A Development Strategy for the Revival of Tourist Hotspots following the COVID-19 Pandemic
Bibliographic record
Abstract
The ongoing Covid-19 crisis has hit many sectors and industries in the hardest possible way. Travel and tourism-related activities have not been an exception. We contend that a systemic approach can be developed and implemented in order to trace and certify individuals who do not present an epidemiological risk to other people, and also to manage their close interaction. This could lead to the certification of a large proportion of the population—millions worldwide—as not representing a risk of infection to others. It can justify the implementation of a system that can speed up the reactivation of several economic sectors and industries, protecting jobs and accelerating economic recovery in many countries. People who have been ill with Covid-19 have acquired the corresponding antibodies and, therefore, have immunity to the disease, they could travel freely, thereby helping to reactivate the economy. We will explain in this paper how a number of high-tech tools can be implemented as a crowd control system to identify those who do not represent a risk to others, either because they have acquired immunity or because they can be regarded as not carriers of a communicable disease. We devise a method based on the use of a 3D-diagram that shows the existence of an inverse relation between the number of tests performed and the number of individuals that have contracted the disease. The results of the study suggest that the implementation of a new epidemiological tourist strategy in Cuba can help to reactivate tourist activities in the country while avoiding the creation of new hotbeds of infection for Covid-19.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".