An Automated Contact Tracing Approach for Controlling Covid-19 Spread Based on Geolocation Data From Mobile Cellular...

Area of Science:

• Epidemiology
• Public Health
• Mobile Technology

Background:

• The COVID-19 pandemic presents a significant global health challenge due to the virus's rapid spread and evolution.
• Traditional containment strategies like social distancing and quarantine are difficult to implement effectively in densely populated regions.
• Existing digital solutions, such as smartphone applications for contact tracing, raise privacy concerns and rely on advanced technology.

Purpose of the Study:

• To propose a new method for COVID-19 contact tracing utilizing mobile phone geolocation data.
• To enable authorities to identify potential COVID-19 cases and at-risk areas without relying on smartphone applications.
• To empower individuals with information about potential exposure for timely medical assistance.

Main Methods:

• Leveraging mobile phone users' geolocation data for contact tracing.
• Developing an algorithm to identify probable infected individuals based on location patterns.
• Analyzing numerical examples to validate the proposed method's efficacy.

Main Results:

• The proposed geolocation-based method effectively identifies probable COVID-19 infected persons.
• The system can delineate high-risk zones, aiding in targeted isolation and lockdown measures.
• Numerical simulations indicate superior performance compared to smartphone app-based solutions.

Conclusions:

• The developed method offers a viable, privacy-conscious alternative for COVID-19 contact tracing.
• Geolocation data analysis provides a scalable solution for pandemic management, especially in crowded areas.
• This approach enhances public health initiatives by improving the accuracy and accessibility of contact tracing.