Nipah virus epidemic in southern India and emphasizing “One Health” approach to ensure global health security
Bibliographic record
Abstract
Nipah virus (NiV) encephalitis first reported in "Sungai Nipah" in Malaysia in 1999 has emerged as a global public health threat in the Southeast Asia region. From 1998 to 2018, more than 630 cases of NiV human infections were reported. NiV is transmitted by zoonotic (from bats to humans, or from bats to pigs, and then to humans) as well as human-to-human routes. Deforestation and urbanization of some areas have contributed to greater overlap between human and bat habitats resulting in NiV outbreaks. Common symptoms of NiV infection in humans are similar to that of influenza such as fever and muscle pain and in some cases, the inflammation of the brain occurs leading to encephalitis. The recent epidemic in May 2018 in Kerala for the first time has killed over 17 people in 7 days with high case fatality and highlighted the importance of One Health approach. The diagnosis is often not suspected at the time of presentation and creates challenges in outbreak detection, timely control measures, and outbreak response activities. Currently, there are no drugs or vaccines specific for NiV infection although this is a priority disease on the World Health Organization's agenda. Antivirals (Ribavirin, HR2-based fusion inhibitor), biologicals (convalescent plasma, monoclonal antibodies), immunomodulators, and intensive supportive care are the mainstay to treat severe respiratory and neurologic complications. There is a great need for strengthening animal health surveillance system, using a One Health approach, to detect new cases and provide early warning for veterinary and human public health authorities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".