A changing landscape: Tracking and analysis of the international HDV epidemiology 1999–2020
Bibliographic record
Abstract
The international epidemiology of Hepatitis Delta Virus (HDV) is challenging to accurately estimate due to limited active surveillance for this rare infectious disease. Prior HDV epidemiological studies have relied on meta-analysis of aggregated and static datasets. These limitations restrict the capacity to actively detect low-level and/or geographically dispersed changes in the incidence of HDV diagnoses. This study was designed to provide a resource to track and analyze the international HDV epidemiology. Datasets analyzed collectively consisted of >700,000 HBV and >9,000 HDV reported cases ranging between 1999-2020. Datasets mined from government publications were identified for Argentina, Australia, Austria, Brazil, Bulgaria, Canada, Finland, Germany, Macao, Netherlands, New Zealand, Norway, Sweden, Taiwan, Thailand, United Kingdom, and United States. Time series analyses, including Mann-Kendall (MK) trend test, Bayesian Information Criterion (BIC), and hierarchal clustering, were performed to characterize trends in the HDV timelines. An aggregated prevalence of 2,560 HDV/HBV100,000 cases (95% CI 180-4940) or 2.56% HDV/HBV cases was identified, ranging from 0.26% in Canada to 20% in the United States. Structural breaks in the timeline of HDV incidence were identified in 2002, 2012, and 2017, with a significant increase occurring between 2013-2017. Significant increasing trends in reported HDV and HBV cases were observed in 47% and 24% of datasets, respectively. Analyses of the HDV incidence timeline identified four distinct temporal clusters, including Cluster I (Macao, Taiwan), Cluster II (Argentina, Brazil, Germany, Thailand), Cluster III (Bulgaria, Netherlands, New Zealand, United Kingdom, United States) and Cluster IV (Australia, Austria, Canada, Finland, Norway, Sweden). Tracking of HDV and HBV cases on an international scale is essential in defining the global impact of viral hepatitis. Significant disruptions of HDV and HBV epidemiology have been identified. Increased surveillance of HDV is warranted to further define the etiology of the recent breakpoints in the international HDV incidence.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".