Abstract
COVID-19 has become a global pandemic during 2020 due to its high contagiousness and the high mobility of the world's population today. In just one year, this virus has caused millions of infections and deaths worldwide. These numbers will continue to grow until the population becomes immune to the virus thanks to an effective vaccine. Until this is possible, the only viable strategy is to try to stop its expansion through preventive measures such as limiting mobility, the use of masks, etc. In order to support these measures, this article presents a service to provide safe navigation solutions to reduce the likelihood of infection by avoiding potential conflict areas in the city. To identify these hotspots, a strategy that combines a rule-based system and a common-sense knowledge base is proposed. Through this strategy, an occupation model and a danger model are inferred. This requires the prior capture of knowledge about the general functioning of the city, its inhabitants and the virus. The proposed service makes decisions from these two models. Finally, a validation process has been carried out through surveys to evaluate the proposed solution. Obtained results demonstrate the potential of the proposed solution as a tool to identify safe routes that allow citizens to move around the city with low exposure to COVID-19.
Highlights
According to the weekly report of 5 April 2021 provided by the World Health Organization (WHO) there have been a total of 130,422,190 confirmed cases and 2,842,135 confirmed deaths from SARS-CoV-2 around the world [1]
According to the information provided by the WHO [4], the human to human spreading of the virus occurs due to close contact with an infected person exposed to coughing, sneezing, respiratory droplets or aerosols
For this model to be really useful it must be able to capture the usual functioning of the city and the routines of its citizens. This is the only way to effectively identify and represent risk in different areas of the city. To obtain this model we propose to combine the accuracy of a rule-based system like CLIPS [9] with the flexibility and inference capability of a common-sense knowledge base as Scone [10], [11]
Summary
According to the weekly report of 5 April 2021 provided by the World Health Organization (WHO) there have been a total of 130,422,190 confirmed cases and 2,842,135 confirmed deaths from SARS-CoV-2 around the world [1]. This is the main objective of the proposed work, the identification of this type of situation to help to correct the patterns of movement of citizens that may be problematic and, as a final result, to reduce the probability of contagion To this end, we propose a safe pedestrian navigation service that allows people to consult possible risk areas in the city and, avoid them through alternative routes or roads. A direct consequence of this approach is that compliance with distance measures would be greatly facilitated and less crowding would occur at certain times of the day To support this safe navigation service, a danger model is proposed to identify everyday situations in the city that, due to their nature, can be dangerous because of the large number or type of people they gather.
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