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

An Automated Contact Tracing Approach for Controlling Covid-19 Spread Based on Geolocation Data From Mobile Cellular Networks. Rahman, M. T., Khan, R. T., Khandaker, M. R. A., Sellathurai, M., & Salan, M. S. A. IEEE Access, 8:213554–213565, 2020. Conference Name: IEEE Access
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The coronavirus (COVID-19) has appeared as the greatest challenge due to its continuous structural evolution as well as the absence of proper antidotes for this particular virus. The virus mainly spreads and replicates itself among mass people through close contact which unfortunately can happen in many unpredictable ways. Therefore, to slow down the spread of this novel virus, the only relevant initiatives are to maintain social distance, perform contact tracing, use proper safety gears, and impose quarantine measures. But despite being conceptually possible, these approaches are very difficult to uphold in densely populated countries and areas. Therefore, to control the virus spread, researchers and authorities are considering the use of smartphone based mobile applications (apps) to identify the likely infected persons as well as the highly risky zones to maintain isolation and lockdown measures. However, these methods heavily depend on advanced technological features and expose significant privacy loopholes. In this article, we propose a new method for COVID-19 contact tracing based on mobile phone users' geolocation data. The proposed method will help the authorities to identify the number of probable infected persons without using smartphone based mobile applications. In addition, the proposed method can help people take the vital decision of when to seek medical assistance by letting them know whether they are already in the list of exposed persons. Numerical examples demonstrate that the proposed method can significantly outperform the smartphone app-based solutions.

@article{rahman_automated_2020,
	title = {An {Automated} {Contact} {Tracing} {Approach} for {Controlling} {Covid}-19 {Spread} {Based} on {Geolocation} {Data} {From} {Mobile} {Cellular} {Networks}},
	volume = {8},
	issn = {2169-3536},
	doi = {10.1109/ACCESS.2020.3040198},
	abstract = {The coronavirus (COVID-19) has appeared as the greatest challenge due to its continuous structural evolution as well as the absence of proper antidotes for this particular virus. The virus mainly spreads and replicates itself among mass people through close contact which unfortunately can happen in many unpredictable ways. Therefore, to slow down the spread of this novel virus, the only relevant initiatives are to maintain social distance, perform contact tracing, use proper safety gears, and impose quarantine measures. But despite being conceptually possible, these approaches are very difficult to uphold in densely populated countries and areas. Therefore, to control the virus spread, researchers and authorities are considering the use of smartphone based mobile applications (apps) to identify the likely infected persons as well as the highly risky zones to maintain isolation and lockdown measures. However, these methods heavily depend on advanced technological features and expose significant privacy loopholes. In this article, we propose a new method for COVID-19 contact tracing based on mobile phone users' geolocation data. The proposed method will help the authorities to identify the number of probable infected persons without using smartphone based mobile applications. In addition, the proposed method can help people take the vital decision of when to seek medical assistance by letting them know whether they are already in the list of exposed persons. Numerical examples demonstrate that the proposed method can significantly outperform the smartphone app-based solutions.},
	journal = {IEEE Access},
	author = {Rahman, Md. Tanvir and Khan, Risala T. and Khandaker, Muhammad R. A. and Sellathurai, Mathini and Salan, Md. Sifat A.},
	year = {2020},
	note = {Conference Name: IEEE Access},
	keywords = {Bluetooth, COVID-19, Coronavirus, Covid-19, Government, Mobile handsets, Pandemics, Privacy, Viruses (medical), contact tracing, geolocation, pandemic},
	pages = {213554--213565},
}

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