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Choice of Antiviral Allocation Scheme for Pandemic Influenza Depends on Strain Transmissibility, Delivery Delay and Stockpile Size

Overview of attention for article published in Bulletin of Mathematical Biology, February 2016
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Title
Choice of Antiviral Allocation Scheme for Pandemic Influenza Depends on Strain Transmissibility, Delivery Delay and Stockpile Size
Published in
Bulletin of Mathematical Biology, February 2016
DOI 10.1007/s11538-016-0144-6
Pubmed ID
Authors

Michael Lydeamore, Nigel Bean, Andrew J. Black, Joshua V. Ross

Abstract

Recently, pandemic response has involved the use of antivirals. These antivirals are often allocated to households dynamically throughout the pandemic with the aim being to retard the spread of infection. A drawback of this is that there is a delay until infection is confirmed and antivirals are delivered. Here an alternative allocation scheme is considered, where antivirals are instead preallocated to households at the start of a pandemic, thus reducing this delay. To compare these two schemes, a deterministic approximation to a novel stochastic household model is derived, which allows efficient computation of key quantities such as the expected epidemic final size, expected early growth rate, expected peak size and expected peak time of the epidemic. It is found that the theoretical best choice of allocation scheme depends on strain transmissibility, the delay in delivering antivirals under a dynamic allocation scheme and the stockpile size. A broad summary is that for realistic stockpile sizes, a dynamic allocation scheme is superior with the important exception of the epidemic final size under a severe pandemic scenario. Our results, viewed in conjunction with the practical considerations of implementing a preallocation scheme, support a focus on attempting to reduce the delay in delivering antivirals under a dynamic allocation scheme during a future pandemic.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 44%
Researcher 3 33%
Student > Bachelor 1 11%
Professor > Associate Professor 1 11%
Readers by discipline Count As %
Medicine and Dentistry 5 56%
Mathematics 1 11%
Biochemistry, Genetics and Molecular Biology 1 11%
Nursing and Health Professions 1 11%
Engineering 1 11%
Other 0 0%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 09 February 2016.
All research outputs
#20,305,223
of 22,844,985 outputs
Outputs from Bulletin of Mathematical Biology
#994
of 1,095 outputs
Outputs of similar age
#333,927
of 397,089 outputs
Outputs of similar age from Bulletin of Mathematical Biology
#10
of 13 outputs
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So far Altmetric has tracked 1,095 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.