Evidence map›Paper›PMID 34038385›Full record

ArticleGlobal health, science and practice2021

Real-Time Tracking of COVID-19 Rumors Using Community-Based Methods in Côte d'Ivoire.

Natalie Tibbels, Abdul Dosso, Aliya Allen-Valley, William Benie, Corinne Fordham, Jeanne Aka Brou, Marjorie Nana, Valère Zounneme, Korodénin Fatoumata Silué, Diarra Kamara and 1 more

Abstract read
In one paragraph

Article in Global health, science and practice, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 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.

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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  5. Community participation in responding to health misinformation.International journal for equity in health · 2026
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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

11 authors.

Natalie TibbelsJohns Hopkins Bloomberg School of Public Health, Center for Communication Programs, Baltimore, Maryland, USA. ntibbel1@jh.edu.
Abdul DossoJohns Hopkins Center for Communication Programs-Côte d'Ivoire, Abidjan, Côte d'Ivoire.
Aliya Allen-ValleyJohns Hopkins Bloomberg School of Public Health, Center for Communication Programs, Baltimore, Maryland, USA.
William BenieJohns Hopkins Center for Communication Programs-Côte d'Ivoire, Abidjan, Côte d'Ivoire.
Corinne FordhamJohns Hopkins Bloomberg School of Public Health, Center for Communication Programs, Baltimore, Maryland, USA.
Jeanne Aka BrouJohns Hopkins Center for Communication Programs-Côte d'Ivoire, Abidjan, Côte d'Ivoire.
Marjorie NanaJohns Hopkins Bloomberg School of Public Health, Center for Communication Programs, Baltimore, Maryland, USA.
Valère ZounnemeJohns Hopkins Center for Communication Programs-Côte d'Ivoire, Abidjan, Côte d'Ivoire.
Korodénin Fatoumata SiluéJohns Hopkins Center for Communication Programs-Côte d'Ivoire, Abidjan, Côte d'Ivoire.
Diarra KamaraJohns Hopkins Center for Communication Programs-Côte d'Ivoire, Abidjan, Côte d'Ivoire.
Danielle NaugleJohns Hopkins Bloomberg School of Public Health, Center for Communication Programs, Baltimore, Maryland, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Global misinformation and information overload have characterized the coronavirus disease (COVID-19) pandemic. Rumors are unverified pieces of information spreading online or person-to-person that reduce trust in health authorities and create barriers to protective practices. Risk communication and community engagement can increase transparency, build trust, and stop the spread of rumors. Building on previous work on Ebola and Zika viruses using Global Health Security Agenda systems strengthening support, the U.S. Agency for International Development-funded Breakthrough ACTION project developed a process and technology for systematically collecting, analyzing, and addressing COVID-19 rumors in real-time in Côte d'Ivoire. Rumors were submitted through community-based contributors and collected from callers to the national hotlines and then processed on a cloud-hosted database built on the open-source software District Health Information System 2 (DHIS2). Hotline teleoperators and data managers coded rumors in near-real-time according to behavioral theory frameworks within DHIS2 and visualized the findings on custom dashboards. The analysis and response were done in full collaboration with the Government of Côte d'Ivoire and implementing partners to ensure a timely and coordinated response. The system captured both widespread rumors consistent with misinformation in other settings, such as suspicions about case counts and the belief that masks were deliberately contaminated, as well as very localized beliefs related to specific influencers. The qualitative findings provided rapid insights on circulating beliefs, enabling risk communicators to nuance and tailor messaging around COVID-19.

Indexed as

CommunicationCOVID-19Health Knowledge, Attitudes, PracticePandemicsResidence CharacteristicsTrustCommunicable Disease ControlCote d'IvoireDatabases, FactualData CollectionGovernmentHotlinesHumansInformation ManagementInternational CooperationInternet

Identifiers

PMID34038385
PMCPMC8324191

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.