Please use this identifier to cite or link to this item: https://covid-19.conacyt.mx/jspui/handle/1000/94
Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning
Raj Dandekar
George Barbastathis
Novel Coronavirus
Acceso Abierto
Atribución-NoComercial-SinDerivadas
10.1101/2020.04.03.20052084
Since the first recording of what we now call Covid-19 infection in Wuhan, Hubei province, China on Dec 31, 2019, the disease has spread worldwide and met with a wide variety of social distancing and quarantine policies. The effectiveness of these responses is notoriously difficult to quantify as individuals travel, violate policies deliberately or inadvertently, and infect others without themselves being detected. Moreover, the publicly available data on infection rates are themselves unreliable due to limited testing and even possibly under-reporting. In this paper, we attempt to interpret and extrapolate from publicly available data using a mixed first-principles epidemiological equations and data-driven neural network model. Leveraging our neural network augmented model, we focus our analysis on four locales: Wuhan, Italy, South Korea and the United States of America, and compare the role played by the quarantine and isolation measures in each of these countries in controlling the effective reproduction number Rt of the virus. Our results unequivocally indicate that the countries in which rapid government interventions and strict public health measures for quarantine and isolation were implemented were successful in halting the spread of infection and prevent it from exploding exponentially. In the case of Wuhan especially, where the available data were earliest available, we have been able to test the predicting ability of our model by training it from data in the January 24 till March 3 window, and then matching the predictions up to April 1. Even for Italy and South Korea, we have a buffer window of one week (25 March - 1 April) to validate the predictions of our model. In the case of the US, our model captures well the current infected curve growth and predicts a halting of infection spread by 20 April 2020. We further demonstrate that relaxing or reversing quarantine measures right now will lead to an exponential explosion in the infected case count, thus nullifying the role played by all measures implemented in the US since mid March 2020. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This effort was partially funded by the Intelligence Advanced Reseach Projects Activity (IARPA.) ### 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 for the infected and recovered case count in Wuhan is obtained from the data released by the Chinese National Health Commission. Infected and recovered count data for Italy, South Korea and USA is obtained from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University.
Cold Spring Harbor Laboratory Press
2020
Preimpreso
https://www.medrxiv.org/content/10.1101/2020.04.03.20052084v1
Inglés
VIRUS RESPIRATORIOS
Appears in Collections:Artículos científicos

Upload archives


File SizeFormat 
Quantifying the effect.pdf782.7 kBAdobe PDFView/Open