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Hybrid harmony search algorithm for social network contact tracing of COVID-19

Ala'a Al-Shaikh1, Basel A Mahafzah2, Mohammad Alshraideh2

  • 1Learning and Teaching Technology Center, Al-Balqa Applied University, Al-Salt, 19117 Jordan.

Soft Computing
|July 5, 2021
PubMed

Insights

A novel Hybrid Harmony Search Contact Tracing (HHS-CT) algorithm improves COVID-19 contact tracing efficiency. This method enhances finding strongly connected components, crucial for tracking infections and saving lives.

Area of Science:

  • Computer Science
  • Infectious Disease Modeling
  • Algorithm Development

Background:

  • The COVID-19 pandemic highlighted the need for efficient public health interventions.
  • Contact tracing is vital for controlling infectious disease spread.
  • Existing methods for identifying transmission pathways can be computationally intensive.

Purpose of the Study:

  • To develop a novel algorithm for efficient contact tracing.
  • To apply graph theory concepts, specifically finding strongly connected components (SCCs), to contact tracing.
  • To enhance the performance of contact tracing through an optimized computational approach.

Main Methods:

  • A Hybrid Harmony Search (HHS) algorithm was developed, termed HHS-CT.
  • The HHS-CT algorithm models contact tracing as a problem of finding SCCs in directed graphs.
  • Stochastic hill climbing was integrated into the HHS algorithm's operators for improved performance.

Main Results:

  • The HHS-CT algorithm demonstrated superior performance compared to existing SCC algorithms.
  • Achieved a 77.18% enhancement in run time.
  • Reported an exceptional average error rate of 1.7%.

Conclusions:

  • The HHS-CT algorithm offers a significant advancement in computational efficiency for contact tracing.
  • This approach provides a faster and more accurate method for identifying infection chains.
  • The findings suggest potential for improved pandemic response strategies through advanced algorithms.

Keywords:
COVID-19Contact tracingCoronavirusHarmony search algorithmHill climbingMetaheuristic approachSocial networks

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