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Multiscale model within-host and between-host for viral infectious diseases

Overview of attention for article published in Journal of Mathematical Biology, May 2018
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Title
Multiscale model within-host and between-host for viral infectious diseases
Published in
Journal of Mathematical Biology, May 2018
DOI 10.1007/s00285-018-1241-y
Pubmed ID
Authors

Alexis Erich S. Almocera, Van Kinh Nguyen, Esteban A. Hernandez-Vargas

Abstract

Multiscale models possess the potential to uncover new insights into infectious diseases. Here, a rigorous stability analysis of a multiscale model within-host and between-host is presented. The within-host model describes viral replication and the respective immune response while disease transmission is represented by a susceptible-infected model. The bridging of scales from within- to between-host considered transmission as a function of the viral load. Consequently, stability and bifurcation analyses were developed coupling the two basic reproduction numbers [Formula: see text] and [Formula: see text] for the within- and the between-host subsystems, respectively. Local stability results for each subsystem, including a unique stable equilibrium point, recapitulate classical approaches to infection and epidemic control. Using a Lyapunov function, global stability of the between-host system was obtained. Our main result was the derivation of the [Formula: see text] as an increasing function of [Formula: see text]. Numerical analyses reveal that a Michaelis-Menten form based on the virus is more likely to recapitulate the behavior between the scales than a form directly proportional to the virus. Our work contributes basic understandings of the two models and casts light on the potential effects of the coupling function on linking the two scales.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 21%
Student > Ph. D. Student 6 14%
Professor 4 10%
Student > Postgraduate 3 7%
Student > Master 3 7%
Other 7 17%
Unknown 10 24%
Readers by discipline Count As %
Mathematics 6 14%
Agricultural and Biological Sciences 3 7%
Immunology and Microbiology 2 5%
Computer Science 2 5%
Physics and Astronomy 2 5%
Other 11 26%
Unknown 16 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 April 2019.
All research outputs
#13,881,022
of 23,972,269 outputs
Outputs from Journal of Mathematical Biology
#264
of 686 outputs
Outputs of similar age
#165,765
of 331,409 outputs
Outputs of similar age from Journal of Mathematical Biology
#6
of 12 outputs
Altmetric has tracked 23,972,269 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 686 research outputs from this source. They receive a mean Attention Score of 3.7. This one has gotten more attention than average, scoring higher than 61% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,409 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.