Harnessing community-based one health interventions implementation beyond Mpox outbreak management in Africa: insights and benefits
Bibliographic record
Abstract
BACKGROUND: Little is documented on key community-based One Health (OH) approach implementation, pro-activeness and effectiveness of interactions and strategies against Mpox outbreak public health emergency in international concern (PHEIC) in various African countries in order to stamp out the persisting Mpox outbreak threat and burden. Prioritizing critical community-based interventions and lessons learned from previous COVID-19, Mpox, Ebola, COVID-19, Rift Valley Fever and Marburg virus outbreaks revealed critical shortcomings in funding, surveillance, and community engagement that plague public health initiatives across the continent. The article provides critical insights and benefits of community-based One Health approaches implementation against Mpox outbreak management in Africa. MAIN BODY: Our findings provides a comprehensive community and primary healthcare systems strategies essential to foster community engagement and resilience, while addressing the social determinants of health. Investing in targeted, effective and contextual community-based OH strategies implementation shows to improve immediate vulnerable communities integrated (human,animal and environment) preparedness and response and building sustainable resilience strategies against Mpox and future emergencies threats. The importance of global and regional multi-sectorial collaboration, solidarity and coordination cannot be over-emphasized, to mobilize resource, sharing knowledge and successes in enhancing local OH anticipatory and ownership programs capabilities for equitably shared benefits. Timely strengthening community empowerment and national health systems last miles, WASH and vaccination activities are essential to control, contain and sustainable recovery from the ongoing Mpox outbreak and future crises. Tackling survivors and at high risk affected populations stigma, fear and misinformation surrounding Mpox those hinder effective health communication and disease management, highlighting the need for culturally sensitive educational and empowerment strategies. A comprehensive assessment community-based one-health approaches implementation was performed to understand and prioritize key data-driven community-based OH strategies in infectious disease outbreaks beyond. Leveraging on outbreak valuable lessons learned and emerging technologies benefits in addressing the health social determinants, optimizing Mpox PHEIC implemented programs capabilities efforts, building communities resilience and sustainable solutions, and prioritizing strategies against outbreaks/pandemics threats and burden. CONCLUSIONS: Catalyzing evidence-based community-based OH governance, leadership and domestic financing commitment serve as a critical engine connecting all stakeholders in prioritizing and optimizing unprecedented outbreaks threats preparedness and response initiatives implementation and upholding global health security returns.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".