Watch how subtle changes in social behaviour or the level of contagiousness of Covid-19 can affect the battle to stop its spread
This interactive feature was originally published in April 2020, it was updated on 27 August 2021 to compare the infectiousness of the Delta coronavirus variant.
One important characteristic of viruses and other pathogens is how contagious they are.
This is measured in a few ways. One key measure is the R0, or basic reproduction number, which indicates how many new cases one 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 infectiousness of a disease. It also depends on other factors, including the rate of contact within a population and the duration of the infectious period. It’s a situation-dependent value, so in one city the R0 might be higher and in another lower. It also assumes that the entire population is susceptible to the disease.
Early studies of the behaviour of Covid-19 in Wuhan estimated the average R0 to be between 2.2 and 2.7, while smaller-scale outbreaks such as the one that occurred onboard the Diamond Princess had an R0 estimated at 2.2. Other studies suggest that values as low as 1.5 and as high as 3.8 were possible.
So what does that look like, and how does it compare with other diseases?
Here, you can see a
small red circle representing one infected person.
With a R0 of 2.6 the initial infection results in two to three other cases…
…and those people pass the infection on to two to 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
isolating just one individual.
How does Covid-19 compare with other diseases?
Here, you can see the same spread patterns generated based on the R0 for a range of pathogens in a range of situations, ranging from Spanish flu to the highly contagious measles, for which R0 has been estimated to range between 12 and 18.
But the 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 carrying an
infection in a hypothetical population of 1,000
With any R value greater than 1, and a population that is entirely susceptible, the infection will spread throughout.
But if some people are not susceptible to infection – because of immunity through vaccination, because they have previously been infected or because of other biological reasons – or if transmission is curbed due to part of the population being
isolated, then the effective R value becomes lower, and the spread is incomplete, and slowed.
If the effective R is reduced below one, the spread can be halted. Reducing the R generally will also allow healthcare systems to better cope with the influx of patients.
Another variable is how deadly the disease is. Some diseases, like Ebola, have a lower R0 than other diseases but a high fatality rate. Here, the
purple circles indicate a death after the infection has moved through the population.
The estimated fatality rate for Covid-19 varies again depending on the location and situation, and particularly on the level of testing being conducted. In the early outbreak in China, for example, one report put the case fatality rate at 2.3%. In South Korea the virus has an estimated case 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 far more complex but this does demonstrate the basic concepts underlying the spread of diseases. This model uses a hypothetical population of 1,000 people to better visualise the proportion of infections and deaths, and assumes that every person can come into contact with every other person in the population.
The basic reproductive number (
R0) indicates how many new cases one infected person generates
Fatality rate is the percentage of deaths caused by a disease compared to the total number of people with the disease
In this model
Susceptibility indicates if a person can become infected. This could be due to vaccination or immunity acquired through prior infection
isolation rate is the percentage of the population which is isolated or quarantined
Choose a case study or use the sliders to see a scenario play out
Population of 1000 people
Pandemics in the movies
The isolation value is not used for most of the case studies as isolation data is not usually available.
- The Delta variant case study is intended to show a hypothetical scenario where no isolation measures were in place.
- The Diamond Princess case study is intended to visualise the proportional outcome of Covid-19 infections onboard the Diamond Princess. The susceptible proportion of the population to 18% based on an attack rate of 18% (696 cases / 3711 total passengers) and is not indicative of the true susceptibility figure (which might be closer to 100%). Isolation is not used as there is no data available. The death rate is set at 1.8% based on the current total of deaths (13) over cases (696). The R0 used is from Zhang et al. 2020.
- The no intervention case study is an example that uses figures from various sources to show a hypothetical scenario with no isolation measures. The susceptible proportion of the population is set to 70% based on comments here and is not indicative of the true susceptibility figure (which might be closer to 100%). The death rate is set at 1% based on various estimates of the case fataliy ratio from cases in China (Verity et al. 2020, Wighton et al. 2020). The R0 used is 2.7.
- The strong intervention case study is an example that uses figures from various sources to show a hypothetical scenario with social distancing and isolation measures covering 40% of the population. The susceptible proportion of the population is set to 70% based on comments here and is not indicative of the true susceptibility figure (which might be closer to 100%). The death rate is set at 0.66%, lower than the no intervention scenario as it assumes health care would cope better with reduced case load. The R0 used is 2.7.
- The measles case study uses values from Information is Beautiful and the CDC. Actual R0 for measles can vary widely depending on the country and if studies were done before or after the introduction of the measles vaccine. Case fatality ratios also very widely depending on the country and outbreak situation.
- The ebola case study uses a R0 from Taylor et al 2016, a case fatality rate from the WHO and sets susceptibility to 30% based on an attack rate of 30% (actual susceptibility might differ).
- The Contagion case study uses values mentioned in the movie: R0 of 4, fatality rate of 25% to 30%, and “will infect 1 in 12 people on the planet” (8.333%).