Zoonotic diseases in low and middle-income countries (LMICs): Economic burden, challenges, strategies, and future directions
Bibliographic record
Abstract
More than 200 zoonotic diseases are known to affect humans, representing a significant share of both emerging and existing infectious diseases worldwide. These diseases pose a major global public health challenge, particularly in low- and middle-income countries (LMICs), where limited surveillance systems, inadequate containment measures, and structural inequities allow rapid transmission. Key contributing factors include poverty, fragile healthcare infrastructure, political instability, fragmented policies, and low public awareness. Addressing these challenges demands integrated and context-sensitive strategies that strengthen community-based surveillance, promote the development of affordable diagnostic and preventive tools, and empower local institutions. Collaborative efforts among national and international partners, grounded in the One Health framework, are essential for achieving sustainable disease control and prevention. In addition, the strategic application of emerging technologies such as genomics, artificial intelligence, and precision medicine can improve diagnostic capacity, facilitate real-time data sharing, enable predictive modelling, and support evidence-based policy decisions. Together, these approaches can enhance equity, efficiency, and sustainability in the management of endemic, emerging, and novel zoonotic diseases, while strengthening preparedness for future zoonotic threats. • Zoonotic diseases constitute a major share of emerging infections, placing a significant burden on LMICs public health. • Weak surveillance and limited healthcare access impede zoonotic disease prevention in LMICs. • The One Health framework fosters multisectoral collaborationfor disease prevention and outbreak response. • Integrating genomicsartificial intelligence, and ecosystem methods enhances early prediction of zoonotic threats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".