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. 2020 Aug 7:8:145242-145255.
doi: 10.1109/ACCESS.2020.3015002. eCollection 2020.

Tracking COVID-19 by Tracking Infectious Trajectories

Badreddine Benreguia  1 , Hamouma Moumen  1 , Mohammed Amine Merzoug  1
Affiliations

Tracking COVID-19 by Tracking Infectious Trajectories

Badreddine Benreguia et al. IEEE Access. .

Abstract

Nowadays, the coronavirus pandemic has and is still causing large numbers of deaths and infected people. Although governments all over the world have taken severe measurements to slow down the virus spreading (e.g., travel restrictions, suspending all sportive, social, and economic activities, quarantines, social distancing, etc.), a lot of persons have died and a lot more are still in danger. Indeed, a recently conducted study [1] has reported that 79% of the confirmed infections in China were caused by undocumented patients who had no symptoms. In the same context, in numerous other countries, since coronavirus takes several days before the emergence of symptoms, it has also been reported that the known number of infections is not representative of the real number of infected people (the actual number is expected to be much higher). That is to say, asymptomatic patients are the main factor behind the large quick spreading of coronavirus and are also the major reason that caused governments to lose control over this critical situation. To contribute to remedying this global pandemic, in this article, we propose an IoT investigation system that was specifically designed to spot both undocumented patients and infectious places. The goal is to help the authorities to disinfect high-contamination sites and confine persons even if they have no apparent symptoms. The proposed system also allows determining all persons who had close contact with infected or suspected patients. Consequently, rapid isolation of suspicious cases and more efficient control over any pandemic propagation can be achieved.

Keywords: Big data; COVID-19; Internet of Things; coronavirus; infection tracking; information and communications technologies.

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Figures

FIGURE 1.
FIGURE 1.
Infection tracking - example of historical trajectories taken by a COVID-19 patient and all persons (resp. places) who he might have encountered/infected (resp. visited).
FIGURE 2.
FIGURE 2.
Overall system architecture.
FIGURE 3.
FIGURE 3.
Data filtering (preprocessing).
FIGURE 4.
FIGURE 4.
Set of all suspected persons at time t.
FIGURE 5.
FIGURE 5.
Main investigation algorithm - finding and categorizing suspected cases into disjoint subsets.
FIGURE 6.
FIGURE 6.
Algorithm executed by clients (e.g., mobile applications).
FIGURE 7.
FIGURE 7.
Black areas determination algorithm.
FIGURE 8.
FIGURE 8.
Set of all suspected persons who have frequented black areas at time t.
FIGURE 9.
FIGURE 9.
Algorithm for finding persons who have visited black areas.
FIGURE 10.
FIGURE 10.
Developed application architecture.
FIGURE 11.
FIGURE 11.
Screenshots from the developed application.
FIGURE 12.
FIGURE 12.
Snippet of the collected trajectories of one user.
FIGURE 13.
FIGURE 13.
Main application interface for authorities.
FIGURE 14.
FIGURE 14.
Examples of interfaces for government and health authorities.
See this image and copyright information in PMC

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