Summary
The basic reproduction number, R nought (R0), is defined as the average number of secondary cases of an infectious disease arising from a typical case in a totally susceptible population, and can be estimated in populations if pre-existing immunity can be accounted for in the calculation. R0 determines the herd immunity threshold and therefore the immunisation coverage required to achieve elimination of an infectious disease. As R0 increases, higher immunisation coverage is required to achieve herd immunity. In July, 2010, a panel of experts convened by WHO concluded that measles can and should be eradicated. Despite the existence of an effective vaccine, regions have had varying success in measles control, in part because measles is one of the most contagious infections. For measles, R0 is often cited to be 12–18, which means that each person with measles would, on average, infect 12–18 other people in a totally susceptible population. We did a systematic review to find studies reporting rigorous estimates and determinants of measles R0. Studies were included if they were a primary source of R0, addressed pre-existing immunity, and accounted for pre-existing immunity in their calculation of R0. A search of key databases was done in January, 2015, and repeated in November, 2016, and yielded 10 883 unique citations. After screening for relevancy and quality, 18 studies met inclusion criteria, providing 58 R0 estimates. We calculated median measles R0 values stratified by key covariates. We found that R0 estimates vary more than the often cited range of 12–18. Our results highlight the importance of countries calculating R0 using locally derived data or, if this is not possible, using parameter estimates from similar settings. Additional data and agreed review methods are needed to strengthen the evidence base for measles elimination modelling.
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Access any 5 articles from the Lancet Family of journals
Subscribe:
Subscribe to The Lancet Infectious Diseases
Already a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- 1.
Measles: pathology, management and public health issues.Nurs Stand. 2014; 28: 51-58
- 2.
Vaccines through centuries: major cornerstones of global health.Front Public Health. 2015; 3: 269
- 3.
Measles elimination: progress, challenges and implications for rubella control.Expert Rev Vaccines. 2013; 12: 917-932
- 4.
Proceedings of the Global Technical Consultation to assess the feasibility of measles eradication, 28–30 July 2010.J Infect Dis. 2011; 204: S4-13
- 5. Infectious disease of humans. Oxford University Press, Oxford1991: 768
- 6. An introduction to infectious disease modelling. Oxford University Press, Oxford2010
- 7.
Directly transmitted infections diseases: control by vaccination.Science. 1982; 215: 1053-1060
- 8.
Age-related changes in the rate of disease transmission: implications for the design of vaccination programmes.J Hyg (Lond). 1985; 94: 365-436
- 9.
The development and validation of a meta-tool for quality appraisal of public health evidence: Meta Quality Appraisal Tool (MetaQAT).Public Health. 2016; 136: 57-65
- 10.
Historical data and modern methods reveal insights in measles epidemiology: a retrospective closed cohort study.BMJ Open. 2013; 3: e002033
- 11.
Estimating the transmission rate for a highly infectious disease.Biometrics. 1998; 54: 730-738
- 12.
Deciphering the relative weights of demographic transition and vaccination in the decrease of measles incidence in Italy.Proc Biol Sci. 2014; 281: 20132676
- 13.
Seasonality and comparative dynamics of six childhood infections in pre-vaccination Copenhagen.Proc Biol Sci. 2009; 276: 4111-4118
- 14.
Transmission and control of arboviruses.in: Ludwig D Cooke KL Proceedings of the SIMS conference on epidemiology. Society for Industrial and Applied Mathematics, Philadelphia, PA1975: 104-121
- 15.
Oscillations and chaos in epidemics: a nonlinear dynamic study of six childhood diseases in Copenhagen, Denmark.Theor Popul Biol. 1988; 33: 344-370
- 16.
Parameterizing state-space models for infectious disease dynamics by generalized profiling: measles in Ontario.J R Soc Interface. 2011; 8: 961-974
- 17.
The pre-vaccination epidemiology of measles, mumps and rubella in Europe: implications for modelling studies.Epidemiol Infect. 2000; 125: 635-650
- 18.
Estimation of measles reproduction ratios and prospects for elimination of measles by vaccination in some western European countries.Epidemiol Infect. 2001; 127: 281-295
- 19.
Plug-and-play inference for disease dynamics: measles in large and small populations as a case study.J R Soc Interface. 2010; 7: 271-283
- 20.
A review of data needed to parameterize a dynamic model of measles in developing countries.BMC Res Notes. 2010; 3 (75-0500-3-75.)
- 21.
Epidemiological impact of vaccination on the dynamics of two childhood diseases in rural Senegal.Microbes Infect. 2005; 7: 593-599
- 22.
Estimation of the basic reproduction number of measles during an outbreak in a partially vaccinated population.Epidemiol Infect. 2000; 124: 273-278
- 23.
Reconstruction of measles dynamics in a vaccinated population.Vaccine. 2003; 21: 2643-2650
- 24.
Estimating transmission intensity for a measles epidemic in Niamey, Niger: lessons for intervention.Trans R Soc Trop Med Hyg. 2006; 100: 867-873
- 25.
Estimation of measles vaccine efficacy and critical vaccination coverage in a highly vaccinated population.J R Soc Interface. 2010; 7: 1537-1544
- 26.
The effect of heterogeneity in uptake of the measles, mumps, and rubella vaccine on the potential for outbreaks of measles: a modelling study.Lancet Infect Dis. 2016; 16: 599-605
- 27.
Is the basic reproductive number (R0) for measles viruses observed in recent outbreaks lower than in the pre-vaccination era?.Euro Surveill. 2012; 17: 22
- 28.
An ongoing large outbreak of measles in Merseyside, England, January to June 2012.Euro Surveill. 2012; 17: 20226
- 29.
2015 country snapshots.http://www.un.org/en/development/desa/policy/cdp/cdp_publications/2015_ldc_factsheet_all.pdf(accessed June 25, 2017).Date: 2015
- 30.
Unraveling R0: considerations for public health applications.Rev Panam Salud Publica. 2015; 38: 167-176
- 31.
Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature.BMC Infect Dis. 2014; 14 (1471-2334-14-480.)
- 32.
Models of the impact of dengue vaccines: a review of current research and potential approaches.Vaccine. 2011; 29: 5860-5868
- 33.
Impact of population size on incidence of rubella and measles in comparison with that of other infectious diseases.Jpn J Infect Dis. 2015; 68: 80
- 34.
Measles in developing countries. Part I. Epidemiological parameters and patterns.Epidemiol Infect. 1988; 100: 111-133
- 35.
Theory versus data: how to calculate R0?.PLoS One. 2007; 2: e282
- 36.
Perspectives on the basic reproductive ratio.J R Soc Interface. 2005; 2: 281-293
- 37.
The failure of R0.Comput Math Methods Med. 2011; 2011: 527610
- 38.
Evolution and use of dynamic transmission models for measles and rubella risk and policy analysis.Risk Anal. 2016; 36: 1383-1403
- 39.
The basic reproduction number as a predictor for epidemic outbreaks in temporal networks.PLoS One. 2015; 10: e0120567
- 40.
A new framework and software to estimate time-varying reproduction numbers during epidemics.Am J Epidemiol. 2013; 178: 1505-1512
- 41.
Guidelines for accurate and transparent health estimates reporting: the GATHER statement.Lancet. 2016; 388: e19-e23
- 42.
GRADE guidelines: a new series of articles in the Journal of Clinical Epidemiology.J Clin Epidemiol. 2011; 64: 380-382
- 43.
Dynamic transmission modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-5.Med Decis Making. 2012; 32: 712-721
- 44.
The R0 package: a toolbox to estimate reproduction numbers for epidemic outbreaks.BMC Med Inform Decis Mak. 2012; 12 (1472-6947-12-147.)
- 45.
Modeling infectious disease dynamics in the complex landscape of global health.Science. 2015; 347: aaa4339
- 46.
An intuitive formulation for the reproductive number for the spread of diseases in heterogeneous populations.Math Biosci. 2000; 167: 65-86
- 47.
An IDEA for short term outbreak projection: nearcasting using the basic reproduction number.PLoS One. 2013; 8: e83622
- 48.
Emergence of viral diseases: mathematical modeling as a tool for infection control, policy and decision making.Crit Rev Microbiol. 2010; 36: 195-211
- 49.
Framework for verifying elimination of measles and rubella.Wkly Epidemiol Rec. 2013; 88: 89-99
- 50.
Verification of measles elimination in Australia: Application of World Health Organization regional guidelines.J Epidemiol Glob Health. 2016; 6: 197-209
- 51.
Epidemiology of transmissible diseases after elimination.Am J Epidemiol. 2000; 151: 1039-1052
Article info
Publication history
Published: July 27, 2017
Identification
Copyright
© 2017 Elsevier Ltd. All rights reserved.