Response to COVID-19: building resilience through water and wastewater management in Ghana
Bibliographic record
Abstract
Abstract This study assessed the effects of COVID-19 on Ghana's WASH system. It focused on low-income households and WASH sector stakeholders using Ayawaso East Municipality as a case study to document lessons from the pandemic's impact on the WASH sector. We used the water and sanitation system approach to understand the effects of COVID-19 mitigation measures on the WASH system. Data were collected through surveys, stakeholder engagements, and document analysis. We found that the government's WASH response increased hygiene practices, solid and liquid waste generation, and water consumption. Sanitation service providers experienced reduced demands for their services, lost clients, and increased operational expenditure. The pandemic's impact is gendered, with women and girls experiencing a greater burden. We argue that responses to the pandemic highlight the need and opportunities for sustainable management of sanitation waste through integrated, circular economy business models, turning waste into valuable resources. Responses to COVID-19 in the WASH system are multisectoral because of its interconnected nature, highlighting the need to integrate sectors beyond water and sanitation. This requires improved institutional structures, policies, investment, and professionalising service providers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".