Facets (new session)
Description
Metadata
Settings
owl:sameAs
Inference Rule:
b3s
b3sifp
dbprdf-label
facets
http://dbpedia.org/resource/inference/rules/dbpedia#
http://dbpedia.org/resource/inference/rules/opencyc#
http://dbpedia.org/resource/inference/rules/umbel#
http://dbpedia.org/resource/inference/rules/yago#
http://dbpedia.org/schema/property_rules#
http://www.ontologyportal.org/inference/rules/SUMO#
http://www.ontologyportal.org/inference/rules/WordNet#
http://www.w3.org/2002/07/owl#
ldp
oplweb
skos-trans
virtrdf-label
None
About:
Prediction of the COVID-19 spread in African countries and implications for prevention and controls: A case study in South Africa, Egypt, Algeria, Nigeria, Senegal and Kenya
Goto
Sponge
NotDistinct
Permalink
An Entity of Type :
schema:ScholarlyArticle
, within Data Space :
wasabi.inria.fr
associated with source
document(s)
Type:
Academic Article
research paper
schema:ScholarlyArticle
New Facet based on Instances of this Class
Attributes
Values
type
Academic Article
research paper
schema:ScholarlyArticle
isDefinedBy
Covid-on-the-Web dataset
has title
Prediction of the COVID-19 spread in African countries and implications for prevention and controls: A case study in South Africa, Egypt, Algeria, Nigeria, Senegal and Kenya
Creator
Zhao, Xin
Li, Xin
Li, Feng
Liu, Feng
Wang,
Zhu, Gaofeng
Africa, Kenya
Liu, Gaofeng
Ma, Chunfeng
Ma, Liangxu
Wang, Liangxu
Zhao, Kenya
Zhu, Chunfeng
Source
Elsevier; Medline; PMC; WHO
abstract
Abstract COVID-19 (Corona Virus Disease 2019) is globally spreading and the international cooperation is urgently required in joint prevention and control of the epidemic. Using the Maximum-Hasting (MH) parameter estimation method and the modified Susceptible Exposed Infectious Recovered (SEIR) model, the spread of the epidemic under three intervention scenarios (suppression, mitigation, mildness) is simulated and predicted in South Africa, Egypt, and Algeria, where the epidemic situations are severe. The studies are also conducted in Nigeria, Senegal and Kenya, where the epidemic situations are growing rapidly and the socio-economic are relatively under-developed, resulting in more difficulties in preventing the epidemic. Results indicated that the epidemic can be basically controlled in late April with strict control of scenario one, manifested by the circumstance in the South Africa and Senegal. Under moderate control of scenario two, the number of infected people will increase by 1.43–1.55 times of that in scenario one, the date of the epidemic being controlled will be delayed by about 10 days, and Algeria, Nigeria, and Kenya are in accordance with this situation. In the third scenario of weak control, the epidemic will be controlled by late May, the total number of infected cases will double that in scenario two, and Egypt is in line with this prediction. In the end, a series of epidemic controlling methods are proposed, including patient quarantine, close contact tracing, population movement control, government intervention, city and county epidemic risk level classification, and medical cooperation and the Chinese assistance.
has issue date
2020-04-25
(
xsd:dateTime
)
bibo:doi
10.1016/j.scitotenv.2020.138959
bibo:pmid
32375067
has license
els-covid
sha1sum (hex)
b2cc35b4f97bffb860416d9b453a0b268aa69a3c
schema:url
https://doi.org/10.1016/j.scitotenv.2020.138959
resource representing a document's title
Prediction of the COVID-19 spread in African countries and implications for prevention and controls: A case study in South Africa, Egypt, Algeria, Nigeria, Senegal and Kenya
has PubMed Central identifier
PMC7182531
has PubMed identifier
32375067
schema:publication
Sci Total Environ
resource representing a document's body
covid:b2cc35b4f97bffb860416d9b453a0b268aa69a3c#body_text
is
schema:about
of
named entity 'RESULTS'
named entity 'MAXIMUM'
named entity 'INTERVENTION'
named entity 'parameter estimation'
named entity 'South Africa'
named entity 'prevention and control'
named entity 'epidemic'
named entity 'epidemic'
named entity 'Kenya'
named entity 'epidemic'
named entity 'Nigeria'
named entity 'COVID-19'
named entity 'case study'
named entity 'healthcare professionals'
named entity 'Asymptomatic'
named entity 'infection rate'
named entity 'epidemic'
named entity 'Senegal'
named entity 'virus'
named entity 'epidemic'
named entity 'time series'
named entity 'epidemic'
named entity 'probability space'
named entity 'SEIR'
named entity 'Senegal'
named entity 'quarantine'
named entity 'probability'
named entity 'Egypt'
named entity 'epidemic'
named entity 'dry season'
named entity 'SEIR'
named entity 'SEIR'
named entity 'Epidemic'
named entity 'virus'
named entity 'prevention and control'
named entity 'time series'
named entity 'Egypt'
named entity 'Algeria'
named entity 'medical supplies'
named entity 'epidemic'
named entity 'Cameroon'
named entity 'epidemic'
named entity 'prevention and control'
named entity 'parameter estimation'
named entity 'socio-economic levels'
named entity 'Africa'
named entity 'Bayesian inference'
named entity 'social and economic'
named entity 'epidemic'
named entity 'Africa'
named entity 'SEIR'
named entity 'incubation period'
named entity 'South Africa'
named entity 'parameter space'
named entity 'virus'
named entity 'epidemic'
named entity 'quarantine'
named entity 'epidemic'
named entity 'infectious disease'
named entity 'COVID-19'
named entity 'sampling frequency'
named entity 'Medical supplies'
named entity 'standard deviation'
named entity 'parameter estimation'
named entity 'South Africa'
named entity 'epidemic'
named entity 'epidemic'
named entity 'epidemic'
named entity 'epidemic'
◂◂ First
◂ Prev
Next ▸
Last ▸▸
Page 1 of 3
Go
Faceted Search & Find service v1.13.91 as of Mar 24 2020
Alternative Linked Data Documents:
Sponger
|
ODE
Content Formats:
RDF
ODATA
Microdata
About
OpenLink Virtuoso
version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software