Systematic Review and Meta-analysis on the Inactivation Rate of Viruses and Bacteriophage by Solar Wavelength Radiation
Bibliographic record
Abstract
Abstract Sunlight is a known biocide, and photodriven inactivation is an important avenue for controlling viruses in both natural and engineered systems. However, there remain significant unknowns regarding damage to viruses by sunlight, including the impact of wavelength and viral characteristics. Herein, a systematic review of the literature and meta-analysis was conducted to identify inactivation rate constants (k-values) when exposed to solar wavelengths (280–700 nm) for common human viruses and surrogates in natural and synthetic matrices. We identified 457 k-values, with 356 for nonenveloped viruses. Extracted rate constants were transformed into UV fluence-normalized k-values to isolate the most photobiologically relevant wavelengths in the solar spectrum and reported for the first time in terms of energy, rather than time, based units. Each spectral region was assessed independently, with UVB illumination reporting the highest inactivation rates, UVA contributing to inactivation both in the presence and absence of photosensitizers, and visible light demonstrating no biocidal activity. Inactivation mechanisms are reviewed identifying knowledge gaps in translating UVC mechanisms to longer wavelengths. The data compiled in this meta-analysis can be applied to inform the environmental transport of viruses, estimate solar disinfection performance in variable light conditions, or design disinfection systems based on UVA and UVB light.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.021 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".