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An agent-based model simulation of influenza interactions at the host level: insight into the influenza-related burden of pneumococcal infections

Overview of attention for article published in BMC Infectious Diseases, June 2017
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Title
An agent-based model simulation of influenza interactions at the host level: insight into the influenza-related burden of pneumococcal infections
Published in
BMC Infectious Diseases, June 2017
DOI 10.1186/s12879-017-2464-z
Pubmed ID
Authors

Hélène Arduin, Matthieu Domenech de Cellès, Didier Guillemot, Laurence Watier, Lulla Opatowski

Abstract

Host-level influenza virus-respiratory pathogen interactions are often reported. Although the exact biological mechanisms involved remain unelucidated, secondary bacterial infections are known to account for a large part of the influenza-associated burden, during seasonal and pandemic outbreaks. Those interactions probably impact the microorganisms' transmission dynamics and the influenza public health toll. Mathematical models have been widely used to examine influenza epidemics and the public health impact of control measures. However, most influenza models overlooked interaction phenomena and ignored other co-circulating pathogens. Herein, we describe a novel agent-based model (ABM) of influenza transmission during interaction with another respiratory pathogen. The interacting microorganism can persist in the population year round (endemic type, e.g. respiratory bacteria) or cause short-term annual outbreaks (epidemic type, e.g. winter respiratory viruses). The agent-based framework enables precise formalization of the pathogens' natural histories and complex within-host phenomena. As a case study, this ABM is applied to the well-known influenza virus-pneumococcus interaction, for which several biological mechanisms have been proposed. Different mechanistic hypotheses of interaction are simulated and the resulting virus-induced pneumococcal infection (PI) burden is assessed. This ABM generates realistic data for both pathogens in terms of weekly incidences of PI cases, carriage rates, epidemic size and epidemic timing. Notably, distinct interaction hypotheses resulted in different transmission patterns and led to wide variations of the associated PI burden. Interaction strength was also of paramount importance: when influenza increased pneumococcus acquisition, 4-27% of the PI burden during the influenza season was attributable to influenza depending on the interaction strength. This open-source ABM provides new opportunities to investigate influenza interactions from a theoretical point of view and could easily be extended to other pathogens. It provides a unique framework to generate in silico data for different scenarios and thereby test mechanistic hypotheses.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 1%
Unknown 67 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 19%
Professor > Associate Professor 8 12%
Student > Master 8 12%
Student > Bachelor 7 10%
Researcher 6 9%
Other 10 15%
Unknown 16 24%
Readers by discipline Count As %
Medicine and Dentistry 8 12%
Mathematics 7 10%
Computer Science 7 10%
Immunology and Microbiology 4 6%
Agricultural and Biological Sciences 4 6%
Other 17 25%
Unknown 21 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 07 June 2017.
All research outputs
#15,412,384
of 22,977,819 outputs
Outputs from BMC Infectious Diseases
#4,500
of 7,715 outputs
Outputs of similar age
#198,645
of 317,446 outputs
Outputs of similar age from BMC Infectious Diseases
#102
of 178 outputs
Altmetric has tracked 22,977,819 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,715 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.7. This one is in the 41st percentile – i.e., 41% of its peers scored the same or lower than it.
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 317,446 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 178 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.