Page 109 - ITU Journal, Future and evolving technologies - Volume 1 (2020), Issue 1, Inaugural issue

ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1

A BLUEPRINT FOR EFFECTIVE PANDEMIC MITIGATION

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Rahul Singh , Wenbo Ren , Fang Liu , Dong Xuan , Zhiqiang Lin , Ness B. Shroff 6
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1 Department of ECE, Indian Institute of Science, Bangalore, CSE Department, The Ohio State University, ECE
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Department, The Ohio State University, CSE Department, The Ohio State University, CSE Department, The Ohio
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State University, ECE and CSE Departments, The Ohio State University,
NOTE: Corresponding author: Ness B. Shroff, shroff.11@osu.edu
Abstract – Traditional methods for mitigating pandemics employ a dual strategy of contact tracing plus testing
combined with quarantining and isolation. The contact tracing aspect is usually done via manual (human) contact
tracers, which are labor-intensive and expensive. In many large-scale pandemics (e.g., COVID-19), testing capacity is
resource limited, and current myopic testing strategies are resource wasteful. To address these challenges, in this work,
we provide a blueprint on how to contain the spread of a pandemic by leveraging wireless technologies and advances
in sequential learning for efficiently using testing resources in order to mitigate the spread of a large-scale pandemic.
We study how different wireless technologies could be leveraged to improve contact tracing and reduce the probabilities
of detection and false alarms. The idea is to integrate different streams of data in order to create a susceptibility graph
whose nodes correspond to an individual and whose links correspond to spreading probabilities. We then show how
to develop efficient sequential learning based algorithms in order to minimize the spread of the virus infection. In
particular, we show that current contact tracing plus testing strategies that are aimed at identifying (and testing)
individuals with the highest probability of infection are inefficient. Rather, we argue that in a resource constrained
testing environment, it is instead better to test those individuals whose expected impact on virus spread is the highest.
We rigorously formulate the resource constrained testing problem as a sequential learning problem and provide efficient
algorithms to solve it. We also provide numerical results that show the efficacy of our testing strategy.
Keywords – contact tracing, COVID-19, selective testing

1. INTRODUCTION approach, relying on a human being’s memory. Such
an approach cannot scale to large and rapidly moved
The outbreak of COVID-19 has unfolded an unprece- populations today. Meanwhile, manual tracing may
dented worldwide health, economical, and social crisis. result in delays, which could limit its utility. There-
Today, COVID-19 has spread to 188 countries, infected fore, recently numerous digital contact tracing systems
nearly 30 million people globally, and resulted in close to have been developed and deployed across the globe, by
one million deaths. The International Monetary Fund using a wide variety of sources to track “encounters”
(IMF) has predicted that the global economy will shrink including CCTV footage, records of credit card trans-
by 3% this year, the worst decline since the Great De- actions [1], locations measured using cellular networks
pression of the 1930s [2]. Today, millions of workers have or WiFi hotspots [26], locations via GPS, and crypto-
been laid off, and the tourism or hospitality industry has graphic tokens exchanged via Bluetooth Low Energy
been hurt particularly hard. (BLE) or acoustic channels [15]. For a recent survey
The COVID-19 outbreak and the mixed successes that of works on contact tracing and privacy-aware contact
nations have had in controlling the virus has under- tracing, see [9, 11, 10, 23, 24, 28, 7, 19]. Also see [14, 25]
scored the need for the development of technological for some interesting recent works in this area.
tools for pandemic mitigation. This paper provides a
blueprint on how technologies should be used in con- However, currently contact tracing system appears to be
junction with smart testing techniques in order to con- uncoordinated individual efforts focused on individual
tain a pandemic such as COVID-19. The first step for technologies and one type of data stream (e.g., camera
based systems, phone apps, etc.). Further, a number of
pandemic mitigation is to identify or trace the close con- these solutions suffer from a variety of technological lim-
tacts who might have been exposed to the disease from itations including a lack of coverage, privacy and secu-
a contagious individual. Contact tracing, an old tech- rity concerns, high missed detection and/or false alarm
nique, has been used as effective tools to battle pan- rates. For instance, increasingly Bluetooth-based con-
demics for many years, and some countries do use ag- tact tracing has gained mainstream use particularly with
gressive contact tracing to successfully contain COVID- Apple/Google’s support. However, our recent analy-
19. However, traditionally, contact tracing is a manual sis [29] with the released COVID-19 contact tracing apps

© International Telecommunication Union, 2020 89