Population Mobility and Infectious Diseases: The Diminishing Impact of Classical Infectious Diseases and New Approaches for the 21st Century
Bibliographic record
Abstract
In an increasingly globalized world, rapid population mobility and migration is reducing the differences in infectious disease epidemiology between regions of the world. The movement and relocation of populations between locations where the prevalence and incidence of infections are markedly different poses current and future challenges to those involved in clinical infectious diseases and public health program management. Historically, international attention has focused on the screening and treatment of acute infections of epidemic potential, but, as immigration significantly changes the demography of many nations, chronic infections will require increased attention. In countries with large mobile populations, the population-based burden of infections with long latency periods or significant noninfectious sequelae will make up an increasing amount of the infectious disease caseload and will require more-modern approaches than the traditional screening of arrivals. The globalization of chronic infectious disease epidemiology will require corresponding development of integrated programs to anticipate and manage these diseases in response to an increasingly mobile patient population.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".