Evidence map›Paper›PMID 24290841›Full record

ReviewThe Lancet. Infectious diseases2014

Internet-based surveillance systems for monitoring emerging infectious diseases.

Gabriel J Milinovich, Gail M Williams, Archie C A Clements, Wenbiao Hu

Registry-linked trialAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The Lancet. Infectious diseases, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05442762 (A Social Media-based Machine Learning Study to Monitor Vaccine Confidence and Hesitancy and Early Warn Emerging Vaccine-related Risks in Real Time), which is not on this map. Cited by 143 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
143citing papers in PubMed, 11 pooled it
–field-weighted citation impact
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.

NCT05442762 withdrawnnot on this mapstarted 2022, after this paper: background citation

A Social Media-based Machine Learning Study to Monitor Vaccine Confidence and Hesitancy and Early Warn Emerging Vaccine-related Risks in Real Time

TypeobservationalSponsorFudan UniversityRan2022 to 2022Enrolled0ConditionsSocial Medium, Data Collection, Patient Acceptance of Health Care, Immunization Programs
3 · Its place in the literature

Who cites it

143 citing papers in PubMed, 11 syntheses or guidelines pooled it.

  1. Pooled it
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  6. Identifying potential emerging threats through epidemic intelligence activities-looking for the needle in the haystack?International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2019
    Pooled it
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  12. Review
  13. Review
  14. Article
  15. [Transformation of Epidemiology in the Age of Artificial Intelligence].Revista medica del Instituto Mexicano del Seguro Social · 2026
    Article
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  19. Observational
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83 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Gabriel J MilinovichInfectious Disease Epidemiology Unit, School of Population Health, The University of Queensland, Herston, QLD, Australia. Electronic address: g.milinovich@uq.edu.au.
Gail M WilliamsInfectious Disease Epidemiology Unit, School of Population Health, The University of Queensland, Herston, QLD, Australia.
Archie C A ClementsInfectious Disease Epidemiology Unit, School of Population Health, The University of Queensland, Herston, QLD, Australia.
Wenbiao HuInfectious Disease Epidemiology Unit, School of Population Health, The University of Queensland, Herston, QLD, Australia; School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, QLD, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Emerging infectious diseases present a complex challenge to public health officials and governments; these challenges have been compounded by rapidly shifting patterns of human behaviour and globalisation. The increase in emerging infectious diseases has led to calls for new technologies and approaches for detection, tracking, reporting, and response. Internet-based surveillance systems offer a novel and developing means of monitoring conditions of public health concern, including emerging infectious diseases. We review studies that have exploited internet use and search trends to monitor two such diseases: influenza and dengue. Internet-based surveillance systems have good congruence with traditional surveillance approaches. Additionally, internet-based approaches are logistically and economically appealing. However, they do not have the capacity to replace traditional surveillance systems; they should not be viewed as an alternative, but rather an extension. Future research should focus on using data generated through internet-based surveillance and response systems to bolster the capacity of traditional surveillance systems for emerging infectious diseases.

Indexed as

InternetCommunicable Diseases, EmergingDengueDeveloped CountriesDeveloping CountriesGlobal HealthHumansInfluenza, HumanModels, StatisticalPublic Health SurveillanceSearch EngineSocial Media

Identifiers

PMID24290841
PMCPMC7185571

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.