By Clarice Cudischevitch
And it’s not intuitive: in the state of São Paulo, it’s best not to test in the capital
There are no Covid-19 tests for all Brazilians. The state of São Paulo, for example, now manages to test 30,000 people a day – 750 per million people. It is little and implies decisions: is it better to focus tests in the capital? In small towns? Where is it most effective to test the population?
Researcher Tiago Pereira of the USP Center for Mathematical Sciences Applied to Industry uses mathematics to find these answers. Together with other research groups from USP, IMPA, FGV, UFAL and Unicamp, he developed a model that combines demographic data and cell phone data to understand how people move around. Understanding this pattern can help you figure out the best way to distribute the tests.
The aim is to find an intelligent test protocol that, by reducing the transmission of the virus, enables a possible return to normal and, for example, avoids the closure of the trade. On this basis, the algorithm decides where and when the tests should be sent.
Pereira’s personal goal was to bring life back to normal: If you liked going to university, you had to turn your 5-year-old son’s room into an office. Having three children at home shortened working hours while obligations doubled. “The work has quadrupled,” he jokes.
The mathematical model works like this: First, it predicts what action the government should take (e.g. restricting the movement of people) if no one has been tested. It is then assessed how much more efficient this state intervention becomes in different test distribution models.
If the tests are only done in small towns, the efficiency of getting life back to normal without harming the health system increases by 10%. If they only go to the capital, 30%. If you test on demand, that is, test symptomatic people, the increase is 35%. With intelligent tests, the rate is 70%.
In early December, the group received the results of these intelligent tests. The best protocol is the distribution of tests in the metropolitan area of São Paulo – not in the capital, but in the surrounding area. This guarantees that people from smaller cities who go to the capital will not cause any new infections. “The conclusion may not seem intuitive,” says Pereira. “Not testing in the capital is not expected to result in better control of the pandemic.”
The model takes into account not only the number of people in each region and their age groups, but also the employment in the intensive care unit, as the testing strategy is combined with the redistribution of beds to avoid any disruption to health. If a region is close to 100% bed occupancy, the equation solves itself to relocate the tests there.
The work should be published in the coming weeks. However, this wasn’t the first time the group had used mathematical models in studies of Covid-19. At the beginning of the pandemic, they were looking for a way to optimize social distance without all cities having to close at the same time. It won’t be the last either: Researchers are now trying to develop a model that says who, where and when needs to be vaccinated to end the coronavirus.
The mission is not easy because, unlike countries like the USA, Germany and France, Brazil does not have a database called contact matrix that tells how the age groups talk to each other (how many 10 year old children live with) how many teenagers live in schools with people aged 50 and over, etc.).
“This information is important in knowing who to start vaccination with,” he explains. The mobility matrix is directly related to the cultural aspects of a country. You can point out that vaccinating everyone over the age of 60 is not necessarily best. This depends on several factors, including the situation of ICU beds in cities.
Researchers are currently trying to fill this gap through statistical analysis with no definitive results. But it seems that here they also promise not to be intuitive.
Clarice Cudischevitch is a journalist, communications manager at Instituto Serrapilheira and coordinator of the blog Ciência Fundamental.
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