Predicted and Measured Virucidal Efficacies of Microbicides for Emerging and Re-emerging Viruses Associated with WHO Priority Diseases
Bibliographic record
Abstract
The World Health Organization has updated its list of priority diseases for 2021 to currently include the following: Ebola virus disease and Marburg virus disease (Filoviridae), Nipah and henipaviral diseases (Paramyxoviridae), Lassa fever (Arenaviridae), Rift Valley fever and Crimean-Congo hemorrhagic fever (Bunyaviridae), Zika (Flaviviridae), COVID-19 (SARS-CoV-2) including Delta, Omicron, and other variants of concern, Middle East respiratory syndrome, severe acute respiratory syndrome (Coronaviridae), and the always present “disease X,” which is a term used for the next emerging pathogen of concern that is not known about today. In this chapter, we review the virucidal efficacy data for microbicides (disinfectants and antiseptics, also known as surface and hand hygiene agents or collectively hygiene agents) for the viruses associated with these diseases. As these diseases are each caused by lipid-enveloped viruses, the susceptibilities of the viruses to virucidal agents are informed by the known hierarchy of susceptibility of pathogens to microbicides. The unique susceptibility of lipid-enveloped viruses to most classes of microbicides is based on the common mechanism of action of envelope-disrupting microbicides. Empirical data supporting this principle and the mitigational role of targeted hygiene in infection prevention and control (IPAC) discussed are presented.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".