DELFRI TECH

https://drive.google.com/drive/folders/1CobRUNIch0tqATt2PvPMILvTnuTmg1Bp?usp=sharing

https://drive.google.com/drive/folders/1LIa59w3cz-x80oXs5rZSDyt0hbOq8Q8L?usp=sharing

DELFRI TECH - Delete From Risk team members: José Cerilo Asencios Chávez, Karen Anaís Rodríguez Espejo, José Sebastián Ibarra Arregui, Walter Martin Izaga Valderrama, Angelo Michael Huaraca Berrospi, and Kelly Marisol Flores Pariona.

Mira el video de DELFRI TECH en el siguiente link: https://drive.google.com/drive/folders/1CobRUNIch0tqATt2PvPMILvTnuTmg1Bp?usp=sharing

OUR TARGET AUDIENCE

Our project is aimed at all people around the world who want to feel safe walking through the streets, visiting a park or taking a walk with their family, trying to return to a new normal. We seek to provide security to people by preventing them from going through highly contagious areas, and instead, from traveling through safe places but without lowering the care against COVID-19. Despite being outdoors, we can get it, but the possibilities are greatly reduced (1), so people should not be careless even when they go out for a few hours. However, the reality is different, most of the world population does not know if these places are highly contagious or not, so they take the least security measures when thinking: "I'm on the street, I can't get it" or " this place is already safe ”, which can lead to a possible contagion and further spread of the virus. So, it is essential to have an application that gives us alerts when we go through highly contagious areas so that we can increase our care and thus avoid getting infected.

The contagion rate also varies according to where we are, so most countries analyze the concurrence of cases according to their departments or states so that they can establish targeted strategies to mitigate the spread of COVID-19 with greater efficiency; However, these data are not treated and processed for public use, so the population cannot easily use them. Can you imagine how many cases could be avoided if people knew which areas are safer to travel through or which ones are not? We consider that many.

On the other hand, despite the fact that confinement has already ended in various parts of the world, many people continue to be semi-detained despite the fact that the mandatory confinement has ended. In Spain, about 34.3% of the population is insecure when leaving their home (2), since they do not know how dangerous it can be for their health, which generates feelings of anxiety, stress, fear and sadness. These feelings affect your mental and psychological health, which is more damaging today.

With DELFRI TECH, we want to give these people the security they seek so that they can carry out their activities respecting the new normal. People will be able to receive personalized and real-time risk alerts through notifications on their smartphones to avoid highly contagious areas, and they can take the corresponding precautions to avoid contagion. In addition, for those who have the doubt if they were infected with COVID-19, DELFRI will carry out a virtual triage through a chatbot, to rule out possible cases. As well as, it will provide information about COVID-19, symptoms, care recommendations, about the vaccine and vaccination centers, to keep people informed.

DELFRI through the interaction of machine learning, data science, geolocation and the chatbot, has the ability to predict the level of contagion risk through an algorithm that according to the user's data, their location and environmental parameters that collects live, in order to provide alerts for highly contagious areas and recommendations to take into account to avoid contagion.

To develop DELFRI, which is an artificial intelligence, it is intended to use a large amount of data that serves as a basis for its training and thus can assess the level of risk for a person. The data he uses for his training are: the rate of infected and mortality by COVID-19 according to district and state, the index of people vaccinated with both first and second doses according to district and state, meteorological data such as temperature, specific humidity, winds, pressure, pollution, solar / UV radiation by district and state; social behavior of the population such as the number of police incidents, as well as geographic data such as population density, GDP, and economic activity represented in number of businesses, supermarkets, cinemas, by district and state. The first correlation that is obtained is of all this data with the number of infected to determine an average level of risk that it can pose for a person. After that, a correlation is sought between the person's health, age and sex with the level of vulnerability they have. So that afterwards a level of risk can be finalized according to each type of person profile.

Now, this artificial intelligence, to determine the personalized risk level, has as input data the ambient temperature, pressure, humidity, geolocation, vulnerability and user profile, which enter the model and interact with the network trained to provide a specific risk response to the type of user determined by the AI.

On the other hand, DELFRI also has a chatbot that interacts with the user. This Chatbot is developed with IBM's Watson Assistant, and its main objective is to provide two functions to the application. First, it provides a virtual triage to monitor the status of the person, in which easy questions are asked that the user can answer, and give a first diagnosis of COVID-19. So, the user can check if he is okay or his symptoms belong to another disease. Secondly, in the future it is sought to connect the chatbot with the main AI so that DELFRI can provide recommendations of safe places that the user can visit. These recommendations would be based on the level of risk that person may have in the place, so it recommends the places where the risk is the least.

1. https://sedac.ciesin.columbia.edu/data/sets/browse - It will be used to obtain the data on the socioeconomic level
2. https://search.earthdata.nasa.gov/search? - fdc=Goddard%20Earth%20Sciences%20Data%20and%20Information%20Services%20Center%20(GES%20DISC)&m=-11.911376953125!-78.5830078125!7!1!0!0%2C2
3. https://earthdata.nasa.gov/learn/articles/sedac-covid-19-viewer
4. Watson Studio IBM
5. Watson Assitans IBM
6. https://earthdata.nasa.gov/eosdis/daacs/gesdisc

OUR EXPERIENCE

The experience has allowed us to create synergy between five different people with different skills and knowledge in order to solve a problem. We learned that working on an idea and brainstorming for its development allows for a creative solution, but especially if the team is willing to compromise. This challenge has caught our attention mainly because 3 members of the team are biomedical engineering students, and due to their interest in providing solutions through technology to health problems, they found this ideal global problem to solve. In fact, as a whole team, we consider that the challenge of being Covid 19 currently represents a problem that must be fought. Above all to ensure the welfare of the population.

There were certain challenges and setbacks with some members of the team, even two of them had to retire and be replaced by new ones, because as we initially considered, the creative and innovative solution is reached only if the whole team wants to commit. Fortunately, the problems were solved through assertive communication and without stopping the work on the project. In the end, we all could get into a very special solution for Covid 19 contagion risk.

1. https://www.bbc.com/mundo/noticias-55692541
2. https://www.lavanguardia.com/vivo/psicologia/20201107/49263847863/coronavirus-semirecluidos-desde-marzo-miedo-pisar-calle-covid-19.html 
3. https://terapiayemocion.com/coronavirus/miedo-al-contagio-miedo-al-covid-19
4. https://covid19.minsa.gob.pe/sala_situacional.asp
5. https://cdn.www.gob.pe/uploads/document/file/700267/TECNOLOG%C3%8DA_DE_AVANZADA_PARA_COMBATIR_EL_COVID-19_EN_EL_PER%C3%9A_x2.pdf
6. https://www.cognitiva.la/noticia/el-chatbot-chileno-que-identifica-contagiados-de-coronavirus-con-inteligencia-artificial/
7. https://gisanddata.maps.arcgis.com/apps/dashboards/bda7594740fd40299423467b48e9ecf6
8. https://www.gob.pe/busquedas?categoria%5B%5D=6-salud&contenido%5B%5D=noticias&institucion%5B%5D=minsa&sheet=1&sort_by=recent&tipo_noticia%5B%5D=3-comunicado
9. https://covid19.minsa.gob.pe/sala_situacional.asp
10. https://blogs.iadb.org/innovacion/es/tecnologia-y-conectividad-enfrentar-crisis-coronavirus/

IBM Watson Studio

1. https://www.ibm.com/cloud/watson-assistant
2. https://eodashboard.org/?country=PE
3. https://cloud.ibm.com/docs/assistant?topic=assistant-getting-started
4. https://www.ibm.com/docs/es/wsd?topic=overview

1. Implementación rápida de tecnología móvil para la epidemiología en tiempo real de COVID-19
2. Desde <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7200009/> 
3. Desarrollo de una aplicación de Android para ver las zonas de contención de Covid-19 y monitorear a los infractores que ingresan mediante Firebase y Geofencing
4. Desde <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328652/> 
5. Una revisión de las aplicaciones móviles disponibles en la aplicación y las tiendas de Google Play utilizadas durante el brote de COVID-19
6. Desde <https://www.dovepress.com/a-review-of-mobile-applications-available-in-the-app-and-google-play-s-peer-reviewed-fulltext-article-JMDH#> 

#MobileApp #COVID-19 #Geolocation #Risk #Health

This project has been submitted for consideration during the Judging process.