Hantavirus infections in Italy: not reported doesn't mean inexistent.
Bibliographic record
Abstract
BACKGROUND: Hantaviruses can cause serious human diseases including hemorrhagic fever with renal syndrome (HFRS) and Hantavirus Cardiopulmonary Syndrome (HCPS). European Hantavirus are usually associated with HFRS, and their geographical distribution mirrors the ecology of reservoir host species. Epidemiology of HFRS is well-studied in Western Europe, but data from Italy are fragmentary. METHODS: We searched into two different databases (PubMed and EMBASE), focusing on studies reporting the prevalence of Hantaviruses in Italy. Data were extracted using a standardized assessment form, and results of the analyses were systematically reported, summarized and compared. RESULTS: We identified a total of 18 articles, including 12 reports (total population: 5,336 subjects, 1981-2019) and 6 case reports (1984-2019). In total, 200 subjects exhibited some degree of seropositivity, with a pooled seroprevalence of 1.7% (95% confidence interval 0.7%-4.0%) in the general population. Higher occurrence was reported in selected subgroups, i.e. acute (28.7%, 95%CI 22.1-36.2) and chronic (6.6%, 95%CI 4.7-9.1) renal failure, forestry workers (3.0%, 95%CI 1.4-6.5, actual range 0.0 to 10.8%). CONCLUSIONS: In the last decade, no human cases of hantavirus infection have been officially reported in Italy. However, our analysis stresses the actual occurrence of Hantavirus among general population and in selected population groups. Further studies on hantavirus infection rates in reservoir host species (rodents, shrews, and bats) and virus transmission to humans are needed to prevent outbreaks in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".