Basic reproduction number
| Disease | Transmission | |
| Measles | Aerosol | 12–18 |
| Chickenpox | Aerosol | 10–12 |
| Mumps | Respiratory droplets | 10–12 |
| Polio | Fecal–oral route | 5–7 |
| Rubella | Respiratory droplets | 5–7 |
| Pertussis | Respiratory droplets | 5.5 |
| Smallpox | Respiratory droplets | 3.5–6 |
| HIV/AIDS | Body fluids | 2–5 |
| SARS | Respiratory droplets | 0.19–1.08 |
| SARS-CoV-2/COVID-19 | Respiratory droplets | 2–6 |
| Common cold | Respiratory droplets | 2–3 |
| Diphtheria | Saliva | 1.7–4.3 |
| Influenza | Respiratory droplets | 1.4–2.8 |
| Ebola | Body fluids | 1.5–1.9 |
| Influenza | Respiratory droplets | 1.4–1.6 |
| Influenza | Respiratory droplets | 0.9–2.1 |
| MERS | Respiratory droplets | 0.3–0.8 |
in the context of the COVID-19 pandemic.
In epidemiology, the basic reproduction number, or basic reproductive number, denoted , of an infection can be thought of as the expected number of cases directly generated by one case in a population where all individuals are susceptible to infection. The definition describes the state where no other individuals are infected or immunized. Some definitions, such as that of the Australian Department of Health, add absence of "any deliberate intervention in disease transmission". The basic reproduction number is not to be confused with the effective reproduction number , which is the number of cases generated in the current state of a population, which does not have to be the uninfected state. Also, it is important to note that is a dimensionless number and not a rate, which would have units of time−1, or units of time like doubling time.
is not a biological constant for a pathogen as it is also affected by other factors such as environmental conditions and the behaviour of the infected population. Furthermore values are usually estimated from mathematical models, and the estimated values are dependent on the model used and values of other parameters. Thus values given in the literature only make sense in the given context and it is recommended not to use obsolete values or compare values based on different models. does not by itself give an estimate of how fast an infection spreads in the population.
The most important uses of are determining if an emerging infectious disease can spread in a population and determining what proportion of the population should be immunized through vaccination to eradicate a disease. In commonly used infection models, when the infection will be able to start spreading in a population, but not if. Generally, the larger the value of, the harder it is to control the epidemic. For simple models, the proportion of the population that needs to be effectively immunized to prevent sustained spread of the infection has to be larger than. Conversely, the proportion of the population that remains susceptible to infection in the endemic equilibrium is.
The basic reproduction number is affected by several factors, including the duration of infectivity of affected people, the infectiousness of the microorganism, and the number of susceptible people in the population that the infected people contact.
History
The roots of the basic reproduction concept can be traced through the work of Ronald Ross, Alfred Lotka and others, but its first modern application in epidemiology was by George MacDonald in 1952, who constructed population models of the spread of malaria. In his work he called the quantity basic reproduction rate and denoted it by. Calling the quantity a rate can be misleading, insofar as "rate" can then be misinterpreted as a number per unit of time. "Number" or "ratio" is now preferred.Definitions in specific cases
Contact rate and infectious period
Suppose that infectious individuals make an average of infection-producing contacts per unit time, with a mean infectious period of. Then the basic reproduction number is:This simple formula suggests different ways of reducing and ultimately infection propagation. It is possible to decrease the number of infection-producing contacts per unit time by reducing the number of contacts per unit time or the proportion of contacts that produces infection. It is also possible to decrease the infectious period by finding and then isolating, treating or eliminating infectious individuals as soon as possible.
With varying latent periods
Latent period is the transition time between contagion event and disease manifestation. In cases of diseases with varying latent periods, the basic reproduction number can be calculated as the sum of the reproduction numbers for each transition time into the disease. An example of this is tuberculosis. Blower and coauthors calculated from a simple model of TB the following reproduction number:In their model, it is assumed that the infected individuals can develop active TB by either direct progression considered above as FAST tuberculosis or endogenous reactivation considered above as SLOW tuberculosis.
Heterogeneous populations
In populations that are not homogeneous, the definition of is more subtle. The definition must account for the fact that a typical infected individual may not be an average individual. As an extreme example, consider a population in which a small portion of the individuals mix fully with one another while the remaining individuals are all isolated. A disease may be able to spread in the fully mixed portion even though a randomly selected individual would lead to fewer than one secondary case. This is because the typical infected individual is in the fully mixed portion and thus is able to successfully cause infections. In general, if the individuals infected early in an epidemic are on average either more likely or less likely to transmit the infection than individuals infected late in the epidemic, then the computation of must account for this difference. An appropriate definition for in this case is "the expected number of secondary cases produced by a typical infected individual early in an epidemic".Estimation methods
During an epidemic, typically the number of diagnosed infections over time is known. In the early stages of an epidemic, growth is exponential, with a logarithmic growth rateFor exponential growth, can be interpreted as the cumulative number of diagnoses or the present number of infection cases; the logarithmic growth rate is the same for either definition. In order to estimate, assumptions are necessary about the time delay between infection and diagnosis and the time between infection and starting to be infectious.
In exponential growth, is related to the doubling time as
Simple model
If an individual, after getting infected, infects exactly new individuals only after exactly a time has passed, then the number of infectious individuals over time grows asor
The underlying matching differential equation is
or
In this case, or.
For example, with and, we would find.
If is time dependent
showing that it may be important to keep below 0, time-averaged, to avoid exponential growth.
Latent infectious period, isolation after diagnosis
In this model, an individual infection has the following stages:- Exposed: an individual is infected, but has no symptoms and does not yet infect others. The average duration of the exposed state is.
- Latent infectious: an individual is infected, has no symptoms, but does infect others. The average duration of the latent infectious state is. The individual infects other individuals during this period.
- isolation after diagnosis: measures are taken to prevent further infections, for example by isolating the infected person.
This estimation method has been applied to COVID-19 and SARS. It follows from the differential equation for the number of exposed individuals and the number of latent infectious individuals,
The largest eigenvalue of the matrix is the logarithmic growth rate, which can be solved for.
In the special case, this model results in, which is different from the simple model above. For example, with the same values and, we would find, rather than the true value of. The difference is due to a subtle difference in the underlying growth model; the matrix equation above assumes that newly infected patients are currently already contributing to infections, while in fact infections only occur due the number infected at ago. A more correct treatment would require the use of delay differential equations.
Effective reproduction number
In reality, varying proportions of the population are immune to any given disease at any given time. To account for this, the effective reproduction number is used, also written as, or the average number of new infections caused by a single infected individual at time t in the partially susceptible population. It can be found by multiplying by the fraction S of the population that is susceptible. When the fraction of the population that is immune increases so much that drops below 1, "herd immunity" has been achieved and the number of cases occurring in the population will gradually decrease to zero.Limitations of
Use of in the popular press has led to misunderstandings and distortions of its meaning. can be calculated from many different mathematical models. Each of these can give a different estimate of, which needs to be interpreted in the context of that model. Therefore, the contagiousness of different infectious agents cannot be compared without recalculating with invariant assumptions. values for past outbreaks might not be valid for current outbreaks of the same disease. Generally speaking, can be used as a threshold, even if calculated with different methods: if, the outbreak will die out, and if, the outbreak will expand. In some cases, for some models, values of can still lead to self-perpetuating outbreaks. This is particularly problematic if there are intermediate vectors between hosts, such as malaria. Therefore, comparisons between values from the "Values of of well-known infectious diseases" table should be conducted with caution.Although cannot be modified through vaccination or other changes in population susceptibility, it can be modified by physical distancing and other public policy or social interventions. Collectively, most of these are considered nonpharmacological interventions. This creates some confusion, because is not a constant; whereas most mathematical parameters with "naught" subscripts are constants.
depends on many factors, many of which need to be estimated. Each of these factors adds to uncertainty in estimates of. Many of these factors are not important for informing public policy. Therefore, public policy may be better served by metrics similar to, but which are more straightforward to estimate, such as doubling time or half-life.
Methods used to calculate include the survival function, rearranging the largest eigenvalue of the Jacobian matrix, the next-generation method, calculations from the intrinsic growth rate, existence of the endemic equilibrium, the number of susceptibles at the endemic equilibrium, the average age of infection and the final size equation. Few of these methods agree with one another, even when starting with the same system of differential equations. Even fewer actually calculate the average number of secondary infections. Since is rarely observed in the field and is usually calculated via a mathematical model, this severely limits its usefulness.