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A predictive model for Covid-19 spread – with application to eight US states and how to end the pandemic
A compartmental model is proposed to predict the coronavirus 2019 (Covid-19) spread. It considers: detected and undetected infected populations, social sequestration, release from sequestration, plus reinfection. This model, consisting of seven coupled equations, has eight coefficients which are eva...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588724/ https://www.ncbi.nlm.nih.gov/pubmed/33028445 http://dx.doi.org/10.1017/S0950268820002423 |
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author | Khan, Z. S. Van Bussel, F. Hussain, F. |
author_facet | Khan, Z. S. Van Bussel, F. Hussain, F. |
author_sort | Khan, Z. S. |
collection | PubMed |
description | A compartmental model is proposed to predict the coronavirus 2019 (Covid-19) spread. It considers: detected and undetected infected populations, social sequestration, release from sequestration, plus reinfection. This model, consisting of seven coupled equations, has eight coefficients which are evaluated by fitting data for eight US states that make up 43% of the US population. The evolution of Covid-19 is fairly similar among the states: variations in contact and undetected recovery rates remain below 5%; however, variations are larger in recovery rate, death rate, reinfection rate, sequestration adherence and release rate from sequestration. Projections based on the current situation indicate that Covid-19 will become endemic. If lockdowns had been kept in place, the number of deaths would most likely have been significantly lower in states that opened up. Additionally, we predict that decreasing contact rate by 10%, or increasing testing by approximately 15%, or doubling lockdown compliance (from the current ~15% to ~30%) will eradicate infections in Texas within a year. Extending our fits for all of the US states, we predict about 11 million total infections (including undetected), and 8 million cumulative confirmed cases by 1 November 2020. |
format | Online Article Text |
id | pubmed-7588724 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-75887242020-10-27 A predictive model for Covid-19 spread – with application to eight US states and how to end the pandemic Khan, Z. S. Van Bussel, F. Hussain, F. Epidemiol Infect Original Paper A compartmental model is proposed to predict the coronavirus 2019 (Covid-19) spread. It considers: detected and undetected infected populations, social sequestration, release from sequestration, plus reinfection. This model, consisting of seven coupled equations, has eight coefficients which are evaluated by fitting data for eight US states that make up 43% of the US population. The evolution of Covid-19 is fairly similar among the states: variations in contact and undetected recovery rates remain below 5%; however, variations are larger in recovery rate, death rate, reinfection rate, sequestration adherence and release rate from sequestration. Projections based on the current situation indicate that Covid-19 will become endemic. If lockdowns had been kept in place, the number of deaths would most likely have been significantly lower in states that opened up. Additionally, we predict that decreasing contact rate by 10%, or increasing testing by approximately 15%, or doubling lockdown compliance (from the current ~15% to ~30%) will eradicate infections in Texas within a year. Extending our fits for all of the US states, we predict about 11 million total infections (including undetected), and 8 million cumulative confirmed cases by 1 November 2020. Cambridge University Press 2020-10-08 /pmc/articles/PMC7588724/ /pubmed/33028445 http://dx.doi.org/10.1017/S0950268820002423 Text en © The Author(s) 2020 http://creativecommons.org/licenses/by-nc-sa/4.0/ http://creativecommons.org/licenses/by-nc-sa/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike licence (http://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is included and the original work is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use. |
spellingShingle | Original Paper Khan, Z. S. Van Bussel, F. Hussain, F. A predictive model for Covid-19 spread – with application to eight US states and how to end the pandemic |
title | A predictive model for Covid-19 spread – with application to eight US states and how to end the pandemic |
title_full | A predictive model for Covid-19 spread – with application to eight US states and how to end the pandemic |
title_fullStr | A predictive model for Covid-19 spread – with application to eight US states and how to end the pandemic |
title_full_unstemmed | A predictive model for Covid-19 spread – with application to eight US states and how to end the pandemic |
title_short | A predictive model for Covid-19 spread – with application to eight US states and how to end the pandemic |
title_sort | predictive model for covid-19 spread – with application to eight us states and how to end the pandemic |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588724/ https://www.ncbi.nlm.nih.gov/pubmed/33028445 http://dx.doi.org/10.1017/S0950268820002423 |
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