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Mendeley readers
Attention Score in Context
| Title |
Policy Trap and Optimal Subsidization Policy under Limited Supply of Vaccines
|
|---|---|
| Published in |
PLOS ONE, July 2013
|
| DOI | 10.1371/journal.pone.0067249 |
| Pubmed ID | |
| Authors |
Ming Yi, Achla Marathe |
| Abstract |
We adopt a susceptible-infected-susceptible (SIS) model on a Barabási and Albert (BA) network to investigate the effects of different vaccine subsidization policies. The goal is to control the prevalence of the disease given a limited supply and voluntary uptake of vaccines. The results show a uniform subsidization policy is always harmful and increases the prevalence of the disease, because the lower degree individuals' demand for vaccine crowds out the higher degree individuals' demand. In the absence of an effective uniform policy, we explore a targeted subsidization policy which relies on a proxy variable instead of individuals' connectivity. Findings show a poor proxy-based targeted program can still increase the disease prevalence and become a policy trap. The results are robust to general scale-free networks. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
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 % |
|---|---|---|
| United Kingdom | 1 | 11% |
| Canada | 1 | 11% |
| Unknown | 7 | 78% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 44% |
| Student > Master | 2 | 22% |
| Lecturer > Senior Lecturer | 1 | 11% |
| Researcher | 1 | 11% |
| Unknown | 1 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Social Sciences | 3 | 33% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 11% |
| Physics and Astronomy | 1 | 11% |
| Mathematics | 1 | 11% |
| Medicine and Dentistry | 1 | 11% |
| Other | 1 | 11% |
| Unknown | 1 | 11% |
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 02 July 2013.
All research outputs
#15,274,055
of 22,713,403 outputs
Outputs from PLOS ONE
#130,179
of 193,923 outputs
Outputs of similar age
#120,423
of 194,634 outputs
Outputs of similar age from PLOS ONE
#3,074
of 4,799 outputs
Altmetric has tracked 22,713,403 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 193,923 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one is in the 24th percentile – i.e., 24% 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 194,634 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4,799 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.