We present the SIR model approach of Team Valencia.
My name is Francisco Collado.
I am a teacher in a high school in Valencia.- Spain. I teach Automation and Industrial Robotics.
Regarding Covid19, by those early days of March, it was already known that several Valencia CF fans who had gone to Milano to attend the Champions game against Atalanta Bergamo had become ill. Although there was still ridicule, as a class exercise we started to think about making a field hospital using containers and infrared sensors for fever, humidity for sweat and CO2 / airflow for cough, and sick breathing. So we started doing calculations to find out how many containers we would need to assemble, so how many people would get sick.
When we were leaving for the first week of March I told my students that we would reach 100,000 sick and 500 dead in one month, using a SIR model spreadsheet; we were still going for 400 infected and 8 dead. Call me exaggerated. How were we going to reach 500 dead? We weren't in China!
And it arrived on March 8: International Women's Day and hundreds of stadiums with football games. Tens of thousands of people summoned in every event, without notice from the authorities; on the contrary, several ministers encouraged attendance at feminist events. After March 8, it was passed to 1000 sick and 16 dead. Duplicated !! A qualitative and quantitative leap. That Tuesday I performed the sequence of numbers again. I forecast for the first of April 100,000 infected and 10,000 dead.
But we were in full spring festivities throughout Spain. Las Fallas de Valencia, where mass meetings of 10,000 people are held. In the worst-case scenario, a million people were killed. I stared at the screen. They were typical data from 1349, the black plague.
And the middle of March came and the order came to stay home. So I continued alone the calculations I had shown in the classroom.
I started with the SIR model. Unfortunately, the health ministry changed the methods of calculating infected about fifteen days ago, which created a question for me: to continue with the initial numbers or to modify them. I kept the initial sequence, so the forecast is not the official figure, but what it really should be.
For a future datathon, there would be several options:
A Markov process could be carried out to determine the evolution of the cycle. Or apply a new method: "Machine Learning" and analyze images of pandemic curve patterns using a bot that creates predictive behavior. For this, I would emphasize that a disease curve pattern can be found based on these variables (By country):
1.- When is the zero outbreak discovered?
2.- When are restrictive measures of confinement taken?
3- When 1000 cases of contagion have been verified.
4.- Hospital capacity in beds per 100,000 inhabitants and sanitary equipment and materials.