An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system.
An operationally implementable predictive model has been developed to forecast the number of COVID-19 infections in the patient population, hospital floor and ICU censuses, ventilator and related supply chain demand. The model is intended for clinical, operational, financial and supply chain leaders...
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Public Library of Science (PLoS)
2021
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oai:doaj.org-article:34b95e3bc7374ebbbff5e5545344b6b22021-12-02T20:07:49ZAn operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system.1932-620310.1371/journal.pone.0258710https://doaj.org/article/34b95e3bc7374ebbbff5e5545344b6b22021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258710https://doaj.org/toc/1932-6203An operationally implementable predictive model has been developed to forecast the number of COVID-19 infections in the patient population, hospital floor and ICU censuses, ventilator and related supply chain demand. The model is intended for clinical, operational, financial and supply chain leaders and executives of a comprehensive healthcare system responsible for making decisions that depend on epidemiological contingencies. This paper describes the model that was implemented at NorthShore University HealthSystem and is applicable to any communicable disease whose risk of reinfection for the duration of the pandemic is negligible.Daniel ChertokChad KonchakNirav ShahKamaljit SinghLoretta AuJared HammernikBrian MurrayAnthony SolomonidesErnest WangLakshmi HalasyamaniPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258710 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q Daniel Chertok Chad Konchak Nirav Shah Kamaljit Singh Loretta Au Jared Hammernik Brian Murray Anthony Solomonides Ernest Wang Lakshmi Halasyamani An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system. |
| description |
An operationally implementable predictive model has been developed to forecast the number of COVID-19 infections in the patient population, hospital floor and ICU censuses, ventilator and related supply chain demand. The model is intended for clinical, operational, financial and supply chain leaders and executives of a comprehensive healthcare system responsible for making decisions that depend on epidemiological contingencies. This paper describes the model that was implemented at NorthShore University HealthSystem and is applicable to any communicable disease whose risk of reinfection for the duration of the pandemic is negligible. |
| format |
article |
| author |
Daniel Chertok Chad Konchak Nirav Shah Kamaljit Singh Loretta Au Jared Hammernik Brian Murray Anthony Solomonides Ernest Wang Lakshmi Halasyamani |
| author_facet |
Daniel Chertok Chad Konchak Nirav Shah Kamaljit Singh Loretta Au Jared Hammernik Brian Murray Anthony Solomonides Ernest Wang Lakshmi Halasyamani |
| author_sort |
Daniel Chertok |
| title |
An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system. |
| title_short |
An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system. |
| title_full |
An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system. |
| title_fullStr |
An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system. |
| title_full_unstemmed |
An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system. |
| title_sort |
operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2021 |
| url |
https://doaj.org/article/34b95e3bc7374ebbbff5e5545344b6b2 |
| work_keys_str_mv |
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| _version_ |
1718375232921665536 |