Optimal locations of emergency medical tents for pandemic preparedness in Quezon City using facility location models

Clicks: 3
ID: 286871
2022
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #2,601 of 3,757 articles by views in Malay Journal

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
The outbreak of COVID-19, caused by the SARS-CoV-2 virus, is a global pandemic which caused serious implications for hospitals. Hospitals are now struggling to cope with the demand for care. In this paper, we have shown how facility location models can be used to identify optimal locations for Emergency Medical Tents (EMT) which can help hospitals in dealing with the high demand for healthcare during the pandemic. We have used two different facility location models: the Maximal Coverage Location Problem (MCLP) and the Set Covering Location Problem (SCLP). The MCLP model maximizes demand covered with a limited number of facilities, while the SCLP model ensures full coverage with a minimum number of facilities. The National Capital Region (NCR) has the highest accumulated COVID-19 cases in the Philippines. Among these cities, Quezon City has the highest population density which is the reason for being the focus of this study.
Reference Key
persistent_1760659913_68f189c96637b Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fuentebella, Joaquin Miguel Lilam
Journal Malay Journal
Year 2022
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdb_math/8
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.