↓ Skip to main content

Autonomous Targeting of Infectious Superspreaders Using Engineered Transmissible Therapies

Overview of attention for article published in PLoS Computational Biology, March 2011
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

news
1 news outlet
blogs
3 blogs
twitter
2 X users
patent
1 patent
wikipedia
3 Wikipedia pages

Citations

dimensions_citation
46 Dimensions

Readers on

mendeley
103 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Autonomous Targeting of Infectious Superspreaders Using Engineered Transmissible Therapies
Published in
PLoS Computational Biology, March 2011
DOI 10.1371/journal.pcbi.1002015
Pubmed ID
Authors

Vincent T. Metzger, James O. Lloyd-Smith, Leor S. Weinberger

Abstract

Infectious disease treatments, both pharmaceutical and vaccine, face three universal challenges: the difficulty of targeting treatments to high-risk 'superspreader' populations who drive the great majority of disease spread, behavioral barriers in the host population (such as poor compliance and risk disinhibition), and the evolution of pathogen resistance. Here, we describe a proposed intervention that would overcome these challenges by capitalizing upon Therapeutic Interfering Particles (TIPs) that are engineered to replicate conditionally in the presence of the pathogen and spread between individuals--analogous to 'transmissible immunization' that occurs with live-attenuated vaccines (but without the potential for reversion to virulence). Building on analyses of HIV field data from sub-Saharan Africa, we construct a multi-scale model, beginning at the single-cell level, to predict the effect of TIPs on individual patient viral loads and ultimately population-level disease prevalence. Our results show that a TIP, engineered with properties based on a recent HIV gene-therapy trial, could stably lower HIV/AIDS prevalence by ∼30-fold within 50 years and could complement current therapies. In contrast, optimistic antiretroviral therapy or vaccination campaigns alone could only lower HIV/AIDS prevalence by <2-fold over 50 years. The TIP's efficacy arises from its exploitation of the same risk factors as the pathogen, allowing it to autonomously penetrate superspreader populations, maintain efficacy despite behavioral disinhibition, and limit viral resistance. While demonstrated here for HIV, the TIP concept could apply broadly to many viral infectious diseases and would represent a new paradigm for disease control, away from pathogen eradication but toward robust disease suppression.

X Demographics

X Demographics

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 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 8%
Netherlands 1 <1%
Belgium 1 <1%
Unknown 93 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 33 32%
Student > Ph. D. Student 24 23%
Student > Master 10 10%
Student > Bachelor 5 5%
Student > Doctoral Student 4 4%
Other 15 15%
Unknown 12 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 29%
Biochemistry, Genetics and Molecular Biology 11 11%
Medicine and Dentistry 11 11%
Immunology and Microbiology 7 7%
Nursing and Health Professions 4 4%
Other 23 22%
Unknown 17 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 39. 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 17 April 2022.
All research outputs
#1,074,675
of 25,809,966 outputs
Outputs from PLoS Computational Biology
#853
of 9,025 outputs
Outputs of similar age
#4,141
of 130,962 outputs
Outputs of similar age from PLoS Computational Biology
#4
of 59 outputs
Altmetric has tracked 25,809,966 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,025 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done particularly well, scoring higher than 90% 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 130,962 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.