COVID-19: urgent actions, critical reflections and future relevance of ‘WaSH’: lessons for the current and future pandemics
Bibliographic record
Abstract
The COVID-19 pandemic placed hygiene at the centre of disease prevention. Yet, access to the levels of water supply that support good hand hygiene and institutional cleaning, our understanding of hygiene behaviours, and access to soap are deficient in low-, middle- and high-income countries. This paper reviews the role of water, sanitation and hygiene (WaSH) in disease emergence, previous outbreaks, combatting COVID-19 and in preparing for future pandemics. We consider settings where these factors are particularly important and identify key preventive contributions to disease control and gaps in the evidence base. Urgent substantial action is required to remedy deficiencies in WaSH, particularly the provision of reliable, continuous piped water on-premises for all households and settings. Hygiene promotion programmes, underpinned by behavioural science, must be adapted to high-risk populations (such as the elderly and marginalised) and settings (such as healthcare facilities, transport hubs and workplaces). WaSH must be better integrated into preparation plans and with other sectors in prevention efforts. More finance and better use of financing instruments would extend and improve WaSH services. The lessons outlined justify no-regrets investment by government in response to and recovery from the current pandemic; to improve day-to-day lives and as preparedness for future pandemics.
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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 | 0.027 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".