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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/122| Forecasting the Worldwide Spread of COVID-19 based on Logistic Model and SEIR Model | |
| Weiguo Zhu Xiang Zhou Yun Long Na Hong Yingying Ma Jie He Huizhen Jiang Chun Liu Guangliang Shan Longxiang Su | |
| Novel Coronavirus | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| 10.1101/2020.03.26.20044289 | |
| Background: As the outbreak of coronavirus disease 2019 (COVID-19), a sudden case increase in late February 2020 in global attracted deep concern. Italy, South Korea, Iran, France, Germany, Spain, the U.S. and Japan are probable the most severe countries. Collecting epidemiological data and predicting epidemic trends are important to develop and measure public intervention strategies. Epidemic predictions results yield by different mathematical models are out of line, therefore, we sought to compare different models and their prediction results, so as to generate objective conclusions. Methods: We used the number of cases reported from January 23 to March 20, 2020 to estimate possible spread size and peak time of COVID-19, especially in 8 high risk countries. Logistic growth model, basic SEIR model and adjusted SEIR model were adopted for predicting. Considering different model inputs may infer different model outputs, we implemented three model predictions with three scenarios of epidemic development. Results: When contrasting all 8 countries short-term prediction results and peak predictions, the difference between the models was relatively large. The logistic growth model estimated a smaller epidemic size than the basic SERI model, however, once we added parameters which considered the effects of public health interventions and control measures, the adjusted SERI model results demonstrated a considerably rapid decelerate of the epidemic development. Our results demonstrated contact rate, quarantine scale, quarantine initiate time and length are important factors to control the epidemic size and length. Conclusions: We demonstrated a comparative assessment of the predictions of COVID-19 outbreak of 8 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 understand the transmission dynamics of COVID-19 and recommend operation suggestions to slow down the epidemic. It is suggested that quickly detecting cases, enough quarantine implementation and public self-protection behaviors are critical to slow down the epidemic. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial NA ### Funding Statement None. ### Author Declarations All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Contact Prof. Yun Long or Prof. Weiguo Zhu to access the data. | |
| Cold Spring Harbor Laboratory Press | |
| 2020 | |
| Preimpreso | |
| https://www.medrxiv.org/content/10.1101/2020.03.26.20044289v1 | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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