COVID-19 proactive screening protocol during early outbreak using web-based application: Implementation in Thai Rural Area
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) caused by an infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been recognized as one of the biggest problems to human health worldwide. The potential strategy to control the spreading of the virus is awareness of infection status, especially among non-patient under investigation (non-PUI). Additionally, implementing gathering control and suspending “social distancing” are key to a decrease in the chance of infection. This study aimed to conduct and implement the pilot management system for COVID-19 testing and to perform proactive screening test among non-PUI people in Phayao Province, the rural area of Thailand. This was a cross-sectional study. People who could access websites- or mobile applications were eligible to be recruited into the study. An online questionnaire was developed to collect information on socio-demographics, medical conditions and symptoms related to COVID-19 from participants who were living in Phayao Province from July to August 2020. 200 participants performed self-evaluation but only 143 (71.5%) participants booked an appointment and visited the collecting site to get the test. There were 25 (9%) participants being at high risk of infection. The nasopharyngeal/throat swabs were collected and proceeded to determine a presence of SARS-CoV-2 using RT-PCR. Of all, none was found to be positive for SARS-CoV-2. In conclusion, this developed management system would be an important tool for managing laboratory testing during the COVID-19 outbreak by means of reducing the chance of infection in the epidemic situation. This proactive screening system can also be applied for other medical testing and services.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".