Combatting anthrax outbreaks across Nigeria’s national land borders: need to optimize surveillance with epidemiological surveys
Bibliographic record
Abstract
BACKGROUND: Anthrax is a non-contagious zoonotic disease caused by the Gram-positive, spore-forming bacterium Bacillus anthracis. Infection is common in livestock and wild animals such as cattle, goats, sheep, camels, and antelopes. In humans, anthrax may occur after contact with contaminated carcasses or animal products like milk and meat. The best method to prevent anthrax in people is to ensure livestock are vaccinated, which significantly limits the risk of zoonotic spread to humans. However, the rate of vaccination of domesticated animals kept by nomadic pastoralists in West Africa is low. These groups regularly cross over national boundaries with their grazing herds. Nigeria is a country that historically has done comparatively well to contain this public health threat. However, in 2023 several outbreaks of human disease appear linked to the consumption of anthrax-contaminated animal products brought into Nigeria by pastoralists from neighboring countries. Clinical manifestations include skin sores or ulcers, nausea, vomiting, and fever. This article aims to raise awareness of recent outbreaks of anthrax in West Africa and to call for a renewed focus on measures to combat this neglected public health concern to the region. MAIN BODY: The imperative to pinpoint pivotal issues relating to the ongoing emergence of anthrax cases in Nigeria cannot be overstated. By delving into the prevalence of anthrax in both livestock and human populations residing along Nigeria's borders, unraveling the genetic diversity and potential sources of B. anthracis strains, and identifying the primary animal host(s) responsible for transmission, we stand to enhance our understanding of this critical issue. Furthermore, investigating the multifaceted factors contributing to anthrax transmission, assessing community knowledge and practices, mapping common migratory routes of pastoralists, and formulating targeted intervention strategies tailored to the challenges of border communities, are each crucial steps towards effective control and prevention. CONCLUSION: Closing these knowledge gaps on anthrax is not only essential for safeguarding both animal and human health but also for fostering sustainable and resilient communities. Addressing research questions on these interdisciplinary concerns will undoubtedly pave the way for informed decision-making, proactive measures, and a more secure future for Nigeria and its border regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".