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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/125| A data-driven tool for tracking and predicting the course of COVID-19 epidemic as it evolves | |
| Norden E Huang Fangli Qiao Ka-Kit Tung | |
| Novel Coronavirus | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| 10.1101/2020.03.28.20046177 | |
| For an emergent disease, such as Covid-19, with no past epidemiological data to guide models, modelers struggle to make predictions of the course of the epidemic (1). Policy decisions depend on such predictions but they vary widely. On the other hand much empirical information is already contained in the data of evolving epidemiological profiles. We show, both with evidence from data, and theoretically, how the ratio of daily infected and recovered cases can be used to track and predict the course of the epidemic. Ability to predict the turning points and the end of the epidemic is of crucial importance for fighting the epidemic and planning for a return to normalcy. The accuracy of the prediction of the peaks of the epidemic is validated using data in different regions in China showing the effects of different levels of quarantine. The validated tool can be applied to other countries where Covid-19 has spread, and generally to future epidemics. A preliminary prediction for South Korea is made with limited data, with end of the epidemic as early as the second week of April, surprisingly. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement No funding information. ### 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 Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.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 All data used in this study are publicly available. | |
| Cold Spring Harbor Laboratory Press | |
| 2020 | |
| Preimpreso | |
| https://www.medrxiv.org/content/10.1101/2020.03.28.20046177v1 | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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| Herd inmmunity vs suppressed.pdf | 8.4 MB | Adobe PDF | Visualizar/Abrir |