Artículo del estudio de EE UU sobre donde se producen las infecciones... Maravilloso, el jueves pasado Tegnell decía que los restaurantes eran un lugar de bajo riesgo. Os daís cuenta en manos de quién está Suecia? Y lo idolatran aquí la gente! Creen que es un dolido genio! Como nuestro pobre amigo
@Atanasio Lonchafinista 
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Restaurants, gyms, cafes and hotels are environments with an extra high risk of spreading infection according to a new study based on data from mobile phones in the USA's ten largest cities. The study also shows that vulnerable groups are particularly hard hit by the pandemic because they cannot reduce their mobility as much as others.
This is how super-distribution works
Super-spreading occasions for the new corona bichito sars-cov2 have taken place in many different environments. Why is this happening, and why is superdistribution so important?
Researchers do not know why some people are so much more contagious than others. Despite this, there are good conditions for preventing super-proliferation opportunities from arising.
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Super-scattering events are dependent on several different factors interacting. One is the biology of the infected individuals. But beyond that, most super-scattering events occur when many people are gathered for a long time in a small area. You could say that the environment itself is a super-spreader, says Serina Chang at the Department of Computer Science at Stanford University.
She and her staff have built a computer model for how the infection spread in various places, such as restaurants, hotels, gyms, churches and grocery stores, in major cities in the United States during the spring.
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Ten percent of the places accounted for over 80 percent of the infections in these metropolitan areas between March and May according to our results. It's quite remarkable. The infection is therefore not spread evenly in society, says Serina Chang.
Restaurants with table service had the greatest contagion of the environments in the researchers' model, according to the results published this week in the journal Nature.
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Restaurants are about four times as dangerous as the next category, which is the gym.
Then come cafes and hotels, says study leader Jure Leskovec, associate professor of computer science at Stanford.
The researchers used data from mobile phones that show how people move between their residential areas and different public environments.
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We use mobile data to simulate the spread of el bichito-19. We capture the movement pattern of almost one hundred million people in the United States' ten largest metropolitan areas, says Jure Leskovec.
The new model can help decision-makers deal with the pandemic, the researchers hope.
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Eight months into the pandemic, there is still a debate about when we should open society again, which places should be opened and how we should open them. We really believe that a stronger empirical basis is needed to choose the right strategy, says Jure Leskovec.
- According to our results, we should not treat all different environments equally, or open them in the same way, because the risk of spreading infection varies so much, says Serina Chang.
The mobile data the researchers had access to were anonymised, but they could divide it into groups of between 600 and 3,000 people according to the different residential areas the people's daily movements were based on.
- We could model how people moved hour by hour, and predict where and when someone would be infected, says Serina Chang.
The researchers assumed that there was a small proportion of those infected in all groups at the beginning of the pandemic.
- Our simulation begins on March 20. Then we pressed play. Every hour thereafter, some people from the different groups go to one of the environments. All movements in the model are based on real mobile data. Some people are susceptible to viruses, some are infected. How likely it is that someone will be infected in the model depends on the surface of the premises, how long you are there, how many other visitors are there at the same time and how many of them are infected, says Serina Chang.
Even when the people in the model are at home, there is a certain risk of becoming infected.
- To calibrate and check that our model is reliable, we compared our predictions with the actual number of daily cases of el bichito-19, according to the report in the New York Times. We show that our model can predict the number of reported cases in all ten metropolitan areas, says Serina Chang.
During the spring, several model calculations with gloomy predictions for the development of the pandemic in Sweden received much attention. With the results in hand, it turned out that the models had overestimated both the need for intensive care units and the number of deaths large. According to a study recently published in the Journal of the Royal Society Interface, many such models end up in real trouble because they expect an even spread of infection and do not take into account the dynamics of super-spread events that have been shown to be so characteristic of el bichito-19. The Swedish models were also criticized early on because they were far too complex and contained a large number of unknown parameters.
(COMORRRRR? Sobreestimado las muertes es tener 10 veces más muertos que noruega? Este diario el DN es totalmente prosocialista y pro-Tegnell, colaboracionistas genocidas).
On the contrary, the model Serina Chang and her co-workers have created has very few parameters.
- Mobility is the only parameter that varies with time. During this period, from March to May, people's behaviors changed a lot, with, for example, mouth protection, hand washing, social distance and the like. That our model still succeeds so well in recreating the real infection curve may indicate that mobility in particular plays a crucial role in the spread of infection, she says.
Across the world, the pandemic has hit low-income earners and other vulnerable groups hardest. The researchers' model provides a possible explanation: The vulnerable groups have not been able to afford or have the opportunity to stay at home.
- It is striking that our model makes correct predictions that groups with lower incomes and with a lower proportion of whites are infected with el bichito-19 at a significantly higher rate, only based on mobility. This is probably due to the fact that the groups are overrepresented among people who have jobs that cannot be managed from home. It makes a difference if you work in or only shop in a grocery store, for example, says Serina Chang.
- Grocery stores that low-income earners visit are generally more full of people. According to our model, a visit to the grocery store is twice as dangerous for a low-income earner as for a high-income earner, says Jurek Leskovec.
