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# Basic reproduction number

Values of R0 of well-known infectious diseases[1]
Disease Transmission R0
Measles Airborne 12–18[2]
Polio Fecal–oral route 5–7
Rubella Airborne droplet 5–7
Mumps Airborne droplet 4–7
Pertussis Airborne droplet 5.5[3]
Smallpox Airborne droplet 3.5–6[4]
HIV/AIDS Body fluids 2–5
SARS Airborne droplet 2–5[5]
Diphtheria Saliva 1.7–4.3[6]
COVID-19 Airborne droplet 1.4–3.9[7][8][9][10][11]
Influenza
(1918 pandemic strain)
Airborne droplet 1.4–2.8[12]
Ebola
(2014 Ebola outbreak)
Body fluids 1.5–2.5[13]
Influenza
(2009 pandemic strain)
Airborne droplet 1.4–1.6[14]
Influenza
(seasonal strains)
Airborne droplet 0.9–2.1[14]
MERS Airborne droplet 0.3–0.8[15]
A video discussing the basic reproduction number (at about 4 min) and case fatality rate in the context of the 2019–20 coronavirus pandemic.

In epidemiology, the basic reproduction number (sometimes called basic reproductive ratio, or incorrectly basic reproductive rate, and denoted R0, pronounced R nought or R zero[16]) 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.[17] The definition describes the state where no other individuals are infected or immunized (naturally or through vaccination). Some definitions, such as that of the Australian Department of Health, add absence of "any deliberate intervention in disease transmission".[18] The basic reproduction number is not to be confused with the effective reproduction number R which is the number of cases generated in the current state of a population, which does not have to be the uninfected state. By definition R0 cannot be modified through vaccination campaigns. Also it is important to note that R0 is a dimensionless number and not a rate, which would have units of time[19] like doubling time[20].

R0 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 R0 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.[21] R0 does not by itself give an estimate of how fast an infection spreads in the population.

The most important uses of R0 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 R0 > 1 the infection will be able to start spreading in a population, but not if R0 < 1. Generally, the larger the value of R0, the harder it is to control the epidemic. For simple models, the proportion of the population that needs to be effectively immunized (meaning not susceptible to infection) to prevent sustained spread of the infection has to be larger than 1 − 1/R0.[22] Conversely, the proportion of the population that remains non-susceptible to infection in the endemic equilibrium is 1/R0.

The basic reproduction number is affected by several factors including the duration of infectivity of affected patients, the infectiousness of the organism, and the number of susceptible people in the population that the affected patients are in contact with.

## History

The roots of the basic reproduction concept can be traced through the work of Ronald Ross, Alfred Lotka and others,[23] but its first modern application in epidemiology was by George MacDonald in 1952,[24] who constructed population models of the spread of malaria. In his work he called the quantity basic reproduction rate and denoted it by Z0. Calling the quantity a "rate" can be misleading, insofar as it can be interpreted as number per unit of time. The expressions number or ratio are now preferred.

## Definitions in specific cases

### Reproductive number as it relates to contact rate and infectious period

R0 is the average number of people infected from one other person, for example, Ebola has an R0 of two, so on average, a person who has Ebola will pass it on to two other people.

Say that an infectious individual makes β contacts per unit time producing new infections with a mean infectious period of γ. Therefore, the basic reproduction number is

$R_0 = \beta\,\gamma$

This simple formula suggests different ways of reducing R0 and ultimately infection propagation. It is possible to decrease the number of infection producing contacts per unit time β by reducing the number of contacts per unit time (for example staying at home if the infection requires contact with others to propagate) or the proportion of contacts that produces infection (for example wearing some sort of protective equipment). It is also possible to decrease the infectious period γ by finding and then isolating, treating or eliminating (as is often the case with animals) infectious individuals as soon as possible.

### With varying latent periods

In cases of diseases with varying latent periods, the basic reproduction number can be calculated as the sum of the reproduction number for each transition time into the disease. An example of this is tuberculosis. Blower et al.[25] calculated from a simple model of TB the following reproduction number:

$R_0 = R_0^{\mathrm{FAST}} + R_0^{\mathrm{SLOW}}$

In their model, it is assumed that the infected individuals can develop active TB by either direct progression (the disease develops immediately after infection) considered above as FAST tuberculosis or endogenous reactivation (the disease develops years after the infection) considered above as SLOW tuberculosis.

### Heterogeneous populations

In populations that are not homogeneous, the definition of R0 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 who become infected early in an epidemic may be more (or less) likely to transmit than a randomly chosen individual late in the epidemic, then our computation of R0 must account for this tendency. An appropriate definition for R0 in this case is "the expected number of secondary cases produced by a typical infected individual early in an epidemic".[26]

## Estimation methods

During an epidemic, typically the number of diagnosed infections $N(t)$ over time $t$ is known. In the early stages of an epidemic, growth is exponential, with a logarithmic growth rate[citation needed]

$K = \frac{d\ln(N)}{dt}.$

For exponential growth, $N$ can be interpreted as the cumulative number of diagnoses (including individuals who have recovered) or the present number of diagnosed patients; the logarithmic growth rate is the same for either definition. In order to estimate $R_0$, assumptions are necessary about the time delay between infection and diagnosis and the time between infection and starting to be infectious.

### Latent infectious period, isolation after diagnosis

In this model, an individual infection has the following stages:

1. Exposed: an individual is infected, but has no symptoms and does not yet infect others. The duration of the exposed state is $\tau_E$.
2. Latent infectious: an individual is infected, has no symptoms, but does infect others. The duration of the latent infectious state is $\tau_I$. The individual infects $R_0$ other individuals during this period.
3. isolation after diagnosis: measures are taken to prevent further infections, for example by isolating the patient.

This is a SEIR model and R0 may be written in the following form[27]

$R_0 = 1 + K(\tau_E+\tau_I) + K^2\tau_E\tau_I.$

This estimation method has been applied to COVID-19 and SARS.[28] It follows from the differential equation for the number of exposed individuals $n_E$ and the number of latent infectious individuals $n_I$,

$\frac{d}{dt} \begin{pmatrix} n_E \\ n_I \end{pmatrix} = \begin{pmatrix} -1/\tau_E & R_0/\tau_I \\ 1/\tau_E & -1/\tau_I \end{pmatrix} \begin{pmatrix} n_E \\ n_I \end{pmatrix}.$

The largest eigenvalue of the matrix is the logarithmic growth rate $K$, which can be solved for $R_0$.

## Other uses

R0 is also used as a measure of individual reproductive success in population ecology,[29] evolutionary invasion analysis and life history theory. It represents the average number of offspring produced over the lifetime of an individual (under ideal conditions).

For simple population models, R0 can be calculated, provided an explicit decay rate (or "death rate") is given. In this case, the reciprocal of the decay rate (usually 1/d) gives the average lifetime of an individual. When multiplied by the average number of offspring per individual per timestep (the "birth rate" b), this gives R0 = b/d. For more complicated models that have variable growth rates (e.g. because of self-limitation or dependence on food densities), the maximum growth rate should be used.

## Limitations of R0

When calculated from mathematical models, particularly ordinary differential equations, what is often claimed to be R0 is, in fact, simply a threshold, not the average number of secondary infections. There are many methods used to derive such a threshold from a mathematical model, but few of them always give the true value of R0. This is particularly problematic if there are intermediate vectors between hosts, such as malaria.[30]

What these thresholds will do is determine whether a disease will die out (if R0 < 1) or whether it may become epidemic (if R0 > 1), but they generally cannot compare different diseases. Therefore, the values from the table above should be used with caution, especially if the values were calculated from mathematical models.

Methods include the survival function, rearranging the largest eigenvalue of the Jacobian matrix, the next-generation method,[31] calculations from the intrinsic growth rate,[32] existence of the endemic equilibrium, the number of susceptibles at the endemic equilibrium, the average age of infection[33] 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 R0 is rarely observed in the field and is usually calculated via a mathematical model, this severely limits its usefulness.[34]

## In popular culture

In the 2011 film Contagion, a fictional medical disaster thriller, R0 calculations are presented to reflect the progression of a fatal viral infection from case studies to a pandemic.

In the TV series Travelers, R0 calculations are used to inform a group on the progression of a viral infection from various sources during a pandemic.

## Notes

• Compartmental models in epidemiology describe disease dynamics over time in a population of susceptible (S), infectious (I), and recovered (R) people using the SIR model. Note that in the SIR model, R(0) and R0 are different quantities - the former describes the number of recovered at t = 0 whereas the latter describes the ratio between the frequency of contacts to the frequency of recovery.
• According to Guangdong Provincial Center for Disease Control and Prevention, "The effective reproductive number (R) is more commonly used to describe transmissibility, which is defined as the average number of secondary cases generated by per [sic] infectious case. In the absence of control measures, R = R0χ, where χ is the proportion of the susceptible population." For example, the effective reproductive number for 2019-nCoV was found as 2.9, whereas for SARS it was 1.77.[35]

## References

1. Unless noted R0 values are from History and Epidemiology of Global Smallpox Eradication (Archived 2016-05-10 at the Wayback Machine), a module of the training course "Smallpox: Disease, Prevention, and Intervention". The CDC and the World Health Organization, 2001. Slide 17. This gives sources as "Modified from Epid Rev 1993;15: 265-302, Am J Prev Med 2001; 20 (4S): 88-153, MMWR 2000; 49 (SS-9); 27-38"
2. Guerra, Fiona M.; Bolotin, Shelly; Lim, Gillian; Heffernan, Jane; Deeks, Shelley L.; Li, Ye; Crowcroft, Natasha S. (December 1, 2017). "The basic reproduction number (R0) of measles: a systematic review". The Lancet Infectious Diseases. 17 (12): e420–e428. doi:10.1016/S1473-3099(17)30307-9. ISSN 1473-3099. Retrieved March 18, 2020.
3. Kretzschmar M, Teunis PF, Pebody RG (2010). "Incidence and reproduction numbers of pertussis: estimates from serological and social contact data in five European countries". PLOS Med. 7 (6): e1000291. doi:10.1371/journal.pmed.1000291. PMC 2889930. PMID 20585374.
4. Gani, Raymond; Leach, Steve (December 2001). "Transmission potential of smallpox in contemporary populations". Nature. 414 (6865): 748–751. doi:10.1038/414748a. ISSN 1476-4687. Retrieved March 18, 2020.
5. Wallinga J, Teunis P (2004). "Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures". Am. J. Epidemiol. 160 (6): 509–16. doi:10.1093/aje/kwh255. PMID 15353409. Archived from the original on October 6, 2007.
6. Truelove, Shaun A.; Keegan, Lindsay T.; Moss, William J.; Chaisson, Lelia H.; Macher, Emilie; Azman, Andrew S.; Lessler, Justin. "Clinical and Epidemiological Aspects of Diphtheria: A Systematic Review and Pooled Analysis". Clinical Infectious Diseases. doi:10.1093/cid/ciz808. Retrieved March 18, 2020.
7. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. (January 2020). "Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia". The New England Journal of Medicine. doi:10.1056/NEJMoa2001316. PMID 31995857.
8. Riou, Julien and Althaus, Christian L. (2020). "Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020". Eurosurveillance. 25 (4). doi:10.2807/1560-7917.ES.2020.25.4.2000058. PMC 7001239. PMID 32019669.CS1 maint: multiple names: authors list (link)
9. Liu T, Hu J, Kang M, Lin L (January 2020). "Time-varying transmission dynamics of Novel Coronavirus Pneumonia in China". bioRxiv. doi:10.1101/2020.01.25.919787.
10. Read JM, Bridgen JRE, Cummings DAT, et al. (January 28, 2020). "Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions". MedRxiv. doi:10.1101/2020.01.23.20018549.
11. Wu, Joseph T.; Leung, Kathy; Bushman, Mary; Kishore, Nishant; Niehus, Rene; de Salazar, Pablo M.; Cowling, Benjamin J.; Lipsitch, Marc; Leung, Gabriel M. (March 19, 2020). "Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China". Nature Medicine: 1–5. doi:10.1038/s41591-020-0822-7. ISSN 1546-170X.
12. Ferguson NM; Cummings DA; Fraser C; Cajka JC; Cooley PC; Burke DS (2006). "Strategies for mitigating an influenza pandemic". Nature. 442 (7101): 448–452. doi:10.1038/nature04795. PMID 16642006.
13. Althaus, Christian L. (2014). "Estimating the Reproduction Number of Ebola Virus (EBOV) During the 2014 Outbreak in West Africa". PLOS Currents. 6. arXiv:1408.3505. Bibcode:2014arXiv1408.3505A. doi:10.1371/currents.outbreaks.91afb5e0f279e7f29e7056095255b288. PMC 4169395. PMID 25642364.
14. Coburn BJ; Wagner BG; Blower S (2009). "Modeling influenza epidemics and pandemics: insights into the future of swine flu (H1N1)". BMC Medicine. 7. Article 30. doi:10.1186/1741-7015-7-30. PMID 19545404.
15. Kucharski, Adam and Althaus, Christian L. (2015). "The role of superspreading in Middle East respiratory syndrome coronavirus (MERS-CoV) transmission". Eurosurveillance. 20 (26): 14–8. doi:10.2807/1560-7917.ES2015.20.25.21167. PMID 26132768.CS1 maint: multiple names: authors list (link)
16. Milligan, Gregg N.; Barrett, Alan D. T. (2015). Vaccinology : an essential guide. Chichester, West Sussex: Wiley Blackwell. p. 310. ISBN 978-1-118-63652-7. OCLC 881386962.
17. Christophe Fraser; Christl A. Donnelly; Simon Cauchemez; et al. (June 19, 2009). "Pandemic Potential of a Strain of Influenza A (H1N1): Early Findings". Science. 324 (5934): 1557–1561. Bibcode:2009Sci...324.1557F. doi:10.1126/science.1176062. PMC 3735127. PMID 19433588.Free text
18. "Department of Health | 2.2 The reproduction number". www1.health.gov.au. Retrieved February 1, 2020.
19. Jones, James. "Notes On R0" (PDF). Stanford University.
20. Siegel, Ethan. "Why 'Exponential Growth' Is So Scary For The COVID-19 Coronavirus". Forbes. Retrieved March 19, 2020.
21. Delamater, Paul L.; Street, Erica J.; Leslie, Timothy F.; Yang, Y. Tony; Jacobsen, Kathryn H. (January 2019). "Complexity of the Basic Reproduction Number (R 0 )". Emerging Infectious Diseases. 25 (1): 1–4. doi:10.3201/eid2501.171901. ISSN 1080-6040. PMC 6302597. PMID 30560777.
22. Fine, Paul; Eames, Ken; Heymann, David L. (April 1, 2011). ""Herd Immunity": A Rough Guide". Clinical Infectious Diseases. 52 (7): 911–916. doi:10.1093/cid/cir007. ISSN 1058-4838. PMID 21427399.
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24. Macdonald, G. (September 1952). "The analysis of equilibrium in malaria". Tropical Diseases Bulletin. 49 (9): 813–829. ISSN 0041-3240. PMID 12995455.
25. Blower, S. M., Mclean, A. R., Porco, T. C., Small, P. M., Hopewell, P. C., Sanchez, M. A., et al. (1995). "The intrinsic transmission dynamics of tuberculosis epidemics." Nature Medicine, 1, 815–821.
26. O Diekmann; J.A.P. Heesterbeek; J.A.J. Metz (1990). "On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations". Journal of Mathematical Biology. 28 (4): 356–382. doi:10.1007/BF00178324. hdl:1874/8051. PMID 2117040.
27. Lipsitch, Marc; Cohen, Ted; Cooper, Ben; Robins, James M.; Ma, Stefan; James, Lyn; Gopalakrishna, Gowri; Chew, Suok Kai; Tan, Chorh Chuan; Samore, Matthew H.; Fisman, David (June 20, 2003). "Transmission Dynamics and Control of Severe Acute Respiratory Syndrome". Science. 300 (5627): 1966–1970. Bibcode:2003Sci...300.1966L. doi:10.1126/science.1086616. ISSN 0036-8075. PMC 2760158. PMID 12766207.
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31. Diekmann O, Heesterbeek JA (2000). Mathematical epidemiology of infectious diseases: model building, analysis and interpretation. New York: Wiley.
32. Chowell G, Hengartnerb NW, Castillo-Chaveza C, Fenimorea PW, Hyman JM (2004). "The basic reproductive number of Ebola and the effects of public health measures: the cases of Congo and Uganda". Journal of Theoretical Biology. 229 (1): 119–126. arXiv:q-bio/0503006. doi:10.1016/j.jtbi.2004.03.006. PMID 15178190.
33. Ajelli M; Iannelli M; Manfredi P; Ciofi degli Atti, ML (2008). "Basic mathematical models for the temporal dynamics of HAV in medium-endemicity Italian areas". Vaccine. 26 (13): 1697–1707. doi:10.1016/j.vaccine.2007.12.058. PMID 18314231. Unknown parameter |last-author-amp= ignored (help)
34. Heffernan JM, Smith RJ, Wahl LM (2005). "Perspectives on the Basic Reproductive Ratio". Journal of the Royal Society Interface. 2 (4): 281–93. doi:10.1098/rsif.2005.0042. PMC 1578275. PMID 16849186.
35. Liu, Tao; Hu, Jianxiong; Kang, Min; Lin, Lifeng; Zhong, Haojie; Xiao, Jianpeng; He, Guanhao; Song, Tie; Huang, Qiong; Rong, Zuhua; Deng, Aiping; Zeng, Weilin; Tan, Xiaohua; Zeng, Siqing; Zhu, Zhihua; Li, Jiansen; Wan, Donghua; Lu, Jing; Deng, Huihong; He, Jianfeng; Ma, Wenjun (January 25, 2020). "Transmission dynamics of 2019 novel coronavirus (2019-nCoV)". bioRxiv. doi:10.1101/2020.01.25.919787.