Sunday, December 5

How contagious is the Delta variant of Covid-19? See how the coronavirus can spread through a population and how countries flatten the curve | World News


As the global effort to limit the impact of the pandemic accelerates, observe how subtle changes in social behavior or the level of contagion of the virus can affect the battle to stop its spread.

This interactive feature was originally published in April 2020 and was updated on August 27, 2021 to compare the infectivity of the Delta coronavirus variant.

An important characteristic of viruses and other pathogens is how contagious they are.

This is measured in a number of ways. A key measure is the R0, or basic reproduction number, which indicates how many new cases an infected person generates.

For an R0 of three, we would expect each new case of a disease to produce three other infections.

This is not just a measure of the inherent infectivity of a disease. It also depends on other factors, including the rate of contact within a population and the length of the infectious period. It is a value that depends on the situation, so in one city the R0 may be higher and in another lower. It also assumes that the entire population is susceptible to the disease.

The first studies on the behavior of Covid-19 in Wuhan estimated that the average R0 was between 2.2 and 2.7, while smaller-scale outbreaks such as the one that occurred aboard the Diamond princess it had an R0 estimated at 2.2. Other studies suggest that values ​​such as as low as 1.5 and as high as 3.8 were possible.

So what does that look like and how does it compare to other diseases?

Here, you can see a
little red circle representing an infected person.

With an R0 of 2.6, the initial infection results in another two or three cases …

… and those people transmit the infection to two or three others

…And so on. In the case of the Covid-19 virus, each new phase takes on average between five and six days.

Here we can see how the spread is drastically reduced by
isolate just one individual.

How does Covid-19 compare to other diseases?

Here, you can see the same spread patterns generated based on R0 for a variety of pathogens in a variety of situations, ranging from Spanish flu to highly contagious measles, for which R0 has been estimated to range from 12 to 18. .

But R0 is not the only important number. The effective reproduction number, R, is a value that takes into account the susceptibility of the population.

Here, we start with a single person wearing a
infection in a hypothetical population of 1000
not infected people.

With any R value greater than 1, and a population that is fully susceptible, the infection will spread everywhere.

But if some people are not susceptible to infection, due to immunity through vaccination, because they have been previously infected or for other biological reasons, or if transmission is slowed because part of the population is sick.
isolated, then the effective R value becomes lower and the dispersion is incomplete and slows down.

If the effective R is reduced below one, the spread can be stopped. In general, reducing the R will also allow healthcare systems to better cope with the influx of patients.

Another variable is how deadly the disease is. Some diseases, such as Ebola, have a lower R0 than other diseases, but a high mortality rate. Here the
purple circles indicate a death after the infection has traveled through the population.

The estimated death rate for Covid-19 varies again depending on the location and situation, and particularly on the level of testing that is performed. In the early outbreak in China, for example, a report placed the fatality rate at 2.3%. In South Korea, the virus has an estimated fatality rate of 1.2%.

Here, you can set the thresholds for different values ​​and see the result using our simplified model. The reality of pandemics is much more complex, but this demonstrates the basic concepts behind the spread of disease. This model uses a hypothetical population of 1,000 people to better visualize the ratio of infections and deaths, and assumes that everyone can come into contact with everyone else in the population.

R0:

The basic reproductive number (
R0) indicates how many new cases an infected person generates

Fatality rate:

Fatality rate is the percentage of deaths caused by a disease compared to the total number of people with the disease

Susceptibility:

In this model
Susceptibility indicates whether a person can become infected. This could be due to vaccination or immunity acquired through previous infection.

Insulation rate: 0%

the
isolation rate is the percentage of the population that is isolated or quarantined

Upon

Choose a case study or use the sliders to watch a scenario unfold

Case study:
select a scenario

Population of 1000 people

Case studies (Covid-19)

Delta variant
1

Diamond princess
2

Without intervention
3

Strong intervention
4

Other diseases

Measles
5

Ebola
6

Pandemics in the cinema

Contagion
7

Notes

The isolation value is not used for most case studies, as isolation data is often not available.

  1. The Delta variant case study aims to show a hypothetical scenario in which no isolation measures were applied.
  2. The Diamond Princess case study aims to visualize the proportional outcome of Covid-19 infections aboard the Diamond Princess. The proportion of the population susceptible to 18% based on an attack rate of 18% (696 cases / 3711 passengers in total) and is not indicative of the actual susceptibility figure (which could be closer to 100%). Isolation is not used because data is not available. The mortality rate is set at 1.8% based on the current total of deaths (13) over cases (696). The R0 used is Zhang et al. 2020.
  3. The hands-off case study is an example that uses figures from various sources to show a hypothetical scenario without isolation measures. The susceptible proportion of the population is set at 70% based on comments here and is not indicative of the actual susceptibility figure (which could be closer to 100%). The death rate is set at 1% according to various estimates of the case fatality rate in China (Verity et al. 2020, Wighton et al. 2020). The R0 used is 2.7.
  4. The strong intervention case study is an example that uses figures from various sources to show a hypothetical scenario with measures of social distancing and isolation covering 40% of the population. The susceptible proportion of the population is set at 70% based on comments here and is not indicative of the actual susceptibility figure (which could be closer to 100%). The mortality rate is set at 0.66%, lower than in the no-intervention scenario, as it assumes that health care would be better suited to a reduced case load. The R0 used is 2.7.
  5. The measles case study uses values ​​of The information is beautiful and the Centers for Disease Control and Prevention. The actual R0 for measles can vary widely depending on the country and whether studies were conducted before or after the introduction of measles vaccine. The fatality rates are also very broad depending on the country and outbreak situation.
  6. The Ebola case study uses an R0 of Taylor et al 2016, a fatality rate from WHO and sets the susceptibility to 30% based on a 30% attack rate (actual susceptibility may differ).
  7. The Contagion case study uses the values ​​mentioned in the film: R0 of 4, mortality rate of 25% to 30% and “will infect 1 in 12 people on the planet” (8.333%).

R0 references for other diseases: Sars, spanish flu, Chickenpox.




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