Infodemic, social contagion and the public health response to COVID-19: insights and lessons from Nigeria
Bibliographic record
Abstract
Background The expansion of the internet and social media platforms have spurred an online infodemic, which has surged towards alarming proportions across the globe. The online infodemic trend has been particularly felt in Nigeria in the area of health information and communication throughout the recurring public health emergencies of the current decade. The outbreak of the ongoing COVID-19 (SARS-CoV-2) pandemic in March 2020 reaffirms the biggest threat of infodemic across online platforms against containment efforts and responses in Nigeria.Methods This study reflects on infodemic trends related to COVID-19 in light of previous zoonotic viral diseases in Nigeria (e.g. Ebola, Lassa, and Monkeypox). Relevant published research and gray literature on zoonotic diseases and communication responses are reviewed.Results Drawing lessons and insights from previous zoonotic viral diseases in Nigeria, we show the extent to which online infodemic hampers public health responses to the COVID-19 pandemic. The theory of social contagion, which describes the fear and panic that emerge during disease outbreaks, is deployed to deepen understanding of how online infodemic pose threats during health emergencies.Conclusion We argue that Nigeria and other countries affected by disease outbreaks would thrive better by proactive inclusion and management of online communication channels in addition to coordinated clinical (prophylactic or therapeutic) models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".