for randomly mixed populations
En citant: Sam Bednarski, Laura L.E. Cowen, Junling Ma,Tanya Philippsen, P. van den Driessche, Manting Wang
Publication date: 2 June 2022
Publication info: Journal of Biological Dynamics, Volume 16, 2022 - Issue 1, Pages 859-879
Cité par: David Price 8:06 PM 5 November 2023 GMT
Citerank: (4) 679818Junling MaI am an associate professor in Department of Mathematics and Statistics, University of Victoria. I received B.Sc. in Applied Mathematics in 1994, and M.Sc in Applied Mathematics in 1997, from Xi'an Jiaotong University, China. I received Ph.D. in Applied Mathematics from Princeton University in 2003.10019D3ABAB, 679826Laura CowenAssociate Professor in the Department of Mathematics and Statistics at the University of Victoria.10019D3ABAB, 701037MfPH – Publications144B5ACA0, 715294Contact tracing859FDEF6
URL:
DOI: https://doi.org/10.1080/17513758.2022.2153938
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| Extrait - [Journal of Biological Dynamics, 2 Jun 2022]
Contact tracing is an important intervention measure to control infectious diseases. We present a new approach that borrows the edge dynamics idea from network models to track contacts included in a compartmental SIR model for an epidemic spreading in a randomly mixed population. Unlike network models, our approach does not require statistical information of the contact network, data that are usually not readily available. The model resulting from this new approach allows us to study the effect of contact tracing and isolation of diagnosed patients on the control reproduction number and number of infected individuals. We estimate the effects of tracing coverage and capacity on the effectiveness of contact tracing. Our approach can be extended to more realistic models that incorporate latent and asymptomatic compartments. |