Chikungunya Fever: An Emerging Public Health Problem in Bangladesh
Bibliographic record
Abstract
Chikungunya virus (CHIKV) is an emerging epidemic-prone and mosquito-transmitted causative agent of chikungunya fever accompanied by severe joint and muscle pain, headache, fatigue and rashes. In recent years, some diseases are emerging such as dengue fever due to climate change and global warming in Bangladesh. [12] it has been observed that during the period of monsoon and post-monsoon there is an increase activity of the vectors with rainfall and their life span is influenced by temperature and humidity. Post monsoon period increases virus transmission due to high vector density and Aedes aegypti is the main the vector responsible for CHIV in Bangladesh. Dhaka, the capital city of Bangladesh, recently swayed with a severe outbreak of chikungunya and there are so far 3,000 cases have been reported in different hospitals and clinics of Dhaka from May to July 2017. It is normally advised that patients with chikungunya should be managed as dengue until dengue fever has been ruled out [13]. Chikungunya infections are confirmed by the detection of the virus, viral RNA or CHIKV specific antibodies in patient sample using serological process but viral RNA can easily be detected by reverse transcriptase-polymerase chain reaction. There is no vaccine for this infection as well as no specific antiviral treatment for Chikungunya fever as it is poorly responsive to analgesia in its acute and chronic phase of the disease. To control the spread of the infection government, non-government officials should come forward and take necessary steps to aware and educate people about the infection so that people can avoid contact with mosquitoes. National surveillance can be run along with active community participation is required to eradicate the mosquitoes the environment.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".