Monkidev

https://espolec-my.sharepoint.com/:v:/g/personal/marsagui_espol_edu_ec/ESoFbtkt5o1Di-Uh6fCcaLcBhED0exOO7Ias3igKcBVPWA?e=j9NuVs

https://github.com/glorona/Monke2/tree/master

Mo-vid obtains data from an api specialized in collecting socio-geographic information called BestTime.app, which provides various statistics on the density of people in specific areas, especially focused on the United States, thanks to this data, our application can make an approximation of how safe it is to go to a place in a certain range of hours, depending on how many people are in a certain location, it is indicated if it is advisable to attend the place or not, if the attendance is high in at that time, a range of hours will be recommended where it is safer to go to the place, in order to make the user aware of the possible risks that moving to certain areas of the city may represent.

Also, Mo-vid fulfills other functions that contribute to the awareness and information of the users who use it, one of these functions is called “Covid in your area”, which based on data provided by Johns Hopkins University (JHU), shows the users percentages and important data on the disease in their geographic area, such as, index of vaccinated people, number of active cases, number of total cases, and other varied information. In addition, that, depending on the geolocation of the user, a series of points of interest will be shown such as pharmacies, vaccination points, hospitals, etc.

With this application, it is sought to reduce the number of infected people worldwide, creating awareness about the risk of mobilizing depending on the time and number of people present in different parts of the world and in this way contribute actively and in real time to the health and care of users who use the application.

Mo-vid makes use of various computer tools, however, its code is programmed in the JAVA language, making use of the Android Studio IDE that facilitates access to the creation of mobile applications that adapt to as many devices as possible. Python and its PANDAS library were also used to filter data and be able to make use of it more easily in the final application.

As we know, we cannot rely only on the density of people as one of the risk factors for covid-19 but also on the rate of vaccinated people, number of possible active cases of infection, use of masks and climatic factors.

One of New York's peak infections was 5,949 on January 13, 2021 and an average of 6,263 cases per 7 days, at a time when the vaccine was not fully developed. However, one of the dips in the curve as on June 28, 2021 there was an average of 182 infections per 7 days, and access to the vaccine was a bit easier. We can note the complete difference of vaccination at the beginning of the inoculations in New York City, having an average at the beginning of 2859.43 people per 7 days, and in the last days it has remained at an average of 18 000 to 20 000 people vaccinated per 7 days.

We could say that thanks to the vaccines the cases of covid-19 could have decreased. Currently on September 29, 2021 there is an average of 1,567 cases per 7 days and in the last 15 days (considered important because they are confirmed positive cases that can spread in the next few days) there is an average of 825 cases using data from Johns Hopkins University (JHU). This increase is due to the decrease of restrictions by the government and certain population that is not yet vaccinated, the confidence of people to go out in the street causes possible crowds and new infections of covid.

With these data, it can be concluded that if the number of vaccinated people increases, the risk of infection decreases and therefore the number of infections also decreases (inverse-proportional relationship). This risk can be reduced, however, there are certain groups of people who do not participate voluntarily in the vaccination and that represent a factor in some important part and cause some covid-19 infections.

Taking into account the purpose of the application, we must consider all the factors that influence this. One of these is the use of masks by sector. Using data surveyed by the New York Times and collected in Github repositories, we were able to obtain the percentage of people who claim to use masks by County in the following ranges: never, rarely, sometimes, frequently and always. This factor is presented to the user as a measure to take into account the risk of contagion according to the place where he/she wants to go.

A study conducted by the Harvard T.H. Chan School of Public Health found that people living in an area with more air pollution have a higher mortality rate than those living in less polluted areas. The study was conducted for more than 3000 counties in the United States (representing 98% of the population). According to the study, using COVID-19 deaths as the outcome and the long-term average of PM2.5 (both at the county level) as the exposure, plus other factors including population size, age distribution, population density, time since the start of the outbreak, time since the state issued the stay-at-home order, hospital beds, number of people tested, climate, and socioeconomic and behavioral variables such as obesity and smoking. The results showed that an increase of just 1 μg/m3 in PM2.5 results in an 8% increase in the Covid-19 mortality rate.

Using this study and satellite data (such as the European Copernicus satellite) of the amount of PM2.5 found in a specific sector, we defined that this is a risk factor that will be notified to the user within the application.

The experience that the Monki dev team lived during the Nasa Spaceapps Challenge 2021 was refreshing and extremely interesting because it is our first time as a group participating in a competition of this level, despite the limitations presented by covid 19, we were able to share a pleasant time demonstrating and developing our personal and knowledge skills, such as the use of apis, learn more about Android programming and use of various tools to perform geolocation and relate it to possible risks related to covid 19 contagion.

At the beginning we were a bit lost and puzzled about the topic of our project and how to approach it, however we were able to overcome the challenges of programming and organization and gradually gained confidence in ourselves and our skills as a team, mainly we managed to overcome these challenges thanks to the research and communication between us, and finally, we would like to thank all the mentors who gave us help and advice on how to improve our idea.

Google maps API for JAVA https://developers.google.com/maps?hl=es-419

BestTime.App Api for JAVA https://besttime.app/

COVID-19 Dashboard

by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) https://gisanddata.maps.arcgis.com/apps/dashboards/bda7594740fd40299423467b48e9ecf6

Nasa sample resources

https://github.com/CSSEGISandData/COVID-19

Covid data resources

https://github.com/nytimes/covid-19-data/blob/master/us-counties.csv

Air pollution PM 2.5 data of NEW YORK

https://www.iqair.com/usa/new-york/new-york-city

https://www.hsph.harvard.edu/news/hsph-in-the-news/air-pollution-linked-with-higher-covid-19-death-rates/

Percentaje of mask usage

https://github.com/nytimes/covid-19-data/blob/master/mask-use/mask-use-by-county.csv

#software #android #maps #geolocalization #social

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