Various restrictions limit our movements and our social life to stop the transmission of covid-19. As it is so difficult for us to assess risks, a specific estimate can make it very clear why we should not have meetings that bring together many people. For example, In the province of Huesca, the probability right now that at least one person was infected at a wedding of 100 guests would be 34%.
This is considered by the platform developed by researchers from the Institute of Biocomputation and Physics of Complex Systems (BIFI) of the University of Zaragoza to raise awareness of the impact of events with large groups. The map shows, by provinces, “an estimate of the risk of exposure to SARS-CoV-2 according to the number of people who attend an event – explains Yamir Moreno, researcher at BIFI -. The risk is expressed as the probability that at least one positive individual is present in it“. The user can interact and see how this risk increases as the size of the event grows (in the same province, from 500 people the probability of coinciding with at least one positive is already 100%). The map is updated daily with the official data on the incidence of covid-19.
Researchers caution that this tool does not express the risk of contracting the disease, which also depends on factors such as the duration of the event, the space in which it takes place and the protection measures.
Lotteries and betting
The humans we are not exactly good at assessing the risk we face every time. “For example, we tend to put ourselves in the minority when it comes to something that does not favor us, such as when we are afraid to get on a plane and not in a car, even though the probability of having a fatal car accident is 86 times higher than having it if we travel by plane –explains Moreno–. On the contrary, if it is something positive, such as winning in the lottery, we don’t mind taking risks or, in this case, investing. “
During the pandemic, many ask a question similar to the previous ones: what is the probability that I will get COVID-19 if I attend an event of a certain size? “This question does not have a universal answer and depends on factors such as the susceptibility that each one of us has to contract or transmit the virus,” he says. “What Yes we can approximate is what is the probability that a person who attends that event or meeting is positive for SARS-CoV-2“.
Moreno explains that to answer this question “it is easier to estimate the probability of just the opposite. For example, If we go to the stadium to watch a football game, what is the probability that none of the 50,000 spectators is infected? Let us now consider each of the viewers. If 1,000 of the approximately 47 million people in Spain are infected, the probability of not being infected can be estimated at 46,999,000 / 47,000,000, that is, a probability of 99.9978%. An optimal perspective from an individual point of view. In terms of bets, we are talking about a favorable event, 47,000 to 1! “Now, he continues,” what is the probability that all 50,000 people are not positive? From a probabilistic point of view, this equates to winning 50,000 bets simultaneously. Although each bet has a very high probability of winning, it is not easy to win them all. This probability can be calculated by multiplying that of a single win (0.999978 …) by itself 50,000 times. So we will get that the probability of winning all bets is only 34.5%. In other words, the probability that at least one bystander tests positive for SARS-CoV-2 is 65.5%! “
The Web ‘Events and covid-19’ It is part of an international project in which American researchers from the Georgia Institute of Technology and Northeastern University and the ISI Foundation in Turin (Italy) collaborate. It was first made for the United States and is now implemented in Italy and the United Kingdom. Spain is the fourth country that is added and the Group of Complex Networks and Systems (Cosnet) of BIFI is responsible for the project. “The idea is to extend the analysis to the more countries the better.”
What it evaluates
- Cases The estimate of the prevalence of cases in the province is given by the number of cases detected by the surveillance system in an interval between the previous 7 and 10 days.
- Detected There are cases that escape the surveillance system. The factor of underestimation of infections, that is, how many we are able to detect in relation to the number of real cases, can vary between 1: 2 (1 in 2 real infected) and 1: 6 (1 in 6 real infected) and introduces a corrective factor to estimate the real prevalence of the number of infected.
- Size Number of participants in the event.
A synthetic Zaragoza to simulate the effect of restrictions
How much would transmission drop if we confine Zaragoza to the perimeter and the curfew is also decreed? And if we also closed the bars, what would the difference be? Computational models predict, allow to ‘test with soda’ any measure. Simulate its effect and see what would happen, with the main function of helping decision-making. But for that you have to put the city in the computer. Yamir Moreno’s group at BIFI is going to “adapt the studies we have been doing in the US to Aragon and to Spain later.” They will start by creating a synthetic Zaragoza, incorporating census data, age, demographics, distribution of work centers and schools, etc. Thus, “we will have a population statistically equivalent to the real one.” Then, it will be necessary to generate the interactions between individuals and finally apply the epidemiological model. In the case of Boston, they had mobile phone data, “as we do not have this type of information here, we will generate interactions and contact networks artificially, but informed by data and mobility models.” He knows that “it will not be as accurate as Boston, but we believe that the average result of many realizations of artificial societies and evolution of the epidemic will be quite reliable.” These models “do not tell you what to do, but they are a tool that allows you to anticipate.”
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