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Influenza Virus Infection Model With Density Dependence Supports Biphasic Viral Decay

Overview of attention for article published in Frontiers in Microbiology, July 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

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Title
Influenza Virus Infection Model With Density Dependence Supports Biphasic Viral Decay
Published in
Frontiers in Microbiology, July 2018
DOI 10.3389/fmicb.2018.01554
Pubmed ID
Authors

Amanda P. Smith, David J. Moquin, Veronika Bernhauerova, Amber M. Smith

Abstract

Mathematical models that describe infection kinetics help elucidate the time scales, effectiveness, and mechanisms underlying viral growth and infection resolution. For influenza A virus (IAV) infections, the standard viral kinetic model has been used to investigate the effect of different IAV proteins, immune mechanisms, antiviral actions, and bacterial coinfection, among others. We sought to further define the kinetics of IAV infections by infecting mice with influenza A/PR8 and measuring viral loads with high frequency and precision over the course of infection. The data highlighted dynamics that were not previously noted, including viral titers that remain elevated for several days during mid-infection and a sharp 4-5 log10 decline in virus within 1 day as the infection resolves. The standard viral kinetic model, which has been widely used within the field, could not capture these dynamics. Thus, we developed a new model that could simultaneously quantify the different phases of viral growth and decay with high accuracy. The model suggests that the slow and fast phases of virus decay are due to the infected cell clearance rate changing as the density of infected cells changes. To characterize this model, we fit the model to the viral load data, examined the parameter behavior, and connected the results and parameters to linear regression estimates. The resulting parameters and model dynamics revealed that the rate of viral clearance during resolution occurs 25 times faster than the clearance during mid-infection and that small decreases to this rate can significantly prolong the infection. This likely reflects the high efficiency of the adaptive immune response. The new model provides a well-characterized representation of IAV infection dynamics, is useful for analyzing and interpreting viral load dynamics in the absence of immunological data, and gives further insight into the regulation of viral control.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 19%
Student > Ph. D. Student 7 16%
Student > Bachelor 5 12%
Student > Master 4 9%
Student > Doctoral Student 3 7%
Other 6 14%
Unknown 10 23%
Readers by discipline Count As %
Mathematics 11 26%
Agricultural and Biological Sciences 4 9%
Biochemistry, Genetics and Molecular Biology 3 7%
Computer Science 2 5%
Physics and Astronomy 2 5%
Other 8 19%
Unknown 13 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 05 November 2019.
All research outputs
#5,666,240
of 23,094,276 outputs
Outputs from Frontiers in Microbiology
#5,263
of 25,264 outputs
Outputs of similar age
#95,930
of 326,353 outputs
Outputs of similar age from Frontiers in Microbiology
#218
of 746 outputs
Altmetric has tracked 23,094,276 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 25,264 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has done well, scoring higher than 79% 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 326,353 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 746 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 70% of its contemporaries.