Our project would be one of the most conclusive tools in the manipulation of the most up-to-date data of the inhabitants of a population, committing to reflect precise behaviors and thus allow direct prevention towards the individuals of the population and the government departments in making decisions for each country.
Choosing this theme was inspired by the fact that we observe that not all people follow the discipline established by their country, lack of education, social class, which are factors that have made the country we were born in, Panama, increase in cases every day more.
The approach to carry out this project is that we want to achieve better control, so that people who are vulnerable to diseases can prevent the spread of covid-19.
Solution
Using the optimization algorithm for ant colonies, we take advantage of its concept in a mobile application, where the cell phone would be the ant, which when they move around an area would leave pheromones on the path they travel, you are losing activation as time passes (This it will be a count that those pheromones carry). So if other mobiles (which are carried by people) take that same path, they make the pheromones stronger. So when mobile devices are constant for a time in an area, that place is marked on the map as an agglomeration of people, that point will be registered; When other devices (Person) are going to pass through that place, the application sends you an alert saying that there is an agglomeration in that area and that it is at a certain distance, here the person can make the following decisions:
Based on the information collected as time passes, if the majority of the answers are affirmative (yes), the process of determining agglomeration and sending the alert to the authorities through Twitter is streamlined. On the other hand, if no one reports or claims that there is an agglomeration, and the algorithm continues to detect grouped devices after a certain time, it would also send a message via Twitter to the corresponding authority in the country in which it is located.
The algorithm would continue to work to update the data and if it detected the same agglomeration again, it would continue to send the alert, until it disappears and regenerates the information of the place.
If the assessment is successful agglomeration, the app sends a message via Twitter to the corresponding authority of the country in which it is located. All this taking advantage of the GPS of the mobile device.
If no one reports or claims that there is an agglomeration, the algorithm continues to detect grouped devices after a certain time, send a message via Twitter to the corresponding authority in the country in which it is located. All this taking advantage of the GPS of the mobile device.
So based on what other people confirm and the registration of the location of the mobiles, we carry out an evaluation to determine if there is an evaluation based on the new information.