Effect of Handwashing During COVID-19 Pandemic to Domestic Water Estimation: Case Study in Banda Aceh City, Indonesia
Bibliographic record
Abstract
New normal routines have continued to campaign since the COVID-19 pandemic broke out in 2019.One of these new habits is to keep washing your hands after every activity.Hand washing can be done using hand sanitizer or soap and washed in running water.As a result, the need for water to meet the habit of washing hands is expected to affect domestic water needs in a certain area.Therefore, this study aims to estimate the increase in domestic water demand during the COVID-19 pandemic caused by the new routine of washing hands in the research area in the city of Banda Aceh, Indonesia.In addition, this study will also estimate domestic water needs until 2030 if the COVID-19 pandemic has not ended.Innovations in this research can help increase efficiency in water use and help prevent the spread of disease.This study uses a sampling method in several places in Banda Aceh city to obtain data related to the volumetric water used, handwashing time, and frequency of handwashing.Besides, data in water discharge from water supply companies in Banda Aceh city from 2018 to 2020 was also collected.Finally, data on the population of Banda Aceh city was also collected.The information and data are then analyzed using a statistical approach between supply and demand.Although it appears that there is a projected increase in domestic water demand of 1.89% per year due to the COVID-19 pandemic from 2021 to 2030, this is still 41.48% greater than the ability of water supply companies in the city of Banda Aceh to meet domestic water needs up to 2030.In conclusion, if the pandemic continues until 2030, with the expected increase in population, the domestic water needs in Banda Aceh city will still be fulfilled.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".