Bibliographic record
Abstract
Infectious diseases and their transmission are good examples of complex systems, with several interacting and interdependent components. We develop an agent-based simulation of the living condition of a typical slum setting in Nigeria which is considered to have a higher incidence and prevalence of tuberculosis. We consider the epidemiology of the disease and create a dynamic model, incorporating both environmental conditions and immune suppressed health conditions that have been observed in previous studies to be associated with emergence of the disease. Based on the developed model, we observe the pattern of transmission and we compare the results obtained with the estimates provided by the World Health Organization (WHO) and other individual surveys carried out in the past. The results obtained showed that increasing the air changes per hour (ACH) reduces the number of new latent tuberculosis infections among close contacts. Incorporating 7 air changes per hour which is the recommended value for a bedroom had a significance impact in reducing the number of new latent infections. Furthermore, introducing about 13 air changes per hour which is the recommended value for a smoking environment had a further reduction in the number of new latent infections. The result obtained also showed that individuals living with both HIV and diabetes have the highest risk of progressing to the active tuberculosis disease. Finally, the results also revealed that close contacts of people living with the active tuberculosis disease have a higher risk of developing the latent tuberculosis infection. These insights are useful findings to support public health policies for tuberculosis management.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".