Literature DB >> 32752644

Dynamic interplay between social distancing duration and intensity in reducing COVID-19 US hospitalizations: A "law of diminishing returns".

Pai Liu1, Payton Beeler1, Rajan K Chakrabarty1.   

Abstract

We uncover and highlight the importance of social distancing duration and intensity in lowering hospitalization demand-to-supply during the coronavirus disease 2019 (COVID-19) epidemic in the USA. We have developed an epidemic progression model involving the susceptible-exposed-infected-recovered dynamics, the age-stratified disease transmissibility, and the possible large-scale undocumented (i.e., asymptomatic and/or untested) transmission of COVID-19 taking place in the USA. Our analysis utilizes COVID-19 observational data in the USA between March 19 and 28, corresponding to the early stage of the epidemic when the impacts of social distancing on disease progression were yet to manifest. Calibrating our model using epidemiological data from this time period enabled us to unbiasedly address the question "How long and with what intensity does the USA need to implement social distancing intervention during the COVID-19 pandemic?" For a short (i.e., up to two weeks) duration, we find a near-linear decrease in hospital beds demand with increasing intensity (φ) of social distancing. For a duration longer than two weeks, our findings highlight the diminishing marginal benefit of social distancing, characterized by a linear decrease in medical demands against an exponentially increasing social distancing duration. Long-term implementation of strict social distancing with φ>50% could lead to the emergence of a second wave of infections due to a large residual susceptible population which highlights the need for contact tracing and isolation before re-opening of the economy. Finally, we investigate the scenario of intermittent social distancing and find an optimal social-to-no-distancing duration ratio of 5:1 corresponding to a sustainable reduction in medical demands.

Entities:  

Mesh:

Year:  2020        PMID: 32752644      PMCID: PMC7394344          DOI: 10.1063/5.0013871

Source DB:  PubMed          Journal:  Chaos        ISSN: 1054-1500            Impact factor:   3.642


  19 in total

1.  The hidden geometry of complex, network-driven contagion phenomena.

Authors:  Dirk Brockmann; Dirk Helbing
Journal:  Science       Date:  2013-12-13       Impact factor: 47.728

Review 2.  The reproductive number of COVID-19 is higher compared to SARS coronavirus.

Authors:  Ying Liu; Albert A Gayle; Annelies Wilder-Smith; Joacim Rocklöv
Journal:  J Travel Med       Date:  2020-03-13       Impact factor: 8.490

3.  A 'small-world-like' model for comparing interventions aimed at preventing and controlling influenza pandemics.

Authors:  Fabrice Carrat; Julie Luong; Hervé Lao; Anne-Violaine Sallé; Christian Lajaunie; Hans Wackernagel
Journal:  BMC Med       Date:  2006-10-23       Impact factor: 8.775

4.  Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study.

Authors:  Joseph T Wu; Kathy Leung; Gabriel M Leung
Journal:  Lancet       Date:  2020-01-31       Impact factor: 79.321

5.  Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2).

Authors:  Ruiyun Li; Sen Pei; Bin Chen; Yimeng Song; Tao Zhang; Wan Yang; Jeffrey Shaman
Journal:  Science       Date:  2020-03-16       Impact factor: 47.728

6.  An interactive web-based dashboard to track COVID-19 in real time.

Authors:  Ensheng Dong; Hongru Du; Lauren Gardner
Journal:  Lancet Infect Dis       Date:  2020-02-19       Impact factor: 25.071

7.  The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak.

Authors:  Matteo Chinazzi; Jessica T Davis; Marco Ajelli; Corrado Gioannini; Maria Litvinova; Stefano Merler; Ana Pastore Y Piontti; Kunpeng Mu; Luca Rossi; Kaiyuan Sun; Cécile Viboud; Xinyue Xiong; Hongjie Yu; M Elizabeth Halloran; Ira M Longini; Alessandro Vespignani
Journal:  Science       Date:  2020-03-06       Impact factor: 47.728

8.  Systematic selection between age and household structure for models aimed at emerging epidemic predictions.

Authors:  Lorenzo Pellis; Simon Cauchemez; Neil M Ferguson; Christophe Fraser
Journal:  Nat Commun       Date:  2020-02-14       Impact factor: 14.919

9.  Statement in support of the scientists, public health professionals, and medical professionals of China combatting COVID-19.

Authors:  Charles Calisher; Dennis Carroll; Rita Colwell; Ronald B Corley; Peter Daszak; Christian Drosten; Luis Enjuanes; Jeremy Farrar; Hume Field; Josie Golding; Alexander Gorbalenya; Bart Haagmans; James M Hughes; William B Karesh; Gerald T Keusch; Sai Kit Lam; Juan Lubroth; John S Mackenzie; Larry Madoff; Jonna Mazet; Peter Palese; Stanley Perlman; Leo Poon; Bernard Roizman; Linda Saif; Kanta Subbarao; Mike Turner
Journal:  Lancet       Date:  2020-02-19       Impact factor: 79.321

10.  Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020.

Authors:  Jantien A Backer; Don Klinkenberg; Jacco Wallinga
Journal:  Euro Surveill       Date:  2020-02
View more
  1 in total

Review 1.  Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges.

Authors:  Jinxing Guan; Yang Zhao; Yongyue Wei; Sipeng Shen; Dongfang You; Ruyang Zhang; Theis Lange; Feng Chen
Journal:  Med Rev (Berl)       Date:  2022-02-28
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.