Implications of climatic and demographic change for seasonal influenza dynamics and evolution
Bibliographic record
Abstract
Abstract Seasonal influenza causes a substantial public health burden, as well as being a key substrate for pandemic emergence. Future climatic and demographic changes may alter both the magnitude, frequency and timing of influenza epidemics and the prospects for pathogen evolution, however, these issues have not been addressed systematically. Here, we use a parsimonious influenza model, grounded in theoretical understanding of the link between climate, demography and transmission to project future changes globally. We find that climate change generally acts to reduce the intensity of influenza epidemics as specific humidity increases. However, this reduction in intensity is accompanied by increased seasonal epidemic persistence with latitude, which may increase suitability for year-round local influenza evolution. Using a range of population growth scenarios, we find that the number of global locations with high evolution suitability may double by 2050. High population growth in tropical Africa could thus make this region a locus of novel strain emergence, shifting the current focus from South East Asia.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".