Fomite workshop recommendations addressing the role of surfaces in virus transmission in the built environment
Bibliographic record
Abstract
The emergence of SARS-CoV-2 has led to a need to assess the role of fomites in viral transmission within the built environment. Assessing the role of fomites is necessary for developing intervention strategies for controlling emerging pathogens. A fomite workshop with experts was convened in November 2024 by academia, several government agencies, and public health officials to evaluate existing data and discuss how to mitigate risks. Fomite transmission is influenced by the nature of the built environment, population density and proximity, environmental factors (humidity, heat, etc.), virus survival, surface type, engineering controls (ventilation, physical barriers, etc.), and human behaviors. Based on our current data, direct contact with a contaminated surface/fomite, even for respiratory viruses, presents a risk of viral exposure and transmission by both contact with the fomite and resuspension in the air. Even respiratory viruses can be resuspended from fomites following human and pet movement, activities (e.g., vacuuming, toilet flushing, etc.), or changes in ventilation/indoor airflow. After resuspension from surfaces, microbes can be potentially inhaled (contributing to droplet and/or aerosol exposure) and/or re-deposited from primary to secondary fomites. Development of standard methods (molecular, chemical/physical, and infectivity assays) for detecting the presence of viruses on fomites and human behavior modeling would help to determine the most effective infection prevention strategies.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.025 | 0.011 |
| Insufficient payload (model declined to judge) | 0.063 | 0.029 |
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".