Transactions on Machine Intelligence

Transactions on Machine Intelligence

A Review Study on the Use of Dynamic Complex Networks in Combating the COVID-19 Pandemic

Document Type : Original Article

Authors
A. M. Karamzadeh 1
N. Ghasri 2
1 Master’s Student, School of Computer Engineering and Sciences, Shahid Beheshti University, Tehran, Iran
2 PhD Student, School of Computer Engineering and Sciences, Shahid Beheshti University, Tehran, Iran
10.47176/TMI.2021.191
Abstract
The COVID-19 pandemic has profoundly affected multiple facets of society, including social interactions, financial activities, international relations, economic stability, and education. The unprecedented scale of the pandemic and the rapid transmission of the virus have necessitated multidisciplinary collaborations, bringing together researchers from diverse fields such as medicine, epidemiology, and computer science to develop predictive models and preventive strategies. Within this interdisciplinary landscape, the study of dynamic complex networks and artificial intelligence (AI) has emerged as a critical tool complementing traditional medical approaches. In the realm of computer science, network theory offers a powerful framework for modeling and simulating the spread of infectious diseases, providing valuable insights into transmission dynamics and intervention strategies. Additionally, AI-driven technological tools have been instrumental in facilitating COVID-19 control measures, including contact tracing, real-time monitoring of infected individuals, and predictive analytics for outbreak management. Many countries have leveraged network science methodologies to track viral spread and assess the broader societal impacts of the pandemic. This study systematically reviews and categorizes key research contributions in the field of dynamic complex networks applied to COVID-19. By analyzing these works, the study highlights major trends, methodological advancements, and challenges in the application of network science and AI. The findings provide a foundation for future research directions, emphasizing the potential of computational methods in enhancing pandemic preparedness and response strategies.
Keywords
Complex Networks
COVID-19
Dynamic Complex Networks
Coronavirus
Contact Tracing
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Transactions on Machine Intelligence
Volume 4, Issue 4
Autumn 2021
Pages 191-200

  • Receive Date 05 July 2021
  • Revise Date 18 September 2021
  • Accept Date 06 December 2021