Literature DB >> 32511467

Facing the COVID-19 epidemic in NYC: a stochastic agent-based model of various intervention strategies.

Nicolas Hoertel1,2, Martin Blachier3, Carlos Blanco4, Mark Olfson5, Marc Massetti3, Frédéric Limosin1,2, Henri Leleu3.   

Abstract

Global spread of coronavirus disease 2019 (COVID-19) has created an unprecedented infectious disease crisis worldwide. Despite uncertainties about COVID-19, model-based forecasting of competing mitigation measures on its course is urgently needed to inform mitigation policy. We used a stochastic agent-based microsimulation model of the COVID-19 epidemic in New York City and evaluated the potential impact of quarantine duration (from 4 to 16 weeks), quarantine lifting type (1-step lifting for all individuals versus a 2-step lifting according to age), post-quarantine screening, and use of a hypothetical effective treatment against COVID-19 on the disease's cumulative incidence and mortality, and on ICU-bed occupancy. The source code of the model has been deposited in a public source code repository (GitHub®). The model calibrated well and variation of model parameter values had little impact on outcome estimates. While quarantine is efficient to contain the viral spread, it is unlikely to prevent a rebound of the epidemic once lifted. We projected that lifting quarantine in a single step for the full population would be unlikely to substantially lower the cumulative mortality, regardless of quarantine duration. By contrast, a two-step quarantine lifting according to age was associated with a substantially lower cumulative mortality and incidence, up to 71% and 23%, respectively, as well as lower ICU-bed occupancy. Although post-quarantine screening was associated with diminished epidemic rebound, this strategy may not prevent ICUs from being overcrowded. It may even become deleterious after a 2-step quarantine lifting according to age if the herd immunity effect does not had sufficient time to become established in the younger population when the quarantine is lifted for the older population. An effective treatment against COVID-19 would considerably reduce the consequences of the epidemic, even more so if ICU capacity is not exceeded.

Entities:  

Keywords:  COVID-19; ICU-bed occupancy; New York; SARS-CoV-2; United States; incidence; lifting; mortality; quarantine; screening; treatment

Year:  2020        PMID: 32511467      PMCID: PMC7255787          DOI: 10.1101/2020.04.23.20076885

Source DB:  PubMed          Journal:  medRxiv


  26 in total

Review 1.  Large-scale spatial-transmission models of infectious disease.

Authors:  Steven Riley
Journal:  Science       Date:  2007-06-01       Impact factor: 47.728

2.  Predictive Mathematical Models of the COVID-19 Pandemic: Underlying Principles and Value of Projections.

Authors:  Nicholas P Jewell; Joseph A Lewnard; Britta L Jewell
Journal:  JAMA       Date:  2020-05-19       Impact factor: 56.272

3.  Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models.

Authors:  Marco Ajelli; Bruno Gonçalves; Duygu Balcan; Vittoria Colizza; Hao Hu; José J Ramasco; Stefano Merler; Alessandro Vespignani
Journal:  BMC Infect Dis       Date:  2010-06-29       Impact factor: 3.090

4.  Mitigation strategies for pandemic influenza A: balancing conflicting policy objectives.

Authors:  T Déirdre Hollingsworth; Don Klinkenberg; Hans Heesterbeek; Roy M Anderson
Journal:  PLoS Comput Biol       Date:  2011-02-10       Impact factor: 4.475

5.  Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases.

Authors:  Tao Ai; Zhenlu Yang; Hongyan Hou; Chenao Zhan; Chong Chen; Wenzhi Lv; Qian Tao; Ziyong Sun; Liming Xia
Journal:  Radiology       Date:  2020-02-26       Impact factor: 11.105

6.  COVID-19 and smoking: A systematic review of the evidence.

Authors:  Constantine I Vardavas; Katerina Nikitara
Journal:  Tob Induc Dis       Date:  2020-03-20       Impact factor: 2.600

7.  The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study.

Authors:  Kiesha Prem; Yang Liu; Timothy W Russell; Adam J Kucharski; Rosalind M Eggo; Nicholas Davies; Mark Jit; Petra Klepac
Journal:  Lancet Public Health       Date:  2020-03-25

8.  The effectiveness of quarantine and isolation determine the trend of the COVID-19 epidemics in the final phase of the current outbreak in China.

Authors:  Biao Tang; Fan Xia; Sanyi Tang; Nicola Luigi Bragazzi; Qian Li; Xiaodan Sun; Juhua Liang; Yanni Xiao; Jianhong Wu
Journal:  Int J Infect Dis       Date:  2020-04-17       Impact factor: 3.623

9.  Early dynamics of transmission and control of COVID-19: a mathematical modelling study.

Authors:  Adam J Kucharski; Timothy W Russell; Charlie Diamond; Yang Liu; John Edmunds; Sebastian Funk; Rosalind M Eggo
Journal:  Lancet Infect Dis       Date:  2020-03-11       Impact factor: 25.071

10.  Epidemiological data from the COVID-19 outbreak, real-time case information.

Authors:  Bo Xu; Bernardo Gutierrez; Sumiko Mekaru; Kara Sewalk; Lauren Goodwin; Alyssa Loskill; Emily L Cohn; Yulin Hswen; Sarah C Hill; Maria M Cobo; Alexander E Zarebski; Sabrina Li; Chieh-Hsi Wu; Erin Hulland; Julia D Morgan; Lin Wang; Katelynn O'Brien; Samuel V Scarpino; John S Brownstein; Oliver G Pybus; David M Pigott; Moritz U G Kraemer
Journal:  Sci Data       Date:  2020-03-24       Impact factor: 6.444

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