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Simulating the progression of the COVID-19 disease in Cameroon using SIR models
This paper analyses the evolution of COVID-19 in Cameroon over the period March 6–April 2020 using SIR models. Specifically, we 1) evaluate the basic reproduction number of the virus, 2) determine the peak of the infection and the spread-out period of the disease, and 3) simulate the interventions o...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7447022/ https://www.ncbi.nlm.nih.gov/pubmed/32841283 http://dx.doi.org/10.1371/journal.pone.0237832 |
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author | Nguemdjo, Ulrich Meno, Freeman Dongfack, Audric Ventelou, Bruno |
author_facet | Nguemdjo, Ulrich Meno, Freeman Dongfack, Audric Ventelou, Bruno |
author_sort | Nguemdjo, Ulrich |
collection | PubMed |
description | This paper analyses the evolution of COVID-19 in Cameroon over the period March 6–April 2020 using SIR models. Specifically, we 1) evaluate the basic reproduction number of the virus, 2) determine the peak of the infection and the spread-out period of the disease, and 3) simulate the interventions of public health authorities. Data used in this study is obtained from the Cameroonian Public Health Ministry. The results suggest that over the identified period, the reproduction number of COVID-19 in Cameroon is about 1.5, and the peak of the infection should have occurred at the end of May 2020 with about 7.7% of the population infected. Furthermore, the implementation of efficient public health policies could help flatten the epidemic curve. |
format | Online Article Text |
id | pubmed-7447022 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-74470222020-08-31 Simulating the progression of the COVID-19 disease in Cameroon using SIR models Nguemdjo, Ulrich Meno, Freeman Dongfack, Audric Ventelou, Bruno PLoS One Research Article This paper analyses the evolution of COVID-19 in Cameroon over the period March 6–April 2020 using SIR models. Specifically, we 1) evaluate the basic reproduction number of the virus, 2) determine the peak of the infection and the spread-out period of the disease, and 3) simulate the interventions of public health authorities. Data used in this study is obtained from the Cameroonian Public Health Ministry. The results suggest that over the identified period, the reproduction number of COVID-19 in Cameroon is about 1.5, and the peak of the infection should have occurred at the end of May 2020 with about 7.7% of the population infected. Furthermore, the implementation of efficient public health policies could help flatten the epidemic curve. Public Library of Science 2020-08-25 /pmc/articles/PMC7447022/ /pubmed/32841283 http://dx.doi.org/10.1371/journal.pone.0237832 Text en © 2020 Nguemdjo et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Nguemdjo, Ulrich Meno, Freeman Dongfack, Audric Ventelou, Bruno Simulating the progression of the COVID-19 disease in Cameroon using SIR models |
title | Simulating the progression of the COVID-19 disease in Cameroon using SIR models |
title_full | Simulating the progression of the COVID-19 disease in Cameroon using SIR models |
title_fullStr | Simulating the progression of the COVID-19 disease in Cameroon using SIR models |
title_full_unstemmed | Simulating the progression of the COVID-19 disease in Cameroon using SIR models |
title_short | Simulating the progression of the COVID-19 disease in Cameroon using SIR models |
title_sort | simulating the progression of the covid-19 disease in cameroon using sir models |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7447022/ https://www.ncbi.nlm.nih.gov/pubmed/32841283 http://dx.doi.org/10.1371/journal.pone.0237832 |
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