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      Projecting hospital utilization during the COVID-19 outbreaks in the United States

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          Significance

          Our results highlight that the growing coronavirus disease 2019 (COVID-19) outbreak in the United States could gravely challenge the critical care capacity, thereby exacerbating case fatality rates. In the absence of a preventive vaccine, efforts to contain the outbreak, such as improving self-isolation rates and encouraging better hygiene practices, can alleviate some of the pressures faced by the healthcare system during an outbreak. Both emergency expansion of hospital facilities to treat COVID-19 and government appropriations to facilitate voluntary case isolation are urgently needed.

          Abstract

          In the wake of community coronavirus disease 2019 (COVID-19) transmission in the United States, there is a growing public health concern regarding the adequacy of resources to treat infected cases. Hospital beds, intensive care units (ICUs), and ventilators are vital for the treatment of patients with severe illness. To project the timing of the outbreak peak and the number of ICU beds required at peak, we simulated a COVID-19 outbreak parameterized with the US population demographics. In scenario analyses, we varied the delay from symptom onset to self-isolation, the proportion of symptomatic individuals practicing self-isolation, and the basic reproduction number R 0. Without self-isolation, when R 0 = 2.5, treatment of critically ill individuals at the outbreak peak would require 3.8 times more ICU beds than exist in the United States. Self-isolation by 20% of cases 24 h after symptom onset would delay and flatten the outbreak trajectory, reducing the number of ICU beds needed at the peak by 48.4% (interquartile range 46.4–50.3%), although still exceeding existing capacity. When R 0 = 2, twice as many ICU beds would be required at the peak of outbreak in the absence of self-isolation. In this scenario, the proportional impact of self-isolation within 24 h on reducing the peak number of ICU beds is substantially higher at 73.5% (interquartile range 71.4–75.3%). Our estimates underscore the inadequacy of critical care capacity to handle the burgeoning outbreak. Policies that encourage self-isolation, such as paid sick leave, may delay the epidemic peak, giving a window of time that could facilitate emergency mobilization to expand hospital capacity.

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          Most cited references15

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          A Novel Coronavirus from Patients with Pneumonia in China, 2019

          Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
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            Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China

            In December 2019, novel coronavirus (2019-nCoV)-infected pneumonia (NCIP) occurred in Wuhan, China. The number of cases has increased rapidly but information on the clinical characteristics of affected patients is limited.
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              Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

              Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                21 April 2020
                3 April 2020
                3 April 2020
                : 117
                : 16
                : 9122-9126
                Affiliations
                [1] aAgent-Based Modelling Laboratory, York University , Toronto, ON M3J 1P3, Canada;
                [2] bCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health , New Haven, CT 06510;
                [3] cCenter for Vaccine Development and Global Health, University of Maryland School of Medicine , Baltimore, MD 21201;
                [4] dCenter for Sustainable Development at Columbia University, Columbia University , New York, NY 10032;
                [5] eDepartment of Biostatistics, Yale School of Public Health , New Haven, CT 06510;
                [6] fDepartment of Integrative Biology, The University of Texas at Austin , Austin, TX 78712;
                [7] gEmerging Pathogens Institute, University of Florida, Gainesville, FL 32610
                Author notes
                1To whom correspondence may be addressed. Email: bhsinger@ 123456epi.ufl.edu .

                Contributed by Burton H. Singer, March 19, 2020 (sent for review March 4, 2020; reviewed by Sandip Mandal and Navideh Noori)

                Author contributions: S.M.M., A.S., B.H.S., and A.P.G. designed research; S.M.M., A.S., M.C.F., C.R.W., P.S., A.P., J.D.S., Z.W., L.A.M., B.H.S., and A.P.G. performed research; S.M.M., A.S., M.C.F., C.R.W., A.P., J.D.S., Z.W., L.A.M., and A.P.G. analyzed data; and S.M.M., A.S., M.C.F., P.S., J.D.S., B.H.S., and A.P.G. wrote the paper.

                Reviewers: S.M., Indian Council of Medical Research; and N.N., Institute for Disease Modeling.

                Author information
                http://orcid.org/0000-0003-1370-8350
                http://orcid.org/0000-0002-8849-8549
                Article
                202004064
                10.1073/pnas.2004064117
                7183199
                32245814
                8953318f-2105-48ce-919a-c9df5e80093d
                Copyright © 2020 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                Page count
                Pages: 5
                Funding
                Funded by: National Institutes of Health
                Award ID: UO1-GM087719
                Award Recipient : Seyed M. Moghadas Award Recipient : Meagan C Fitzpatrick Award Recipient : Alison P. Galvani
                Funded by: Burnett & Stender Families Endowment
                Award ID: Endowment funds
                Award Recipient : Seyed M. Moghadas Award Recipient : Meagan C Fitzpatrick Award Recipient : Alison P. Galvani
                Funded by: Notsew Orm Sands Foundation
                Award ID: Endowment funds
                Award Recipient : Seyed M. Moghadas Award Recipient : Meagan C Fitzpatrick Award Recipient : Alison P. Galvani
                Funded by: National Institutes of Health
                Award ID: 1RO1AI151176-01
                Award Recipient : Seyed M. Moghadas Award Recipient : Meagan C Fitzpatrick Award Recipient : Alison P. Galvani
                Funded by: National Institutes of Health
                Award ID: KO1AI141576
                Award Recipient : Seyed M. Moghadas Award Recipient : Meagan C Fitzpatrick Award Recipient : Alison P. Galvani
                Funded by: Canada Institute of Health Research
                Award ID: DC0190GP
                Award Recipient : Seyed M. Moghadas Award Recipient : Meagan C Fitzpatrick Award Recipient : Alison P. Galvani
                Categories
                530
                Biological Sciences
                Population Biology
                From the Cover
                Custom metadata
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                sars–cov-2,hospitalization,self-isolation,critical care need

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