COVID-19 epidemic peaks distribution in the United-States of America, from epidemiological modeling to public health policies
Abstract COVID-19 prediction models are characterized by uncertainties due to fluctuating parameters, such as changes in infection or recovery rates. While deterministic models often predict epidemic peaks too early, incorporating these fluctuations into the SIR model can provide a more accurate rep...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-30014-2 |
| _version_ | 1797853889887731712 |
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| author | Alexandre Vallée Davide Faranda Maxence Arutkin |
| author_facet | Alexandre Vallée Davide Faranda Maxence Arutkin |
| author_sort | Alexandre Vallée |
| collection | DOAJ |
| description | Abstract COVID-19 prediction models are characterized by uncertainties due to fluctuating parameters, such as changes in infection or recovery rates. While deterministic models often predict epidemic peaks too early, incorporating these fluctuations into the SIR model can provide a more accurate representation of peak timing. Predicting R0, the basic reproduction number, remains a major challenge with significant implications for government policy and strategy. In this study, we propose a tool for policy makers to show the effects of possible fluctuations in policy strategies on different R0 levels. Results show that epidemic peaks in the United States occur at varying dates, up to 50, 87, and 82 days from the beginning of the second, third, and fourth waves. Our findings suggest that inaccurate predictions and public health policies may result from underestimating fluctuations in infection or recovery rates. Therefore, incorporating fluctuations into SIR models should be considered when predicting epidemic peak times to inform appropriate public health responses. |
| first_indexed | 2024-04-09T19:58:07Z |
| format | Article |
| id | doaj.art-f40da62c647d438fbbb1ef336fc33462 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T19:58:07Z |
| publishDate | 2023-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-f40da62c647d438fbbb1ef336fc334622023-04-03T05:23:02ZengNature PortfolioScientific Reports2045-23222023-03-011311810.1038/s41598-023-30014-2COVID-19 epidemic peaks distribution in the United-States of America, from epidemiological modeling to public health policiesAlexandre Vallée0Davide Faranda1Maxence Arutkin2Department of Epidemiology-Data-Biostatistics, Delegation of Clinical Research and Innovation (DRCI), Foch HospitalLaboratoire des Sciences du Climat et de l’Environnement, CEA Saclay l’Orme des Merisiers, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay, IPSLSchool of Chemistry, The Center for Physics and Chemistry of Living Systems, Tel Aviv UniversityAbstract COVID-19 prediction models are characterized by uncertainties due to fluctuating parameters, such as changes in infection or recovery rates. While deterministic models often predict epidemic peaks too early, incorporating these fluctuations into the SIR model can provide a more accurate representation of peak timing. Predicting R0, the basic reproduction number, remains a major challenge with significant implications for government policy and strategy. In this study, we propose a tool for policy makers to show the effects of possible fluctuations in policy strategies on different R0 levels. Results show that epidemic peaks in the United States occur at varying dates, up to 50, 87, and 82 days from the beginning of the second, third, and fourth waves. Our findings suggest that inaccurate predictions and public health policies may result from underestimating fluctuations in infection or recovery rates. Therefore, incorporating fluctuations into SIR models should be considered when predicting epidemic peak times to inform appropriate public health responses.https://doi.org/10.1038/s41598-023-30014-2 |
| spellingShingle | Alexandre Vallée Davide Faranda Maxence Arutkin COVID-19 epidemic peaks distribution in the United-States of America, from epidemiological modeling to public health policies Scientific Reports |
| title | COVID-19 epidemic peaks distribution in the United-States of America, from epidemiological modeling to public health policies |
| title_full | COVID-19 epidemic peaks distribution in the United-States of America, from epidemiological modeling to public health policies |
| title_fullStr | COVID-19 epidemic peaks distribution in the United-States of America, from epidemiological modeling to public health policies |
| title_full_unstemmed | COVID-19 epidemic peaks distribution in the United-States of America, from epidemiological modeling to public health policies |
| title_short | COVID-19 epidemic peaks distribution in the United-States of America, from epidemiological modeling to public health policies |
| title_sort | covid 19 epidemic peaks distribution in the united states of america from epidemiological modeling to public health policies |
| url | https://doi.org/10.1038/s41598-023-30014-2 |
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