Application-Based COVID-19 Micro-Mobility Solution for Safe and Smart Navigation in Pandemics

Short distance travel and commute being inevitable, safe route planning in pandemics for micro-mobility, i.e., cycling and walking, is extremely important for the safety of oneself and others. Hence, we propose an application-based solution using COVID-19 occurrence data and a multi-criteria route planning technique for cyclists and pedestrians. This study aims at objectively determining the routes based on various criteria on COVID-19 safety of a given route while keeping the user away from potential COVID-19 transmission spots. The vulnerable spots include places such as a hospital or medical zones, contained residential areas, and roads with a high connectivity and influx of people. The proposed algorithm returns a multi-criteria route modeled on COVID-19-modified parameters of micro-mobility and betweenness centrality considering COVID-19 avoidance as well as the shortest available safe route for user ease and shortened time of outside environment exposure. We verified our routing algorithm in a part of Delhi, India, by visualizing containment zones and medical establishments. The results with COVID-19 data analysis and route planning suggest a safer route in the context of the coronavirus outbreak as compared to normal navigation and on average route extension is within 8%–12%. Moreover, for further advancement and post-COVID-19 era, we discuss the need for adding open data policy and the spatial system architecture for data usage, as a part of a pandemic strategy. The study contributes new micro-mobility parameters adapted for COVID-19 and policy guidelines based on aggregated contact tracing data analysis maintaining privacy, security, and anonymity.

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Evaluation of epidemiological risk using contact tracing open data (Preprint)

10.2196/preprints.28947 ◽

2021 ◽

Author(s):

Simona Concilio ◽

Luigi Di Biasi ◽

Francesco Marrafino ◽

Simona Concilio

Keyword(s):

Data Analysis ◽

Open Data ◽

Real Data ◽

Security And Privacy ◽

Contact Tracing ◽

The Road ◽

Pearson Coefficient ◽

Epidemiological Risk ◽

Contact Data ◽

Ct Data

BACKGROUND During the 2020s, there was extensive debate about the possible use of contact tracing (CT) for SARS-CoV-2 pandemic containment, and concerns have been raised about data security and privacy. Little has been said about the effectiveness of CT. In this work, we present a real data analysis of a CT experiment conducted in Italy for eight months involving more than 100,000 users. OBJECTIVE We discuss the technical and health aspects of a centralized approach. We show the correlation between the acquired contact data and the number of positives to SARS-CoV-2. We analyze CT data to define population behavior, and we show the potential application of real contact tracing data. METHODS CT data were collected, analyzed, and evaluated on the basis of the duration, persistence and frequency of contacts over several months of observation. A statistical test was conducted to determine whether there is a correlation between indices of behavior calculated from the data and the number of new infections in the population (new positives). RESULTS We evidence a correlation between a weighted measure of contacts with the new positives to the virus (Pearson coefficient = 0.86), paving the road to a better and more accurate data analysis and spread prediction. CONCLUSIONS The data are used to determine the most relevant epidemiological parameters and can be used to develop an agent-based system to simulate the effect of restrictions and vaccinations. Finally, we demonstrated the system's ability to identify the physical locations where the probability of infection is highest. All data collected are available to the scientific community for further analysis.

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