A Systematic Review of Zoonotic Pathogens and the Risk of Future Pandemics: The Focus Areas, Potential Threats, and Global Readiness
Bibliographic record
Abstract
Zoological diseases are a real potential danger to populations and are especially dangerous due to the possibility of future epidemics due to cross-species transmission. As a systematic review, this article offers an understanding of zoonotic pathogens with high pandemic potential, vulnerable areas affected by zoonotic spillovers, and worldwide preparedness for future zoonoses. A systematic electronic search in PubMed, Scopus, Web of Science, and Cochrane Library databases yielded 410 articles published from 2000 to 2023; 60 articles were selected for further analysis. Zoonotic diseases with their pathogens that are connected with the disease state and animals include Nipah virus, Leptospira, and coronaviruses. There are four primary transmissions: possible contact with wild animals, live wildlife markets, and contaminated water. Research shows that countries in Southeast Asia, sub-Saharan Africa, and Latin America are most at risk of epidemic spillovers. Although some progress has been made and the global health community is better prepared to cope with pandemics and epidemics, weaknesses remain: for example, surveillance and requisite healthcare systems in low- and middle-income countries. That is why this review underlines the need for global cooperation, improved diagnostics of zoonotic diseases, and more effective application of prevention measures to decrease the probabilities of future pandemic risks.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".