Climate Change and Emerging Infectious Diseases: A Global Review of Shifting Patterns, Pathogens, and Public Health Risk
Bibliographic record
Abstract
Climate change is changing how emerging infectious diseases (EIDs) spread over the world. The ecological conditions in which diseases, vectors, and hosts interact are changing because of rising temperatures, shifting patterns of rainfall, and increasingly frequent extreme weather events. This narrative review compiles existing research on climate-sensitive infectious illnesses and elucidates the principal mechanisms influencing observed changes. Food- and water-borne illnesses (e.g., cholera, leptospirosis) are increasingly linked to droughts, floods, and disruptions in infrastructure. Vector-borne diseases like dengue, chikungunya, malaria, and Lyme disease are spreading to highland and temperate areas. At the same time, zoonotic spillovers like Ebola, Nipah, and SARS-CoV-2 are happening more commonly in areas where the ecosystem has been altered. New worries are thermotolerant fungal infections and microorganisms that live in permafrost. This review does not offer new epidemiological modelling; instead, it puts recent Global Burden of Disease (GBD) estimates into context. These estimates reveal that the burden of infectious diseases is rising in regions that are sensitive to climate change, including sub-Saharan Africa, South Asia, and Latin America. Weak surveillance systems, health disparities caused by climate change, and broken data streams are some of the biggest gaps in response. Improvements in AI-based forecasting, satellite surveillance, pathogen genomes, and One Health methods provide useful tools for taking action before something happens. It is important to build public health systems that are climate-responsive, transdisciplinary, and fair in order to reduce the growing dangers posed by infectious illnesses connected to climate change.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".