Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-22T14:57:49.607Z Has data issue: false hasContentIssue false
  • English
  • Français

Interpretation of serological surveillance data for measles using mathematical models: implications for vaccine strategy

Published online by Cambridge University Press:  15 May 2009

N. J. Gay ,
L. M. Hesketh ,
P. Morgan-Capner  and
E. Miller
N. J. Gay
Affiliation:
Immunisation Division, PHLS Communicable Disease Surveillance Centre, 61, Colindak Avenue, London NW9 5EQ, UK
L. M. Hesketh
Affiliation:
Preston Public Health Laboratory, Royal Preston Hospital, P.O. Box 202, Sharoe Green Lane, Preston PR2 4HG
P. Morgan-Capner
Affiliation:
Preston Public Health Laboratory, Royal Preston Hospital, P.O. Box 202, Sharoe Green Lane, Preston PR2 4HG
E. Miller
Affiliation:
Immunisation Division, PHLS Communicable Disease Surveillance Centre, 61, Colindak Avenue, London NW9 5EQ, UK
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Serological surveillance of measles immunity has been carried out in England since 1986/7. Results from sera collected in 1989–91 revealed that the proportion of school age children who were susceptible to measles was increasing, following the introduction of the measles, mumps and rubella vaccination programme in October 1988. Mathematical models are used to interpret these data and determine whether this increasing susceptibility is sufficient to allow a resurgence of disease from the low levels achieved by 1993. The models summarize serological profiles by a single parameter, the reproduction number R, which quantifies the level of herd immunity in the population. Results showed that there was cause for concern over the levels of susceptibility to measles, with an epidemic of over 100000 cases likely in 1995/6. These predictions are consistent with trends in the incidence and age distribution of measles and have enabled the planning of a major vaccination campaign.

Type
Research Article
Information
Epidemiology & Infection , Volume 115 , Issue 1 , August 1995 , pp. 139 - 156
Copyright
Copyright © Cambridge University Press 1995

References

1.Registrar General's Statistical Review. Annual review of the Registrar General of England and Wales. London: HMSO, 1956–1965.Google Scholar
2.White, JM, Leon, S, Ramsay, ME. COVER (Cover of vaccination evaluated rapidly): 31. Commun Dis Rep 1994; 4: R129–30.Google ScholarPubMed
3.Brown, DW, Ramsay, ME, Richards, AF, Miller, E. Salivary diagnosis of measles: a study of notified cases in the United Kingdom, 1991–3. BMJ 1994; 308: 1015–17.CrossRefGoogle ScholarPubMed
4.Ramsay, M, Gay, N, Miller, E et al. , The epidemiology of measles in England and Wales: rationale for the 1994 national vaccination campaign. Commun Dis Rep; 4: R141–6.Google Scholar
5.Morgan-Capner, P, Wright, J, Miller, CL, Miller, E. Surveillance of antibody to measles, mumps and rubella by age. BMJ 1988; 297: 770–2.CrossRefGoogle ScholarPubMed
6.Anderson, RM, May, RM. Infectious diseases of humans: dynamics and control, 2nd ed. Oxford: Oxford University Press, 1991.CrossRefGoogle Scholar
7.Anderson, RM, May, RM. Age-related changes in the rate of disease transmission: implications for the design of vaccination programmes. J Hyg 1985; 94: 365–435.CrossRefGoogle ScholarPubMed
8.Anderson, RM, Grenfell, BT. Quantitative investigations of different rubella vaccination policies for the control of congenital rubella syndrome (CRS) in the United Kingdom. J Hyg 1986; 96: 305–33.CrossRefGoogle ScholarPubMed
9.Diekmann, O, Heesterbeek, JAP, Metz, JAJ. On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. J Math Biol 1990; 28: 365–82.CrossRefGoogle ScholarPubMed
10.Farrington, CP. Modelling forces of infection for measles, mumps and rubella. Stat Med 1990; 9: 953–67.CrossRefGoogle ScholarPubMed
11.Babad, HR, Nokes, DJ, Gay, NJ, Miller, E, Morgan-Capner, P, Anderson, RM. Predicting the impact of measles vaccination in England and Wales: model validation and analysis of policy options. Epidemiol Infect 1995; 114: 319–41.CrossRefGoogle ScholarPubMed
12.Calvert, N, Cutts, FT, Miller, E, Brown, DW, Munro, J. Measles among secondary school children in West Cumbria: implications for vaccine policy. Commun Dis Rep 1994; 4: R70–3.Google Scholar
13.Morse, D, O'Shea, M, Hamilton, G et al. , Outbreak of measles in a teenage school population: need to immunize susceptible adolescents. Epidemiol Infect 1994; 113: 355–65.CrossRefGoogle Scholar
14.Miller, E. The new measles campaign. BMJ 1994; 309: 1102–3.CrossRefGoogle ScholarPubMed
15.Christie, P. Measles in Scotland. Commun Dis Environ Hlth Scotland Weekly Rep 1994; 28: 3–8.Google Scholar
16.Chen, RT, Markowitz, LE, Albrecht, P et al. , Measles antibody: reevaluation of protective titres. J Infect Dis 1990; 162: 1036–42.CrossRefGoogle Scholar
17.Fine, PEM, Zell, ER. Outbreaks in highly vaccinated populations - implications for studies of vaccine performance. Am J Epidemiol 1994; 139: 77–90.CrossRefGoogle ScholarPubMed
18.Baughman, AL. Williams, WW, Atkinson, WL, Cook, LG, Collins, MJ. The impact of college prematriculation immunization requirements on risk for measles outbreaks. JAMA 1994; 272: 1127–32.CrossRefGoogle ScholarPubMed