About: Forecasting the Worldwide Spread of COVID-19 based on Logistic Model and SEIR Model

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Forecasting the Worldwide Spread of COVID-19 based on Logistic Model and SEIR Model
Creator	He, Jie Zhou, Xiang Ma, Yingying Zhu, Weiguo Hong, Na Jiang, Huizhen Liu, Chun Long, Yun Shan, Guangliang Su, Longxiang Zhang, Shuyang Ma, Xudong
Source	MedRxiv
abstract	Background: With the outbreak of coronavirus disease 2019 (COVID-19), a sudden case increase in late February 2020 led to deep concern globally. Italy, South Korea, Iran, France, Germany, Spain, the US and Japan are probably the countries with the most severe outbreaks. Collecting epidemiological data and predicting epidemic trends are important for the development and measurement of public intervention strategies. Epidemic prediction results yielded by different mathematical models are inconsistent; therefore, we sought to compare different models and their prediction results to generate objective conclusions. Methods: We used the number of cases reported from January 23 to March 20, 2020, to estimate the possible spread size and peak time of COVID-19, especially in 8 high-risk countries. The logistic growth model, basic SEIR model and adjusted SEIR model were adopted for prediction. Given that different model inputs may infer different model outputs, we implemented three model predictions with three scenarios of epidemic development. Results: When comparing all 8 countries short-term prediction results and peak predictions, the differences among the models were relatively large. The logistic growth model estimated a smaller epidemic size than the basic SERI model did; however, once we added parameters that considered the effects of public health interventions and control measures, the adjusted SERI model results demonstrated a considerably rapid deceleration of epidemic development. Our results demonstrated that contact rate, quarantine scale, and the initial quarantine time and length are important factors in controlling epidemic size and length. Conclusions: We demonstrated a comparative assessment of the predictions of the COVID-19 outbreak in eight high-risk countries using multiple methods. By forecasting epidemic size and peak time as well as simulating the effects of public health interventions, the intent of this paper is to help clarify the transmission dynamics of COVID-19 and recommend operation suggestions to slow down the epidemic. It is suggested that the quick detection of cases, sufficient implementation of quarantine and public self-protection behaviors are critical to slow down the epidemic.
has issue date	2020-03-30(xsd:dateTime)
bibo:doi	10.1101/2020.03.26.20044289
has license	medrxiv
sha1sum (hex)	0185f63fd6ecdf04829a155f4d7f62a5b532f06d
schema:url	https://doi.org/10.1101/2020.03.26.20044289
resource representing a document's title	Forecasting the Worldwide Spread of COVID-19 based on Logistic Model and SEIR Model
resource representing a document's body	covid:0185f63fd6ecdf04829a155f4d7f62a5b532f06d#body_text
is schema:about of	named entity 'COVID-19' named entity 'Japan' named entity 'COVID-19' named entity 'Logistic Model' named entity 'With' named entity 'logistic model' named entity 'evolution' named entity 'France' named entity 'infection' named entity 'infection' named entity 'epidemic' named entity 'infection' named entity 'high-risk' named entity 'COVID-19' named entity 'curve fitting' named entity 'preprint' named entity 'preprint' named entity 'North America' named entity 'SEIR' named entity 'medRxiv' named entity 'logistic model' named entity 'Italy' named entity 'SEIR' named entity 'SEIR' named entity 'preprint' named entity 'SEIR' named entity 'Asia' named entity 'evolution' named entity 'SEIR' named entity 'logistic growth' named entity 'logistic curve' named entity 'testing kits' named entity 'Johns Hopkins University' named entity 'preprint' named entity 'medRxiv' named entity 'parameter estimation' named entity 'COVID' named entity 'quarantine' named entity 'quarantine' named entity 'peer-reviewed' named entity 'viruses' named entity 'contact tracing' named entity 'preprint' named entity 'quarantine' named entity 'Iran' named entity '28 days' named entity 'epidemic' named entity 'social distance' named entity 'epidemic' named entity 'preprint' named entity 'Monte Carlo simulation' named entity 'SEIR' named entity 'logistic function' named entity 'Coronavirus' named entity 'Hubei Province' named entity 'SEIR' named entity 'coronavirus disease 2019' named entity 'logistic model' named entity 'public health' named entity 'vaccine' named entity 'preprint' named entity 'coronavirus' named entity 'COVID-19' named entity 'healthcare policy' named entity 'COVID-19' named entity 'epidemic' named entity 'quarantine' named entity 'historical data' named entity 'increasing cases' named entity 'population immunity' named entity 'Japan' named entity 'SEIR' named entity 'mortality rate' named entity 'epidemic'

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