Abstract
The COVID-19 pandemic has had severe consequences on the global economy, mainly due to indiscriminate geographical lockdowns. Moreover, the digital tracking tools developed to survey the spread of the virus have generated serious privacy concerns. In this paper, we present an algorithm that adaptively groups individuals according to their social contacts and their risk level of severe illness from COVID-19, instead of geographical criteria. The algorithm is fully distributed and therefore, individuals do not know any information about the group they belong to. Thus, we present a distributed clustering algorithm for adaptive pandemic control.
Highlights
C OVID-19 [1] is a disease caused by the new coronavirus SARS-CoV-2
We present an algorithm that adaptively groups individuals according to their social contacts and their risk level of severe illness from COVID-19, instead of geographical criteria
By combining our algorithm with a distributed consensus algorithm, each individual can know the epidemiological situation of the group they belong to and can take the social distancing measures that correspond to the epidemiological situation of their group
Summary
C OVID-19 [1] is a disease caused by the new coronavirus SARS-CoV-2. It was declared a pandemic by the World Health Organization (WHO) in March 2020. Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS gies have been developed with this purpose, such as digital health certificates, which assign a color-coded COVID status to their users, physical surveillance initiatives [11], symptom checkers, or flow modelling tools, which quantify and track people’s movements in specified geographical regions [12] These technologies, raise severe ethical concerns about putting user’s privacy and security at risk. We propose a distributed algorithm that adaptively groups individuals (i.e., creates clusters) according to their social contacts and their risk level of severe illness from COVID-19. This will be modelled as a doubly-weighted undirected graph.
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