Por favor, use este identificador para citar o enlazar este ítem:
http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/95| Modeling the COVID-19 outbreaks and the effectiveness of the containment measures adopted across countries | |
| Edward De Brouwer Daniele Raimondi Yves Moreau | |
| Novel Coronavirus | |
| Acceso Abierto | |
| Atribución-NoComercial | |
| 10.1101/2020.04.02.20046375 | |
| On March 11, 2020, the World Health Organization declared the COVID-19 outbreak, originally started in China, a global pandemic. Since then, the outbreak has indeed spread across all continents, threatening the public health of numerous countries. Although the Case Fatality Rate (CFR) of COVID-19 is relatively low when optimal level of healthcare is granted to the patients, the high percentage of severe cases developing severe pneumonia and thus requiring respiratory support is worryingly high, and could lead to a rapid saturation of Intensive Care Units (ICUs). To overcome this risk, most countries enacted COVID-19 containment measures. In this study, we use a Bayesian SEIR epidemiological model to perform a parametric regression over the COVID-19 outbreaks data in China, Italy, Belgium, and Spain, and estimate the effect of the containment measures on the basic reproduction ratio R_0. We find that the effect of these measures is detectable, but tends to be gradual, and that a progressive strengthening of these measures usually reduces the R_0 below 1, granting a decay of the outbreak. We also discuss the biases and inconsistencies present in the publicly available data on COVID-19 cases, providing an estimate for the actual number of cases in Italy on March 12, 2020. Lastly, despite the data and model's limitations, we argue that the idea of "flattening the curve" is likely to be unfeasible. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement YM is funded by Research Council KU Leuven: C14/18/092 SymBioSys3; CELSA-HIDUCTION CELSA/17/032 Flemish Government:IWT: Exaptation, PhD grants FWO 06260 (Iterative and multi-level methods for Bayesian multirelational factorization with features) This research received funding from the Flemish Government under the "Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen" programme. EU: "MELLODDY" This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 831472. This Joint Undertaking receives support from the European Unions Horizon 2020 research and innovation programme and EFPIA. DR is funded by a FWO postdoctoral fellowship and EDB is funded by a FWO-SB grant. ### 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 Data is publicly available | |
| Cold Spring Harbor Laboratory Press | |
| 2020 | |
| Preimpreso | |
| https://www.medrxiv.org/content/10.1101/2020.04.02.20046375v1 | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
Cargar archivos:
| Fichero | Tamaño | Formato | |
|---|---|---|---|
| Modeling the covid.pdf | 1.55 MB | Adobe PDF | Visualizar/Abrir |