COVID-19 pandemic in Yemen: A questionnaire based survey, what do we know?
Bibliographic record
Abstract
INTRODUCTION: Coronavirus infectious disease 2019 (COVID-19) is currently one of the most important public health crises affecting the global human population. It continues to spread widely, as the world still lacks specific treatments and a vaccine for the virus. The scenario of COVID-19 in Yemen seems obscure due to the lack of adequate data, therefore, we developed an electronic questionnaire and distributed it online among Yemeni people. The aim of this study was to understand the COVID-19 epidemiological situation in Yemen better since there is currently limited published data and limited availability of COVID-19 testing. METHODOLOGY: A 34-question web-based survey was distributed on social media outlets targeting people in Yemen. Data aggregation, analysis, and visualization were performed using Tableau and Microsoft Excel. RESULTS: 2,341 individuals reported symptoms concerning for COVID-19 infection, with 25.4% reporting a chronic medical condition. Diabetes, hypertension, asthma, and immune deficiency were associated with increased severity of the disease, while obesity, cardiovascular disease, kidney disease, and liver disease were not. Only 37 individuals (1.6%) had a confirmatory COVID-19 PCR test. The presence of high fever, dyspnea, chest pain, and dysphagia were symptoms that tended to be correlated to worse clinical outcomes. CONCLUSIONS: This study provides some important information about the early overspread of COVID-19 within the Yemeni community in May, June, and July of 2020. It shows that online questionnaires may help in collecting data about pandemics in resource-limited countries where testing availability is limited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".