AN EXTENSIVE STUDY ON MONKEYPOX: AN EMERGING GLOBAL CRISIS
Bibliographic record
Abstract
Introduction: Several viral diseases with epidemic potential are currently threatening global health security. One of them is Monkeypox. It is an emerging zoonotic infection which is caused by the monkeypox virus. In the past, this disease used to be primarily detected in West and Central Africa. However, the current upsurge of cases outside the African region has led the world to explore the causes that have led to the current situation. Aim and objectives of this study was to explore the reasons for the current upsurge and describe the epidemiology of the disease. The keywords below were used to search from public databases and review the relevantMaterial and Methods: publications on Monkeypox disease and its epidemiology. This search was further used to analyse and summarise the ndings into tables and other graphical representation of the data. Monkeypox cases are currently being reported from non-endemic countriesResult and Conclusion: e.g. USA, UK, Belgium, France, Germany, Italy, Netherlands, Portugal, Spain, Sweden, Australia, Canada, Austria, Canary Islands, Israel and Switzerland. There are no reported cases of monkeypox virus in India till date as on 6th June 2022. Cases have mainly but not exclusively been identied amongst men who have sex with men (MSM) seeking care in primary care and sexual health clinics in the range of 22 to 63 years of age. Follow public health measures to prevent another pandemic in the recent future.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".