Can Japan Achieve Zero Transmission of HIV? Time Series Analysis Using Bayesian Local Linear Trend Model.
Bibliographic record
Abstract
BACKGROUND: The number of newly diagnosed human immunodeficiency virus (HIV) infections and acquired immune deficiency syndrome (AIDS) patients in Japan appears to be decreasing. However, whether these new infections cease to occur in the future in Japan, similar to abroad, is unclear. To evaluate the feasibility of this achievement, we conducted a time series analysis using Bayesian local linear trend model to evaluate the possibility of zero new infection of HIV/AIDS in Japan. METHODS: We used quarterly data on HIV/AIDS from the first quarter, 2001 to the second quarter, 2020. Bayesian analyses were conducted using Markov chain Monte Carlo (MCMC) method, and a local linear trend model was constructed for number of newly diagnosed HIV infection without AIDS diagnosis, AIDS cases, and their aggregate. Predictions for the following 60 quarters until the second quarter of 2035 were also made for all models. RESULTS: The mean aggregate cases of HIV/AIDS patients became 0 by the fourth quarter of 2031 (90% credible interval 0-535). For HIV infections alone, mean cases became 0 by the second quarter of 2030 (90%CrI 0-472). For AIDS alone mean cases were 9 at the second quarter of 2035 (90%CrI 0-231). CONCLUSION: Our local linear trend model suggested that number of HIV/AIDS cases in Japan could decrease to zero by the first quarter of 2031, if the trend of the infections followed the local linear trend model, yet with rather wide credible interval. Achieving zero new transmission of HIV in Japan is a realistic goal but measures to make it faster may be needed.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".