Modeling COVID-19 spread using multi-agent simulation with small-world network approach.

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  • Author(s): Fan Q;Fan Q; Li Q; Li Q; Chen Y; Chen Y; Chen Y; Tang J; Tang J
  • Source:
    BMC public health [BMC Public Health] 2024 Mar 02; Vol. 24 (1), pp. 672. Date of Electronic Publication: 2024 Mar 02.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: BioMed Central Country of Publication: England NLM ID: 100968562 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2458 (Electronic) Linking ISSN: 14712458 NLM ISO Abbreviation: BMC Public Health Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : BioMed Central, [2001-
    • Subject Terms:
      COVID-19*/epidemiology ; Epidemics*; Humans ; SARS-CoV-2 ; Communicable Disease Control/methods ; Computer Simulation
    • Abstract:
      Background: The rapid global spread of COVID-19 has seriously impacted people's daily lives and the social economy while also posing a threat to their lives. The analysis of infectious disease transmission is of significant importance for the rational allocation of epidemic prevention and control resources, the management of public health emergencies, and the improvement of future public health systems.
      Methods: We propose a spatiotemporal COVID-19 transmission model with a neighborhood as an agent unit and an urban spatial network with long and short edge connections. The spreading model includes a network of defined agent attributes, transformation rules, and social relations and a small world network representing agents' social relations. Parameters for each stage are fitted by the Runge-Kutta method combined with the SEIR model. Using the NetLogo development platform, accurate dynamic simulations of the spatial and temporal evolution of the early epidemic were achieved.
      Results: Experimental results demonstrate that the fitted curves from the four stages agree with actual data, with only a 12.27% difference between the average number of infected agents and the actual number of infected agents after simulating 1 hundred times. Additionally, the model simulates and compares different "city closure" scenarios. The results showed that implementing a 'lockdown' 10 days earlier would lead to the peak number of infections occurring 7 days earlier than in the normal scenario, with a reduction of 40.35% in the total number of infections.
      Discussion: Our methodology emphasizes the crucial role of timely epidemic interventions in curbing the spread of infectious diseases, notably in the predictive assessment and evaluation of lockdown strategies. Furthermore, this approach adeptly forecasts the influence of varying intervention timings on peak infection rates and total case numbers, accurately reflecting real-world virus transmission patterns. This highlights the importance of proactive measures in diminishing epidemic impacts. It furnishes a robust framework, empowering policymakers to refine epidemic response strategies based on a synthesis of predictive modeling and empirical data.
      (© 2024. The Author(s).)
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    • Contributed Indexing:
      Keywords: Agent; COVID-19; Small world networks; Social relationship; Spatiotemporal propagation
    • Publication Date:
      Date Created: 20240302 Date Completed: 20240305 Latest Revision: 20240306
    • Publication Date:
      20240306
    • Accession Number:
      PMC10909264
    • Accession Number:
      10.1186/s12889-024-18157-x
    • Accession Number:
      38431581