About: A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation

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About: A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation
Creator	Chattopadhyay, Joydev Ghosh, Indrajit Rodó, Xavier Sardar, Tridip Althouse, Benjamin
Source	Medline; PMC
abstract	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 (R(0)) warns that MERS-CoV may easily take an epidemic form. The best model correctly captures this feature, indicating a high epidemic risk (1≤R(0)≤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.
has issue date	2020-02-14(xsd:dateTime)
bibo:doi	10.1371/journal.pntd.0008065
bibo:pmid	32059047
has license	cc-by
schema:url	https://doi.org/10.1371/journal.pntd.0008065
resource representing a document's title	A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation
has PubMed Central identifier	PMC7046297
has PubMed identifier	32059047
schema:publication	PLoS Negl Trop Dis
resource representing a document's body	covid:PMC7046297#body_text
is schema:about of	named entity 'HIGH RISK' named entity 'MODEL' named entity 'STRAIN' named entity 'MERS-COV' named entity 'PROPAGATION' named entity 'INFECTION' named entity 'EPIDEMIC' named entity 'high risk' named entity 'MERS-CoV' named entity 'MERS-CoV' named entity 'zoonotic transmission' named entity 'dromedaries' named entity 'index case' named entity 'human-to-human' named entity 'disease transmission' named entity 'MERS-CoV' named entity 'Riyadh' named entity 'epidemiological' named entity 'mathematical models' named entity 'SAT' named entity 'MERS-CoV' named entity 'tropism' named entity 'force of infection' named entity 'epidemic' named entity 'Riyadh' named entity 'standard precautions' named entity 'probability' named entity 'force of infection' named entity 'probability' named entity 'MERS-CoV' named entity 'health-care' named entity 'reproduction number' named entity 'MERS-CoV' named entity 'MERS-CoV' named entity 'healthcare facilities' named entity 'epidemics' named entity 'standard deviation' named entity 'Riyadh' named entity 'disease outbreaks' named entity 'Riyadh' named entity 'super-spreading' named entity 'outbreak of MERS' named entity 'infection' named entity 'mathematical models' named entity 'Saudi Arabia' named entity 'MERS-CoV' named entity 'MERS-CoV' named entity 'disease incidence' named entity 'basic reproduction number' named entity 'camel' named entity 'cholera' named entity 'infection prevention and control' named entity 'Middle East' named entity 'zoonotic' named entity 'Riyadh' named entity 'dengue' named entity '3.5' named entity 'MERS-CoV' named entity 'infection' named entity 'basic reproduction number' named entity 'mathematical model' named entity 'hospital settings' named entity 'Saudi-Arabia' named entity 'prediction interval' named entity 'MERS-CoV' named entity 'Egypt' named entity 'epidemiological' named entity 'health-care' named entity 'infection' named entity 'transmission-based precautions' named entity 'health-care' named entity 'Riyadh' named entity 'SAT' named entity 'super-spreaders' named entity 'infection' named entity 'MERS-CoV' named entity 'virus' named entity 'super-spreaders'

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