About: Estimating the size of COVID-19 epidemic outbreak

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Estimating the size of COVID-19 epidemic outbreak
Creator	Channuie, Phongpichit Dam-O, Punsiri Koomhin, Phanit Payaka, Apirak Pimton, Pimchanok Pongkitivanichkul, Chakrit Samart, Daris Tangphati, Takol
Source	MedRxiv
abstract	In this work, we analyze the epidemic data of cumulative infected cases collected from many countries as reported by WHO starting from January 21st,2020 and up till March 21st,2020. Our inspection is motivated by the renormalization group (RG) framework. Here we propose the RG-inspired logistic function of the form αE(t) = a(1 + ec(t-t0))-n as an epidemic strength function with n being asymmetry in the modified logistic function. We perform the non-linear least-squares analysis with data from various countries. The uncertainty for model parameters is computed using the squared root of the corresponding diagonal components of the covariance matrix. We carefully divide countries under consideration into 2 categories based on the estimation of the inflection point: the maturing phase and the growth-dominated phase. We observe that long-term estimations of cumulative infected cases of countries in the maturing phase for both n=1 and n≠ 1 are close to each other. We find from the value of root mean squared error (RMSE) that the RG-inspired logistic model with n≠ 1 is slightly preferable in this category. We also argue that n determines the characteristic of the epidemic at an early stage. However, in the second category, the estimated asymptotic number of cumulative infected cases contain rather large uncertainty. Therefore, in the growth-dominated phase, we focus on using n≠ 1 for countries in this phase. Some of them are in an early stage of an epidemic with an insufficient amount of data leading to a large uncertainty on parameter fits. In terms of the accuracy of the size estimation, the results do strongly depend on limitations on data collection and the epidemic phase for each country.
has issue date	2020-03-31(xsd:dateTime)
bibo:doi	10.1101/2020.03.28.20044339
has license	medrxiv
sha1sum (hex)	98e5d8963ec99952da4e0888a64546e052bd661f
schema:url	https://doi.org/10.1101/2020.03.28.20044339
resource representing a document's title	Estimating the size of COVID-19 epidemic outbreak
resource representing a document's body	covid:98e5d8963ec99952da4e0888a64546e052bd661f#body_text
is schema:about of	named entity 'cumulative' named entity 'large' named entity 'inflection point' named entity 'epidemic' named entity 'COUNTRIES' named entity 'SECOND' named entity 'ROOT' named entity 'SIZE' named entity 'CHARACTERISTIC' named entity 'COUNTRY' covid:arg/98e5d8963ec99952da4e0888a64546e052bd661f named entity 'cases' named entity 'uncertainty' named entity 'renormalization group' named entity 'cumulative' named entity 'cases' named entity 'estimated' named entity 'Therefore' named entity 'leading' named entity 'logistic function' named entity 'inflection point' named entity 'WHO' named entity 'data collection' named entity 'covariance matrix' named entity 'epidemic' named entity 'Austria' named entity 'fixed points' named entity 'logistic function' named entity 'attractor' named entity 'epidemic' named entity 'S-curve' named entity 'material science' named entity 'epidemic' named entity 'epidemic' named entity 'epidemic' named entity 'COVID-19' named entity 'epidemic' named entity 'virus' named entity 'high energy physics' named entity 'non-linear least squares' named entity 'evolution' named entity 'beta function' named entity 'peer review' named entity 'beta function' named entity 'high energy physics' named entity 'epidemic' named entity 'epidemic' named entity 'Japan' named entity 'phase transitions' named entity 'linguistics' named entity 'energy scale' named entity 'inflection point' named entity 'Korea' named entity 'inflection point' named entity 'COVID-19' named entity 'machine learning' named entity 'epidemic' named entity 'inflection point' named entity 'Spain' named entity 'Japan' named entity 'fixed point' named entity 'fixed points' named entity 'China' named entity 'epidemic' named entity 'epidemic' named entity 'Japan' named entity 'epidemic' named entity 'Korea' named entity 'renormalisation group' named entity 'World Health Organization' named entity 'Italy' named entity 'SARS-CoV-2' named entity 'energy scale' named entity 'COVID-19' named entity 'peer review' named entity 'China' named entity 'root mean squared error' named entity 'epidemic'

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