The challenge of winning the game against covid-19

As long as we do not have collective immunity or a vaccine, we must act at the source: cut the chains of transmission and prevent infectious individuals from infecting people. We do not need to test everyone, but rather identify only half of the symptomatic individuals and quarantine them with their family.

Covid-19 was hardly avoidable. It's like when geophysical models predict that there will be a major earthquake in a city in the next few years and no one can evacuate a population because the probability of there being an earthquake at any time in the next decade is 90%. However, it is possible to apply certain mobility restrictions with the early identification of infected individuals and their subsequent isolation.

How is an epidemic extinguished?

The only way we can eradicate an epidemic is for the population to be immune. The most obvious and direct way to achieve population immunity is to have a vaccine and, over time, the epidemic will die out. Another option is to have group or collective immunity, which is the protection that the population has against an infection due to the presence of a high percentage of immune individuals within it or, in other words, the resistance of a group. to an infection, to which a large proportion of individuals have immunity. For the specific case of covid-19, it is estimated that this proportion is around two-thirds of the population.

How does the R Number help quantify the rate of new infections?

There is a way to quantify the rate of new infections for each infectious subject. This is a parameter in epidemiology known as the reproductive number R, which is defined as the number of secondary infections generated, on average, by a typical infectious individual when introduced into a totally susceptible population. If R is greater than 1, it means that each infected person, on average, produces more than one new infected person in the population, so the number of infected people per unit of time will grow. On the contrary, if R is less than 1, we will have a smaller number of new infections in successive times. That is, if R is less than 1, the epidemic will disappear. When? It depends on its value and how many infected we have at the moment when R falls below 1.

Is herd immunity far away in covid-19?

Ironically, trying to contain the contagion with severe confinement and social distancing measures means that we do not achieve the herd immunity necessary to ensure that, when confinement measures are relaxed, the R value does not rise above 1 again and we return to the starting box. According to the recently published results of the serology study, we are far from herd immunity, almost as at the beginning.

Is a second epidemic wave likely?

Yes, because we do not have herd immunity, a second epidemic wave is very likely. But we cannot let the virus circulate freely and allow people to become infected so that they acquire immunity in an uncontrolled manner, as the health system would collapse, causing the unnecessary loss of human life.

How to control this potential second wave and avoid saturation of the health system?

If we cannot obtain immunity, we must act at the source, that is, cut the chains of transmission and prevent infectious individuals from infecting susceptible ones.

What can we do to avoid returning to square one now that confinement is relaxing?

The results of an international study in which we have simulated the evolution of the epidemic in a real population, specifically in the Boston area, show that, indeed, there are safe ways to relax the strict confinement measures that were imposed. To do this, it is necessary to increase the capacity to perform tests, both in quantity and speed. It is essential to detect the greatest number of people who could infect others, that is, identify the origin of the transmission chains. The work shows that we do not need to test everyone, but rather identify only half of symptomatic individuals, on average, two days after they show symptoms. Once they have been located, they and the members of their family must remain in quarantine.

Would we avoid a second wave of the pandemic?

With this measure we would not avoid a possible second wave, but we would significantly reduce the intensity of the epidemic peak.

Would testing everyone solve anything?

Without a doubt, but it is unfeasible. We should isolate those who are positive and those who live with them for two weeks. In addition, contacts of identified infectious individuals would need to be traced.

Why is contact tracing useful?

Because we would have the possibility of controlling the epidemic outbreak, distributing new infections over time, so that health services can support the load and thus avoiding a possible collapse. This time gained from the disease would serve to gradually achieve collective immunity safely or even for science to discover new more effective drugs against SARS-CoV-2 or to develop a specific vaccine on the horizon. something further away.

Could it be enough to confine a part of the population?

According to our study we only need to trace between 20% and 40% of the contacts of symptomatic positives, which means that, on average, we would quarantine at most around 10% of the population. This percentage may seem high, but it is very low if we compare it to having 80-90% of the population confined as in the most restrictive phase of confinement.

The population of Boston, in a model of the evolution of the pandemic

The study 'Modeling the impact of social distancing, testing, contact tracing and household quarantine on second-wave scenarios of the covid-19 epidemic' used real mobility data from mobile phone users in Boston (USA) provided by the Data for good program of Cuebiq Inc., a company that collects user locations and aggregates them anonymously. This made it possible to generate a synthetic population statistically equivalent to the real one and to be able to simulate, with a high degree of realism, the effect of social distancing measures, for example, eliminating interactions that would have occurred in places that remain closed during confinement. . This population model is coupled to an epidemiological one, which allows us to simulate the evolution of covid-19 in this synthetic population and, more importantly, to model hypothetical scenarios and action strategies to contain the epidemic and avoid the second wave. The results of the study show that, indeed, there are safe ways to relax the strict confinement measures that were imposed. To do this, it is necessary to increase the capacity to perform tests, both in quantity and speed.

Who investigates it

Alberto Aleta, from the ISI Foundation and external member of the Institute of Biocomputing and Physics of Complex Systems (BIFI) of the University of Zaragoza, and Yamir Moreno, theoretical physicist, head of the Complex Networks and Systems Group (COSNET), have participated in this research. ) and director of the BIFI. Researchers from the Carlos III University of Madrid, the Massachusetts Institute of Technology (MIT) and Northeastern University in Boston, among other institutions, have also collaborated, demonstrating that there are viable and sustainable exit scenarios. The study can be consulted here.

What can we learn from South Korea?

South Korea has managed to bring the outbreak under control, despite not adopting the strictest measures that were implemented in most European countries. Because? Because they were prepared. South Korea faced MERS in 2015. It was their great test for this epidemic, because as a result of their experience, they modified laws, trained personnel, developed new technologies and educated the population. In the future, we need to increase our ability to respond to these types of global threats, because if one thing is certain, it is that this will not be the last time we face a similar situation. We must invest in health and epidemiological surveillance, but also educate the population on the practices that should be adopted and update legislation to be able to react quickly. The world is evolving and we must evolve accordingly.

Yamir Moreno Group of Networks and Complex Systems (COSNET) of the Institute of Biocomputing and Physics of Complex Systems (BIFI) of the University of Zaragoza

This section is carried out in collaboration with the Scientific Culture Unit of the University of Zaragoza