Thursday, February 18, 2021
What is needed to ultimately stop the spread of the coronavirus in the United States? To answer that question, we created a simulation of a simulated disease called SIMVID-19.
upper[シミュレーションの実行]Click to see how the disease can spread throughout the population and how the disease can stop progressing as the level of vaccination increases.
It was chosen to simulate a fake illness because there are too many unknowns to simulate the course of COVID-19. There are common characteristics in how the infection spreads. When a sufficient number of people acquire immunity by vaccination or innate immunity, the population achieves herd immunity. The disease stops spreading efficiently and begins to disappear.
The time it takes a community to reach herd immunity depends on several factors. The following scenarios show some of the key ones.
1) More infectious variants take over
What if the more infectious strain of SIMVID-19 is dominant in the community? Simulations show that even with a 75% vaccination rate, many people in an imaginary community of 400 are infected.
Higher vaccination rates appear to be required to achieve herd immunity against more infectious strains of SIMVID-19. This is a real concern about new variants of the coronavirus, including the first strain detected in the United Kingdom, and is now spreading rapidly in the United States.
As you can see each time you rerun these simulations, there is always an element of opportunity in how the disease spreads.
2) The population is already badly exposed
The pandemic has hit some communities more than others and has given some innate immunity. This scenario assumes that many people are already immune to SIMVID-19 through previous infections.
In this figure, it was determined that SIMVID-19 infection would continue during the simulation. You can see how high levels of immunity and high vaccination rates can help the community reach herd immunity. In the case of coronavirus, it is not known what percentage of infected people are immune or how long that immunity lasts.
3) Low initial immunity level in the population
In this scenario, you can see how SIMVID-19 can easily spread in a population with few people immunized at the onset of development. Herd immunity is difficult to achieve in this scenario and it is important to increase the level of vaccination. With regard to the coronavirus, existing immunity levels are low in some communities, and scientists believe that the immunity that people have may weaken over time.
Key points of COVID-19
In our SIMVID-19 scenario, we were able to kick a fictitious disease with a 75% vaccination rate, except in one more infectious scenario. In order for the United States to control the outbreak of the coronavirus through herd immunity, scientists predict that somewhere between 70% and 85% of the population must be immune.
Unfortunately, there are many unanswered questions about the coronavirus, and it is not possible to accurately predict the required vaccination coverage.
For example, vaccinated people may be at risk of spreading the virus, even if the vaccine protects them from serious illness. Also, because no vaccine is 100% effective, some vaccinated people may still get sick and take over. This can be seen in our model as dark gray vaccinated individuals sometimes turn infected pink.
And we know that Probably 120 million Americans I’m already infected, so I don’t know how long the immunity will last after infection. Finally, there are concerns that some variants that are currently widespread around the world will reduce the effectiveness of the vaccine.
One thing that is clear from this simulation (and real life) is that the sooner the population is protected by vaccination, the better.
Simulation SIMVID-19 For the sake of explanation, we will simplify many of the details about how new coronavirus-like diseases spread. For example, each cell in a hexagonal grid has up to six other “people” in contact, so the chances of an infection spreading from one cell to another are constant. Vaccination reduces that chance, but it does not completely eliminate it (as in real life). This means that if you rerun the simulation many times, you may get lucky or unlucky roles. To prevent these outliers from causing confusion, the first simulation performed in each scenario starts with the same configuration.
Daniel Wood and Carmel Wroth contributed to this report.
Copyright 2021 NPR. For more information, please visit https://www.npr.org.
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