Evidence map›Paper›PMID 31120877›Full record

ArticlePLoS computational biology2019

Epidemic prevalence information on social networks can mediate emergent collective outcomes in voluntary vaccine schemes.

Anupama Sharma, Shakti N Menon, V Sasidevan, Sitabhra Sinha

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
7.4field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 22 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Mass Testing and Proactiveness Affect Epidemic Spreading.Journal of the Indian Institute of Science · 2021
    Review
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Anupama SharmaThe Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, India.
Shakti N MenonThe Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, India.ORCID 0000-0001-5307-2153
V SasidevanThe Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, India.
Sitabhra SinhaThe Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, India.ORCID 0000-0002-3004-1079
Institute of Mathematical Sciences · INCochin University of Science and Technology · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The effectiveness of a mass vaccination program can engender its own undoing if individuals choose to not get vaccinated believing that they are already protected by herd immunity. This would appear to be the optimal decision for an individual, based on a strategic appraisal of her costs and benefits, even though she would be vulnerable during subsequent outbreaks if the majority of the population argues in this manner. We investigate how voluntary vaccination can nevertheless emerge in a social network of rational agents, who make informed decisions whether to be vaccinated, integrated with a model of epidemic dynamics. The information available to each agent includes the prevalence of the disease in their local network neighborhood and/or globally in the population, as well as the fraction of their neighbors that are protected against the disease. Crucially, the payoffs governing the decision of agents vary with disease prevalence, resulting in the vaccine uptake behavior changing in response to contagion spreading. The collective behavior of the agents responding to local prevalence can lead to a significant reduction in the final epidemic size, particularly for less contagious diseases having low basic reproduction number [Formula: see text]. Near the epidemic threshold ([Formula: see text]) the use of local prevalence information can result in divergent responses in the final vaccine coverage. Our results suggest that heterogeneity in the risk perception resulting from the spatio-temporal evolution of an epidemic differentially affects agents' payoffs, which is a critical determinant of the success of voluntary vaccination schemes.

Indexed as

Communicable Disease ControlCommunicable DiseasesComputer SimulationDecision MakingDisease OutbreaksEpidemicsHumansImmunity, HerdMass VaccinationModels, BiologicalPrevalenceRiskSocial NetworkingVaccinationVaccinesVaccines

Identifiers

PMID31120877
PMCPMC6532839
OpenAlexW2758720639

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.