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The COVID-19 pandemic preparedness simulation tool: CovidSIM
BACKGROUND: Efficient control and management in the ongoing COVID-19 pandemic needs to carefully balance economical and realizable interventions. Simulation models can play a cardinal role in forecasting possible scenarios to sustain decision support. METHODS: We present a sophisticated extension of...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7675392/ https://www.ncbi.nlm.nih.gov/pubmed/33213360 http://dx.doi.org/10.1186/s12879-020-05566-7 |
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author | Schneider, Kristan A. Ngwa, Gideon A. Schwehm, Markus Eichner, Linda Eichner, Martin |
author_facet | Schneider, Kristan A. Ngwa, Gideon A. Schwehm, Markus Eichner, Linda Eichner, Martin |
author_sort | Schneider, Kristan A. |
collection | PubMed |
description | BACKGROUND: Efficient control and management in the ongoing COVID-19 pandemic needs to carefully balance economical and realizable interventions. Simulation models can play a cardinal role in forecasting possible scenarios to sustain decision support. METHODS: We present a sophisticated extension of a classical SEIR model. The simulation tool CovidSIM Version 1.0 is an openly accessible web interface to interactively conduct simulations of this model. The simulation tool is used to assess the effects of various interventions, assuming parameters that reflect the situation in Austria as an example. RESULTS: Strict contact reduction including isolation of infected persons in quarantine wards and at home can substantially delay the peak of the epidemic. Home isolation of infected individuals effectively reduces the height of the peak. Contact reduction by social distancing, e.g., by curfews, sanitary behavior, etc. are also effective in delaying the epidemic peak. CONCLUSIONS: Contact-reducing mechanisms are efficient to delay the peak of the epidemic. They might also be effective in decreasing the peak number of infections depending on seasonal fluctuations in the transmissibility of the disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (doi:10.1186/s12879-020-05566-7). |
format | Online Article Text |
id | pubmed-7675392 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-76753922020-11-19 The COVID-19 pandemic preparedness simulation tool: CovidSIM Schneider, Kristan A. Ngwa, Gideon A. Schwehm, Markus Eichner, Linda Eichner, Martin BMC Infect Dis Research Article BACKGROUND: Efficient control and management in the ongoing COVID-19 pandemic needs to carefully balance economical and realizable interventions. Simulation models can play a cardinal role in forecasting possible scenarios to sustain decision support. METHODS: We present a sophisticated extension of a classical SEIR model. The simulation tool CovidSIM Version 1.0 is an openly accessible web interface to interactively conduct simulations of this model. The simulation tool is used to assess the effects of various interventions, assuming parameters that reflect the situation in Austria as an example. RESULTS: Strict contact reduction including isolation of infected persons in quarantine wards and at home can substantially delay the peak of the epidemic. Home isolation of infected individuals effectively reduces the height of the peak. Contact reduction by social distancing, e.g., by curfews, sanitary behavior, etc. are also effective in delaying the epidemic peak. CONCLUSIONS: Contact-reducing mechanisms are efficient to delay the peak of the epidemic. They might also be effective in decreasing the peak number of infections depending on seasonal fluctuations in the transmissibility of the disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (doi:10.1186/s12879-020-05566-7). BioMed Central 2020-11-19 /pmc/articles/PMC7675392/ /pubmed/33213360 http://dx.doi.org/10.1186/s12879-020-05566-7 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Schneider, Kristan A. Ngwa, Gideon A. Schwehm, Markus Eichner, Linda Eichner, Martin The COVID-19 pandemic preparedness simulation tool: CovidSIM |
title | The COVID-19 pandemic preparedness simulation tool: CovidSIM |
title_full | The COVID-19 pandemic preparedness simulation tool: CovidSIM |
title_fullStr | The COVID-19 pandemic preparedness simulation tool: CovidSIM |
title_full_unstemmed | The COVID-19 pandemic preparedness simulation tool: CovidSIM |
title_short | The COVID-19 pandemic preparedness simulation tool: CovidSIM |
title_sort | covid-19 pandemic preparedness simulation tool: covidsim |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7675392/ https://www.ncbi.nlm.nih.gov/pubmed/33213360 http://dx.doi.org/10.1186/s12879-020-05566-7 |
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