Poverty and the emergence of tuberculosis: An agent-based modelling approach
Bibliographic record
Abstract
Tuberculosis has been observed to thrive in conditions of poverty and there has been a long history of documented linkage between tuberculosis and poverty at the society and community level. This paper utilizes an agent-based model to incorporate three aspects of the conditions faced by the economically poorer and vulnerable individuals. The three aspects considered in the paper include undernutrition, poor living conditions (overcrowding, inadequate ventilation) and access to adequate health care facilities. The paper aims to understand the effects of poor living conditions on the emergence of latent tuberculosis infection among individuals exposed to the disease. Also, we consider the effect of undernutrition on the immune system response to control the progression from the latent tuberculosis infection to the active tuberculosis disease. Finally, the paper studies the effect on adequate access to proper health care as a factor on the emergence of tuberculosis. The results obtained indicate that malnutrition coupled with limited access to adequate health care increases the risk of emergence of the active tuberculosis disease and also that inadequate ventilation increases the risk of emergence of the latent tuberculosis infection (LTBI).
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".