A Comparative Study of the Trends of Imported Dengue Cases in Korea and Japan 2011 - 2015
Bibliographic record
Abstract
BACKGROUND: Dengue is a mosquito-borne febrile disease that represents a major public health problem in tropical and subtropical areas. Even though Korea and Japan are not the regions where dengue is epidemic, there have been many imported cases in both countries, and in increasing numbers. A better understanding of the characteristics of the prevalence of dengue and the recent trends in these neighboring countries may provide information to promote improvement and control strategies for both. The present study investigated the epidemiological status of imported dengue cases in Korea and Japan between 2011 and 2015, and compared their characteristics. METHODS: We analyzed the annual transition of prevalence, geographic origin of dengue infection, and seasonal distribution of occurrence. The raw data on dengue cases in Korea were obtained from the Korea Center for Disease Control and Prevention infectious diseases surveillance website and Korean Statistical Information Service website. Data on dengue cases in Japan were obtained from the National Institute of Infectious Diseases, Japan's Infectious Disease Surveillance Center website. RESULTS: There were 893 reported cases in Korea and 1,054 in Japan between 2011 and 2015. Cumulative incidence per 100,000 overseas travelers from Japan did not substantially differ from that for Korea (1.22 vs. 1.16, respectively), despite Japan's population being roughly 2.5 times larger. These results suggest Koreans engage in overseas travel more than Japanese. For Korea and Japan, Southeast Asia was the region accounting for the most cases of infection (89.4% vs. 75.4%, respectively). Notably, the Philippines and Indonesia were, respectively, the leading origin countries for Korean (38.1%) and Japanese (23.3%) cases. Seasonal distribution shows August and September were the months in which the largest number of cases occurred in Korea and Japan, respectively. These differences evidently derive from characteristics of travel destinations and timing of holidays. CONCLUSION: Based on the recent increasing trend in imported dengue cases in both countries, a more rigorous information system that can effectively provide warning of dengue risk and means of prevention for travelers headed to at-risk areas is urgently needed in both countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".