A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation.
Middle East Respiratory Syndrome Coronavirus (MERS-CoV) causes severe acute respiratory illness with a case fatality rate (CFR) of 35,5%. The highest number of MERS-CoV cases are from Saudi-Arabia, the major worldwide hotspot for this disease. In the absence of neither effective treatment nor a read...
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ftdoajarticles:oai:doaj.org/article:49d48b995ace40f1a3b0cd453fc44105 2023-05-15T15:13:27+02:00 A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation. Tridip Sardar Indrajit Ghosh Xavier Rodó Joydev Chattopadhyay 2020-02-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0008065 https://doaj.org/article/49d48b995ace40f1a3b0cd453fc44105 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0008065 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0008065 https://doaj.org/article/49d48b995ace40f1a3b0cd453fc44105 PLoS Neglected Tropical Diseases, Vol 14, Iss 2, p e0008065 (2020) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2020 ftdoajarticles https://doi.org/10.1371/journal.pntd.0008065 2022-12-31T07:48:36Z Middle East Respiratory Syndrome Coronavirus (MERS-CoV) causes severe acute respiratory illness with a case fatality rate (CFR) of 35,5%. The highest number of MERS-CoV cases are from Saudi-Arabia, the major worldwide hotspot for this disease. In the absence of neither effective treatment nor a ready-to-use vaccine and with yet an incomplete understanding of its epidemiological cycle, prevention and containment measures can be derived from mathematical models of disease epidemiology. We constructed 2-strain models to predict past outbreaks in the interval 2012-2016 and derive key epidemiological information for Macca, Madina and Riyadh. We approached variability in infection through three different disease incidence functions capturing social behavior in response to an epidemic (e.g. Bilinear, BL; Non-monotone, NM; and Saturated, SAT models). The best model combination successfully anticipated the total number of MERS-CoV clinical cases for the 2015-2016 season and accurately predicted both the number of cases at the peak of seasonal incidence and the overall shape of the epidemic cycle. The evolution in the basic reproduction number (R0) warns that MERS-CoV may easily take an epidemic form. The best model correctly captures this feature, indicating a high epidemic risk (1≤R0≤2,5) in Riyadh and Macca and confirming the alleged co-circulation of more than one strain. Accurate predictions of the future MERS-CoV peak week, as well as the number of cases at the peak are now possible. These results indicate public health agencies should be aware that measures for strict containment are urgently needed before new epidemics take off in the region. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 14 2 e0008065 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Tridip Sardar Indrajit Ghosh Xavier Rodó Joydev Chattopadhyay A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation. |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Middle East Respiratory Syndrome Coronavirus (MERS-CoV) causes severe acute respiratory illness with a case fatality rate (CFR) of 35,5%. The highest number of MERS-CoV cases are from Saudi-Arabia, the major worldwide hotspot for this disease. In the absence of neither effective treatment nor a ready-to-use vaccine and with yet an incomplete understanding of its epidemiological cycle, prevention and containment measures can be derived from mathematical models of disease epidemiology. We constructed 2-strain models to predict past outbreaks in the interval 2012-2016 and derive key epidemiological information for Macca, Madina and Riyadh. We approached variability in infection through three different disease incidence functions capturing social behavior in response to an epidemic (e.g. Bilinear, BL; Non-monotone, NM; and Saturated, SAT models). The best model combination successfully anticipated the total number of MERS-CoV clinical cases for the 2015-2016 season and accurately predicted both the number of cases at the peak of seasonal incidence and the overall shape of the epidemic cycle. The evolution in the basic reproduction number (R0) warns that MERS-CoV may easily take an epidemic form. The best model correctly captures this feature, indicating a high epidemic risk (1≤R0≤2,5) in Riyadh and Macca and confirming the alleged co-circulation of more than one strain. Accurate predictions of the future MERS-CoV peak week, as well as the number of cases at the peak are now possible. These results indicate public health agencies should be aware that measures for strict containment are urgently needed before new epidemics take off in the region. |
format |
Article in Journal/Newspaper |
author |
Tridip Sardar Indrajit Ghosh Xavier Rodó Joydev Chattopadhyay |
author_facet |
Tridip Sardar Indrajit Ghosh Xavier Rodó Joydev Chattopadhyay |
author_sort |
Tridip Sardar |
title |
A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation. |
title_short |
A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation. |
title_full |
A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation. |
title_fullStr |
A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation. |
title_full_unstemmed |
A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation. |
title_sort |
realistic two-strain model for mers-cov infection uncovers the high risk for epidemic propagation. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2020 |
url |
https://doi.org/10.1371/journal.pntd.0008065 https://doaj.org/article/49d48b995ace40f1a3b0cd453fc44105 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 14, Iss 2, p e0008065 (2020) |
op_relation |
https://doi.org/10.1371/journal.pntd.0008065 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0008065 https://doaj.org/article/49d48b995ace40f1a3b0cd453fc44105 |
op_doi |
https://doi.org/10.1371/journal.pntd.0008065 |
container_title |
PLOS Neglected Tropical Diseases |
container_volume |
14 |
container_issue |
2 |
container_start_page |
e0008065 |
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1766343997720100864 |