Neglected Tropical Diseases (NTDs) and COVID-19 Pandemic in Africa:Special Focus on Control Strategies
Bibliographic record
Abstract
Neglected Tropical Diseases (NTDs) are a group of twenty (20) chronic, communicable, infectious diseases endemic to the tropics and sub-tropics climate countries, which are intimately associated with poverty, poor sanitation, limited clean water, and healthcare delivery; and dwellers live in proximity to pathogens and diseases vectors. The pathogens are protozoans, bacteria, helminths, fungi, and viruses. NTDs currently affect about one billion people globally, out of which 500 million are Africans living in rural settlements with low political voice and support. In recent years, NTDs have received little research recognition, development, and funding because more research efforts by global health stakeholders are focused on recognized diseases like cancers, hepatitis, tuberculosis, Acquired Immune-Deficiency Syndrome (AIDS), and malaria that affects most developed countries. The emergence of the viral novel COVID-19 will exacerbate the burden of NTDs on disadvantaged communities as global health efforts are again focused on COVID-19 clearance in terms of research and development to find a drug/vaccine amidst other investigations on recognized infections. This development can result in high death tolls due to NTDs if control measures are not prioritized now. This perspective addresses the need for NTDs control amidst COVID-19 clearance efforts to mitigate another viral health crisis in Africa.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".