Age, Sex, Population Density and COVID-19 Pandemic in Thailand: A Nationwide Descriptive Correlational Study
Bibliographic record
Abstract
Objective: It is reported that age and sex have been identified as potential risk factors for severe outcomes and the distribution of coronavirus disease (COVID-19), although the specifics of these relationships are unclear. Furthermore, little is known about the relationship between age, sex, COVID-19, and population density in Thailand. This study proposed to examine the relationships among age, sex, population density, and the number of COVID-19 patients in Thailand.Material and Methods: In this nationwide descriptive correlational study, the dataset of daily COVID-19 cases in Thailand between January 12, 2020, and November 30, 2020, and population density (people/km2 ) in each province of Thailand was retrieved from the Open Government Data of Thailand, the Registration Office Department of the Interior, the Ministry of the Interior, and the National Statistical Office of Thailand. Chi-square and Pearson product-moment correlation were used to determine the difference and relationships among studied variables. Simple linear regression was used to predict the number of COVID-19 cases based on population density.Results: The findings illustrated a significant difference between male and female patients, in which the number of male patients was higher than female patients across age groups 31-45 years, 40-60 years, and >60 years (p-value<0.010). Further, population density was significantly associated with the number of COVID-19 cases.Conclusion: This investigation would provide intervention planning implications during potential future pandemics, especially in groups at higher risk (males, age 17-46 years old, and people living in high-density population areas).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.062 | 0.192 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".