Insights on defeating coronavirus disease (COVID-19) outbreak and predicting tourist arrival on the Chinese Hainan Leisure Island during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Hainan province is a very popular leisure tourist arriving city in China. Coronavirus disease 2019 (COVID-19) emerged in China and rapidly in early 2020, and due to its rapid worldwide spread, the World Health Organization declared COVID-19 as a global emergency. During the COVID-19 pandemic in Hainan province, many businesses and economies were influenced in this unexpected event, especially in tourism. METHODS: This study used 2 classical forecasting methods to predict the number of tourists on Hainan Leisure Island from September to December in the second half of 2020 and to summarize the COVID-19 fighting experience during the pandemic. In addition, the Hainan government implemented epidemic control measures to resume production and work, and promote new tourism measures to acquire superior COVID-19 protection. RESULTS: Winter's method provides a statistical model for predicting the number of visitors to Hainan under normal conditions. The trend analysis method considers the impact of the black swan event, an irregular event, and only uses the data under the influence of the event to predict according to the trend. CONCLUSION: If the impact of the black swan event (COVID-19) continues, the prediction can be made using this method. In addition, the Hainan government has undertaken timely and effective measures against COVID-19 to promote leisure tourism development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".