Evidence map›Paper›PMID 36498096›Full record

ArticleInternational journal of environmental research and public health2022

Early Detection and Control of the Next Epidemic Wave Using Health Communications: Development of an Artificial Intelligence-Based Tool and Its Validation on COVID-19 Data from the US.

Teddy Lazebnik, Svetlana Bunimovich-Mendrazitsky, Shai Ashkenazi, Eugene Levner, Arriel Benis

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
6.3field-weighted citation impact, top 4% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025
    Pooled it
  2. Article
  3. Article
  4. Article
  5. 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

5 authors at 3 institutions in 2 countries.

Teddy LazebnikDepartment of Cancer Biology, Cancer Institute, University College London, London WC1E 6DD, UK.ORCID 0000-0002-7851-8147
Svetlana Bunimovich-MendrazitskyDepartment of Mathematics, Faculty of Natural Sciences, Ariel University, Ariel 4070000, Israel.ORCID 0000-0001-5280-3217
Shai AshkenaziAdelson School of Medicine, Ariel University, Ariel 4077625, Israel.ORCID 0000-0001-7244-0679
Eugene LevnerDepartment of Applied Mathematics, Faculty of Sciences, Holon Institute of Technology, Holon 5810201, Israel.ORCID 0000-0003-0430-408X
Arriel BenisFaculty of Industrial Engineering and Technology Management, Holon Institute of Technology, Holon 5810201, Israel.ORCID 0000-0002-9125-8300
Ariel University · ILHolon Institute of Technology · ILUniversity College London · GB

Funding

Ariel University and Holon Institute of Technology, Israe 00000000000
6 · The paper itself

Abstract

Social media networks highly influence on a broad range of global social life, especially in the context of a pandemic. We developed a mathematical model with a computational tool, called EMIT (Epidemic and Media Impact Tool), to detect and control pandemic waves, using mainly topics of relevance on social media networks and pandemic spread. Using EMIT, we analyzed health-related communications on social media networks for early prediction, detection, and control of an outbreak. EMIT is an artificial intelligence-based tool supporting health communication and policy makers decisions. Thus, EMIT, based on historical data, social media trends and disease spread, offers an predictive estimation of the influence of public health interventions such as social media-based communication campaigns. We have validated the EMIT mathematical model on real world data combining COVID-19 pandemic data in the US and social media data from Twitter. EMIT demonstrated a high level of performance in predicting the next epidemiological wave (AUC = 0.909,

Indexed as

COVID-19Health CommunicationSocial MediaArtificial IntelligenceHumansPandemicsSARS-CoV-2computer simulationcoronavirusepidemicsepidemiologic methodshealth belief modelhealth communicationhealth policyinfluenzamachine learningonline social networkingpandemicsSars-Cov-2social factorssocial mediatime series

Identifiers

PMID36498096
PMCPMC9740968
OpenAlexW4310398127

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.