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Integrating Transgenic Vector Manipulation with Clinical Interventions to Manage Vector-Borne Diseases

Overview of attention for article published in PLoS Computational Biology, March 2016
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6 X users
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63 Mendeley
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Article details
Title
Integrating Transgenic Vector Manipulation with Clinical Interventions to Manage Vector-Borne Diseases
Published in
PLoS Computational Biology, March 2016
DOI 10.1371/journal.pcbi.1004695
Pubmed ID
Authors
Abstract

Many vector-borne diseases lack effective vaccines and medications, and the limitations of traditional vector control have inspired novel approaches based on using genetic engineering to manipulate vector populations and thereby reduce transmission. Yet both the short- and long-term epidemiological effects of these transgenic strategies are highly uncertain. If neither vaccines, medications, nor transgenic strategies can by themselves suffice for managing vector-borne diseases, integrating these approaches becomes key. Here we develop a framework to evaluate how clinical interventions (i.e., vaccination and medication) can be integrated with transgenic vector manipulation strategies to prevent disease invasion and reduce disease incidence. We show that the ability of clinical interventions to accelerate disease suppression can depend on the nature of the transgenic manipulation deployed (e.g., whether vector population reduction or replacement is attempted). We find that making a specific, individual strategy highly effective may not be necessary for attaining public-health objectives, provided suitable combinations can be adopted. However, we show how combining only partially effective antimicrobial drugs or vaccination with transgenic vector manipulations that merely temporarily lower vector competence can amplify disease resurgence following transient suppression. Thus, transgenic vector manipulation that cannot be sustained can have adverse consequences-consequences which ineffective clinical interventions can at best only mitigate, and at worst temporarily exacerbate. This result, which arises from differences between the time scale on which the interventions affect disease dynamics and the time scale of host population dynamics, highlights the importance of accounting for the potential delay in the effects of deploying public health strategies on long-term disease incidence. We find that for systems at the disease-endemic equilibrium, even modest perturbations induced by weak interventions can exhibit strong, albeit transient, epidemiological effects. This, together with our finding that under some conditions combining strategies could have transient adverse epidemiological effects suggests that a relatively long time horizon may be necessary to discern the efficacy of alternative intervention strategies.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United Kingdom 2 3%
Brazil 1 2%
Unknown 60 95%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 15 24%
Student > Master 7 11%
Student > Postgraduate 7 11%
Student > Ph. D. Student 5 8%
Other 3 5%
Other 10 16%
Unknown 16 25%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 13 21%
Biochemistry, Genetics and Molecular Biology 6 10%
Medicine and Dentistry 5 8%
Mathematics 4 6%
Business, Management and Accounting 3 5%
Other 14 22%
Unknown 18 29%
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 02 December 2017.
All research outputs
#12,457,771
of 34,622,316 outputs
Outputs from PLoS Computational Biology
#6,438
of 10,469 outputs
Outputs of similar age
#132,218
of 338,269 outputs
Outputs of similar age from PLoS Computational Biology
#115
of 174 outputs
Altmetric has tracked 34,622,316 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,469 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.7. This one is in the 34th percentile – i.e., 34% 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 338,269 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 174 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.