Your browser doesn't support javascript.
Network interventions for managing the COVID-19 pandemic and sustaining economy.
Nishi, Akihiro; Dewey, George; Endo, Akira; Neman, Sophia; Iwamoto, Sage K; Ni, Michael Y; Tsugawa, Yusuke; Iosifidis, Georgios; Smith, Justin D; Young, Sean D.
  • Nishi A; Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA 90095; akihironishi@ucla.edu.
  • Dewey G; California Center for Population Research, University of California, Los Angeles, CA 90095.
  • Endo A; Bedari Kindness Institute, University of California, Los Angeles, CA 90095.
  • Neman S; Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA 90095.
  • Iwamoto SK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom.
  • Ni MY; The Alan Turing Institute, NW1 2DB London, United Kingdom.
  • Tsugawa Y; School of Medicine, Medical College of Wisconsin, Wauwatosa, WI 53213.
  • Iosifidis G; College of Letters & Science, University of California, Berkeley, CA 94720.
  • Smith JD; School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 999077, Hong Kong Special Administrative Region, China.
  • Young SD; The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, 999077, Hong Kong Special Administrative Region, China.
Proc Natl Acad Sci U S A ; 117(48): 30285-30294, 2020 12 01.
Article in English | MEDLINE | ID: covidwho-920651
ABSTRACT
Sustaining economic activities while curbing the number of new coronavirus disease 2019 (COVID-19) cases until effective vaccines or treatments become available is a major public health and policy challenge. In this paper, we use agent-based simulations of a network-based susceptible-exposed-infectious-recovered (SEIR) model to investigate two network intervention strategies for mitigating the spread of transmission while maintaining economic activities. In the simulations, we assume that people engage in group activities in multiple sectors (e.g., going to work, going to a local grocery store), where they interact with others in the same group and potentially become infected. In the first strategy, each group is divided into two subgroups (e.g., a group of customers can only go to the grocery store in the morning, while another separate group of customers can only go in the afternoon). In the second strategy, we balance the number of group members across different groups within the same sector (e.g., every grocery store has the same number of customers). The simulation results show that the dividing groups strategy substantially reduces transmission, and the joint implementation of the two strategies could effectively bring the spread of transmission under control (i.e., effective reproduction number ≈ 1.0).
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / Social Networking / COVID-19 Type of study: Experimental Studies / Observational study / Randomized controlled trials Topics: Vaccines Limits: Humans Language: English Journal: Proc Natl Acad Sci U S A Year: 2020 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / Social Networking / COVID-19 Type of study: Experimental Studies / Observational study / Randomized controlled trials Topics: Vaccines Limits: Humans Language: English Journal: Proc Natl Acad Sci U S A Year: 2020 Document Type: Article