ReviewImmunology and cell biology2026
Effective communication and public engagement strategies to counter misinformation about infectious diseases.
Review in Immunology and cell biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- What Provider Frequently Asked Questions Miss: Evaluating Unmet Attention-Deficit/Hyperactivity Disorder Information Needs Through Comparison of Online Community Posts Using Large Language Model-Assisted Semantic Analysis in a Mixed Methods Study.Journal of medical Internet research · 2026Article
- Imagining Genomics and Population Health in 2050: Anticipating Future Research, Policy, and Governance Needs.Public health genomics · 2026Article
- Impact of a large-scale interactive and immersive science pop-up shop about infection and hygiene on visitors and volunteers.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Effective communication and public engagement are essential components of infectious disease control, yet they remain underdeveloped in the field of immunology. This review explores how immunologists and scientists can contribute to countering misinformation and improving vaccine uptake through inclusive, culturally sensitive engagement. Drawing on historical and contemporary case studies, we examine how trust, cognitive biases, and community involvement shape public responses. We highlight the importance of co-produced messaging and the role of community champions in building trust, particularly among marginalized groups. Vaccine communication is analyzed through the lens of the five Cs: confidence, complacency, convenience, communication, and context. We discuss how demographic and structural barriers, historical mistrust, and politicization of health messaging contribute to declining vaccine uptake and propose tailored strategies to address these challenges. The final section focuses on data presentation as a core foundation of public communication, emphasizing that clarity, transparency, and ethical framing are critical to public understanding. We outline principles for designing trustworthy visuals, mitigating cognitive biases, and embedding context directly within graphics to prevent misinterpretation. Participatory approaches to data communication are shown to improve comprehension and trust, especially when co-developed with affected communities. Together, these domains-engagement, vaccine communication, and data presentation-form a foundation for resilient public health responses. By integrating immunological expertise with inclusive communication strategies, scientists can play a central role in fostering informed decision making and strengthening public cooperation in future outbreaks.
Indexed as
Identifiers
What OpenQuestion holds
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.