Spread of Puumala Hantavirus to New Areas in a Large Croatian Outbreak of Hemorrhagic Fever with Renal Syndrome, 2021
Bibliographic record
Abstract
Background: The largest documented outbreak of hemorrhagic fever with renal syndrome occurred in Primorje-Gorski Kotar County, Croatia, in 2021, marking the first-time cases of hantavirus infection recorded outside of the known endemic region in the north of the county. Aim: To identify the factors contributing to the spread of the outbreak and to compare risk factors for acquiring hantavirus infection in the endemic and newly affected regions. Methods and Results: A total of 189 cases were confirmed by positive Puumala IgM/IgG antibodies (93.6%), and 13 probable cases were identified by clinical and epidemiological data (6.4%) using a structured questionnaire. Of the 179 cases with available clinical data, 59 (33.0%) were hospitalized. Three cases received hemodialysis, and no deaths were reported. Among 170 cases with information on exposures, 66 (38.8%) reported occupational risk. Cases in the northern part of county were more likely to have been infected in early spring (OR 27.1, 95% CI 2.93–250.7), to report seeing a rodent (OR 6.5; 95%CI 2.3–18.4), and to know someone with hemorrhagic fever with renal syndrome (HFRS) (OR 3.0; 95%CI 1.2–8.0) than cases from the southern part of the county. Data from Croatian Forests Ltd. suggested that an unusually good production of beech seeds in 2020 may have contributed to an increased rodent population in 2021. However, average temperature, rainfall, and humidity data from 2021 did not illustrate a significant difference from previous years (Kruskal–Wallis p = 0.837, p = 0.999, p = 0.108). Conclusion: The 2021 HFRS outbreak was likely fueled by an abundant rodent population and virus transmission in rodent hosts. Human activity, environmental factors, and the ensuing animal–human interactions have spread hantavirus infection from Croatia’s mountainous region to a previously nonendemic coastal area with a Mediterranean climate.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".