Ongoing worldwide homogenization of human pathogens
Bibliographic record
Abstract
Abstract Background Infectious diseases are a major burden on human population, especially in low- and middle-income countries. The increase in the rate of emergence of infectious outbreaks necessitates a better understanding of the worldwide distribution of diseases through space and time. Methods We analyze 100 years of records of diseases occurrence worldwide. We use a graph-theoretical approach to characterize the worldwide structure of human infectious diseases, and its dynamics over the Twentieth Century. Findings Since the 1960s, there is a clear homogenizing of human pathogens worldwide, with most diseases expanding their geographical area. The occurrence network of human pathogens becomes markedly more connected, and less modular. Interpretation Human infectious diseases are steadily expanding their ranges since the 1960s, and disease occurrence has become more homogenized at a global scale. Our findings emphasize the need for international collaboration in designing policies for the prevention of outbreaks. Funding T.P. is funded by a FRQNT-PBEE post-doctoral fellowship, and through a Marsden grant from the Royal Academy of Sciences of New-Zealand. Funders had no input in any part of the study.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".