Geographic Information Systems (GIS), an Informative Start for Challenging Process of Etiologic Investigation of Diseases and Public Health Policy Making
Bibliographic record
Abstract
Background: The public health has been always concerned of the immediate environment of human as causal factors for different diseases and health outcomes. Epidemiology, as one of the fundamental basis of public health, is concerned of how diseases are distributed in terms of geographical, chronological, and human population characteristics and employees the descriptive nature of such spread to draw conclusion on the etiology of health or disease outcome for further policy-making on prevention of disease or promotion of health. Methods: In this paper, we present the importance of GIS technology in epidemiology from both descriptive and etiologic standpoints and elaborate how this technology can stand in the forefront of disease and health outcome measures in the coming decades. The paper will address the history of geo-related health and disease issues. The mapping tool as a traditionally strong resource in the public health will be explored. The advances in Information Technology and one of its best-utilized offshoot, GIS, in Health and disease will be discussed. How the huge repository of generated or ever generating geo-related data and information is utilized to address etiology of diseases or to help public health authorities in making informed policy making decisions are explored. Results: The utilization of GIS technology in diseases with an intermittent host such as malaria, yellow fever, or other parasitic diseases has already been well established. The GIS technology and its utilization in chronic and degenerative diseases such as cancer, diabetes, and aging are under development and new frontiers are discovering. The limitation of GIS technology in addressing host environment interaction in micro-environment (at the molecular biology and tissue pathogenicity level) and gene–environment interaction (at the individual level) will further be discussed. Conclusion: We then distress on the efficient use of GIS both in the etiologic investigation of diseases and health events as well as the utilization of the GIS technology as a administrative tool in the help of public health authorities and policymakers in strategic management of health of a community or emergency management of man-made or technological disasters (e.g., wars) or naturally occurring disasters (e.g., earthquake and floods).
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".