A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation.
PLoS Negl Trop Dis
; 14(2): e0008065, 2020 02.
Article
in English
| MEDLINE | ID: covidwho-1765523
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 (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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Coronavirus Infections
/
Epidemics
/
Middle East Respiratory Syndrome Coronavirus
/
Models, Biological
Type of study:
Observational study
/
Prognostic study
Topics:
Vaccines
Limits:
Humans
Language:
English
Journal:
PLoS Negl Trop Dis
Journal subject:
Tropical Medicine
Year:
2020
Document Type:
Article
Affiliation country:
Journal.pntd.0008065
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