COVID-19 healthcare demand projections: Arizona.
Beginning in March 2020, the United States emerged as the global epicenter for COVID-19 cases with little to guide policy response in the absence of extensive data available for reliable epidemiological modeling in the early phases of the pandemic. In the ensuing weeks, American jurisdictions attemp...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0242588 |
| _version_ | 1818966403217096704 |
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| author | Esma S Gel Megan Jehn Timothy Lant Anna R Muldoon Trisalyn Nelson Heather M Ross |
| author_facet | Esma S Gel Megan Jehn Timothy Lant Anna R Muldoon Trisalyn Nelson Heather M Ross |
| author_sort | Esma S Gel |
| collection | DOAJ |
| description | Beginning in March 2020, the United States emerged as the global epicenter for COVID-19 cases with little to guide policy response in the absence of extensive data available for reliable epidemiological modeling in the early phases of the pandemic. In the ensuing weeks, American jurisdictions attempted to manage disease spread on a regional basis using non-pharmaceutical interventions (i.e., social distancing), as uneven disease burden across the expansive geography of the United States exerted different implications for policy management in different regions. While Arizona policymakers relied initially on state-by-state national modeling projections from different groups outside of the state, we sought to create a state-specific model using a mathematical framework that ties disease surveillance with the future burden on Arizona's healthcare system. Our framework uses a compartmental system dynamics model using a SEIRD framework that accounts for multiple types of disease manifestations for the COVID-19 infection, as well as the observed time delay in epidemiological findings following public policy enactments. We use a compartment initialization logic coupled with a fitting technique to construct projections for key metrics to guide public health policy, including exposures, infections, hospitalizations, and deaths under a variety of social reopening scenarios. Our approach makes use of X-factor fitting and backcasting methods to construct meaningful and reliable models with minimal available data in order to provide timely policy guidance in the early phases of a pandemic. |
| first_indexed | 2024-12-20T13:32:21Z |
| format | Article |
| id | doaj.art-562295ccfa5242cfaffd8b6aae851d38 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T13:32:21Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-562295ccfa5242cfaffd8b6aae851d382022-12-21T19:39:03ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-011512e024258810.1371/journal.pone.0242588COVID-19 healthcare demand projections: Arizona.Esma S GelMegan JehnTimothy LantAnna R MuldoonTrisalyn NelsonHeather M RossBeginning in March 2020, the United States emerged as the global epicenter for COVID-19 cases with little to guide policy response in the absence of extensive data available for reliable epidemiological modeling in the early phases of the pandemic. In the ensuing weeks, American jurisdictions attempted to manage disease spread on a regional basis using non-pharmaceutical interventions (i.e., social distancing), as uneven disease burden across the expansive geography of the United States exerted different implications for policy management in different regions. While Arizona policymakers relied initially on state-by-state national modeling projections from different groups outside of the state, we sought to create a state-specific model using a mathematical framework that ties disease surveillance with the future burden on Arizona's healthcare system. Our framework uses a compartmental system dynamics model using a SEIRD framework that accounts for multiple types of disease manifestations for the COVID-19 infection, as well as the observed time delay in epidemiological findings following public policy enactments. We use a compartment initialization logic coupled with a fitting technique to construct projections for key metrics to guide public health policy, including exposures, infections, hospitalizations, and deaths under a variety of social reopening scenarios. Our approach makes use of X-factor fitting and backcasting methods to construct meaningful and reliable models with minimal available data in order to provide timely policy guidance in the early phases of a pandemic.https://doi.org/10.1371/journal.pone.0242588 |
| spellingShingle | Esma S Gel Megan Jehn Timothy Lant Anna R Muldoon Trisalyn Nelson Heather M Ross COVID-19 healthcare demand projections: Arizona. PLoS ONE |
| title | COVID-19 healthcare demand projections: Arizona. |
| title_full | COVID-19 healthcare demand projections: Arizona. |
| title_fullStr | COVID-19 healthcare demand projections: Arizona. |
| title_full_unstemmed | COVID-19 healthcare demand projections: Arizona. |
| title_short | COVID-19 healthcare demand projections: Arizona. |
| title_sort | covid 19 healthcare demand projections arizona |
| url | https://doi.org/10.1371/journal.pone.0242588 |
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