COVID-19

Analysis and traceability of spread of COVID19 virus through mobile devices

IP 121-674 - UNPCBA y UNC

Funded by ANPCyT

The objective of the project is the development of a platform that supports a geo-referenced social network that allows epidemiological monitoring of the COVID-19 pandemic. On this digital platform, users will be able to report on their health conditions and track cases in real time. Each user will be able to know their current location, their daily places and the places visited in the last days. This information is of vital importance for the prevention of contagion and the spread of the epidemic. Likewise, the aggregate set of users will allow the management and analysis of mobility of a district, enabling decision-making at different levels.

The spread of any infectious disease, such as COVID-19 that affects us at the moment, is mainly produced by the spread of an infectious agent through the mobility of individuals. Knowing therefore the places through which an infected individual transited and remained is of utmost importance to analyze what other individuals or groups of individuals may have been exposed to a contagion and to propose strategies and optimizations of available resources.

Crowdsensing or mobile crowdsensing, is a technique that uses a group of individuals with mobile devices capable of sensing and/or collectively calculating data to extract from them information that allows to measure, map, visualize, estimate or infer any process of interest common (Campbell et al. 2008). This technique is within the geo-crowdsourcing paradigms, a term that could be translated into Spanish as distributed open collaboration, and Voluntary or Participatory Geographic Information (VGI in its English acronym Volunteered Geographic Information) (Chatzimilioudis et al. 2012).

The general objective of this project is then to use crowdsensing techniques to design and develop a digital platform that can be useful for decision-making in the development of the COVID-19 pandemic. The proposed digital platform focuses on the use of mobile devices by citizens. Modern mobile devices are equipped with sensors that allow different aspects of the user's context to be recorded and inferred, including their geographical location. A mobile device can know its location not only by using GPS, but also through nearby WiFi access points in a process called WiFi Positioning Systems (WPS). This process consists of triangulating the location of the device based on the location of nearby WiFi access points. While GPS location works best outdoors, and outdoors, WPS location works best indoors, where there are more WiFi access points to triangulate with.

Using both aforementioned location mechanisms, it is possible to know the location of the user almost anywhere and at any time. In turn, using other sensors in the device, such as the accelerometer, it is possible to know if the device is moving by moving or not. This allows you to adjust the frequency with which the device is located (for example, it is not necessary to locate the device while it is still), thus reducing the use of GPS / WPS and therefore minimizing battery consumption.

The proposed project is built on the basis of a mobile application prototype developed by the work team, whose objective is to record the physical activity and geographic location of the user during their daily routine in a non-intrusive way. From this data, it detects the places that the user visits and the paths that he takes to transport himself from one place to another. The information registered by the application on the mobile devices of the different citizens would be the basis for the proposed digital platform that allows analyzing and controlling a pandemic such as COVID 19 in which the mobility of citizens is a key point. The application could not only provide information on the mobility of citizens, but would also allow communication channels to be established with citizens (either a general information channel or a particular channel for a certain individual) and for citizens to generate data or actively useful information (through reports).

IP: Dr. Jorge Finochietto (UNC)

Team Members:

- Dr. Marcelo Armentano (UNCPBA)

- Dr. Luis Berdun (UNCPBA)

- Dr. Oscar Nigro (UNCPBA)

- Dr. Silvia Schiaffino (UNCPBA)

- Ing. Sebastián Vallejos (UNCPBA)

- Dra. Sandra González Císaro (UNCPBA)

- Dra. Graciela Corral Briones (UNC)

- Dr. Mario Hueda (UNC)

and other members from UNC