[Infographic] This is a scenario where Covid-19 could end up as fast as possible with the lowest death rate

As governments and health agencies around the world are making every effort to prevent the spread of the Covid-19 epidemic, some researchers have gathered data to build a scenario of end of the epidemic. sick.

Based on the number of new infections per day, the number of deaths and the effectiveness of measures such as quarantine isolation, they can build a computer model to foresee the future of the disease, how it is done. grow, weaken and come to an end.

One of those scenarios shows that the number of infections can peak within this month. The following article presents a simplified model that predicts how the Covid-19 epidemic could end up as fast as possible with the lowest mortality.

But to understand these models, before you see you need to know the basic parameters of it.

The simulations below were performed in a population of 4,000 people. Infected people are modeled with a red dot. Deaths are indicated by black dots. And those who are cured will be the blue dot.

You can monitor the progression and spread of the disease by the day it is displayed. There are two parameters that you need to pay attention to.

It is the basic transmission coefficient (R0) – simply put, that is the average number of people who will catch the disease from a single previous infection. With Covid-19, the scientists said its R0 was 2,3. That means that on average, one infected person will infect 2.3 others.

In addition to R0 being a fixed average, the rate of infection of the disease is also expressed as Re – the effective transmission coefficient, which is also equal to the average number of people infected from a new person, but only at one certain times in translation.

Understandably, Re is a variation of R0 and it always changes until it is less than 1 – which means that an infected person is unlikely to infect anyone anymore, the disease will then cease. .

* Stay up to date on the latest Covid-19 translations from around the world here.

And while watching, you might ask why there are white holes in this model, which represent a segment of the lucky population who has never been infected by anyone. There are many possible causes, but they are basically not in contact with infected people.

Some people like doctors or nurses can come in contact with hundreds of sick people a day during an epidemic. Some people are at greater risk of interacting with others who are stewardesses, receptionists, or cashiers. But in the population there are also people who have little contact, they just stay home and don’t go anywhere until the disease is over, and so they will be protected.

Simulated translation of Covid-19

Now, let’s start with a scenario simulation of a population of 4,000 people in Covid-19. The basic transmission coefficient is 2.3 and the whole population is very sensitive. They are not protected by any means including quarantining suspected people, washing their hands, wearing masks or getting vaccines:

You can see, in this scenario, the disease spread quickly, it peaked after about two months, on day 70 with more than 1,000 people infected. But also at the peak of that epidemic, the effective Re transmission ratio decreased from 2.3 to 1.1.

It then continued to decline to 0.5 on day 77, resulting in a drop in the number of cases to just one third. The epidemic ended on day 84 when no one was infectious to others, Re now was 0.3.

But it is not because we have conquered the disease, but because almost everyone is infected with the virus. A rate of 2.25% of the population died and all other infected people were cured creating community immunity that protected a small number of other populations safely.

It is worth mentioning that, this scenario is applied to a closed population of 4,000 people. If these people are not quarantined, an infected person may move away and create a similar pattern of infection in the new population.

The epidemic will only end when nearly everyone in every population is infected, or they will either die or be cured to create community immunity. The total number of deaths would then be enormous.

Compared to a high outbreak of disease …

“The higher the R0 index, the more rapid an outbreak will occur, ” said David Fisman, an epidemiologist and doctor of infectious diseases at the University of Toronto School of Public Health. Together with his colleague Ashleigh R. Tuite, Fisman built a model for calculating the R0 coefficient for Covid-19 translation.

However, the R0 of this disease is actually very low to cause a high outbreak. In order to know how a high outbreak would occur, you had to compare it to measles, with an R0 coefficient of up to 18. That means that one person with measles will average 18 others on average. See what happened then:

To prevent such high outbreaks, people need a powerful weapon: vaccines. The World Health Organization says vaccination is an effective measure against measles. However, anti-vaccine movements in recent years have led to a decrease in the number of children vaccinated against measles. In 2018, only 86% of children under 2 years of age worldwide were vaccinated against measles.

And the measles epidemic then hit Europe and the US, where measles was once a disease nearly wiped out. The scenario you just saw above is a population of 4,000 unvaccinated. It only took 48 days for it to infect the whole and kill 47 people.

… and a less contagious disease with a high mortality rate

There is a fact that there are diseases with low R0, but their mortality is high. Those are the dangerous diseases that have caused a global pandemic like Ebola.

During the 2015 outbreak, Ebola’s transmission coefficient was less than 2, because blood-borne disease would be more difficult than respiratory transmission. However, Ebola can kill up to half of people infected with it, the mortality rate is now much higher than Covid-19 or measles.

This simulation shows that a population of 4,000 is also not protected by any measures. The disease will also end only if it infects almost everyone, but because the lower R0, it will take longer. It was not until the 224 day that the epidemic ended, when Re fell to 0.4.

Slow the spread by protecting yourself

Going back to Covid-19, the scenario below also simulates a population of 4,000. However, one third of them practiced protective measures (modeled with yellow dots) such as washing their hands frequently, wearing masks when interacting with suspected people.

As a result, the spread of Covid-19 has slowed. Although the number of infected people is still high, we have had more time to prepare and deal with it. The number of deaths thus decreased to only half, 50 people corresponding to 1.25%.

Best scenario: Effective quarantine and isolation

This is exactly what China and the whole world are trying to accomplish. This scenario is what we are aiming for. Infected and suspected people are effectively and thoroughly isolated, epidemic cities are cordoned off so as not to spread the disease.

Although the R0 coefficient in the population is still 2.4, thanks to quarantine isolation, we were able to prevent its spread. At this point, the disease can be quickly extinguished, after the first few cases are discovered and cured.

But quarantine and quarantine isolation must be done from start to finish, until no longer anyone in this circle can become ill or recur to infect the outside population anymore.

In fact, when the disease broke out in Wuhan in China, the quarantine and blockade were made too late, creating the opportunity for Covid-19 to spread to other places.

But we can hope that, with the best preparation and vigilance, from now on, the hot spots will be able to effectively quarantine suspected people under this scenario. If so, we are still highly likely to win against Covid-19.

Refer to Washingtonpost