Skip to main content Accesibility Help
×
×
Home

Episodic outbreaks bias estimates of age-specific force of infection: a corrected method using measles as an example

  • M. J. FERRARI (a1) (a2), A. DJIBO (a3), R. F. GRAIS (a4), B. T. GRENFELL (a1) (a2) and O. N. BJØRNSTAD (a1) (a2) (a5)...
Summary

Understanding age-specific differences in infection rates can be important in predicting the magnitude of and mortality in outbreaks and targeting age groups for vaccination programmes. Standard methods to estimate age-specific rates assume that the age-specific force of infection is constant in time. However, this assumption may easily be violated in the face of a highly variable outbreak history, as recently observed for acute immunizing infections like measles, in strongly seasonal settings. Here we investigate the biases that result from ignoring such fluctuations in incidence and present a correction based on the epidemic history. We apply the method to data from a measles outbreak in Niamey, Niger and show that, despite a bimodal age distribution of cases, the estimated age-specific force of infection is unimodal and concentrated in young children (<5 years) consistent with previous analyses of age-specific rates in the region.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Episodic outbreaks bias estimates of age-specific force of infection: a corrected method using measles as an example
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Episodic outbreaks bias estimates of age-specific force of infection: a corrected method using measles as an example
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Episodic outbreaks bias estimates of age-specific force of infection: a corrected method using measles as an example
      Available formats
      ×
Copyright
Corresponding author
*Author for correspondence: Dr M. J. Ferrari, Center for Infectious Disease Dynamics, Department of Biology, 208 Mueller Laboratory, Penn State University, University Park, PA 16802, USA. (Email: mferrari@psu.edu)
References
Hide All
1. Cliff, AD, Haggett, P, Smallman-Raynor, M. Measles: An Historical Geography of a Major Human Viral Disease from Global Expansion to Local Retreat, 1840–1990. Oxford: Blackwell, 1993, pp. 462.
2. Wolfson, LJ, et al. Has the 2005 measles mortality reduction goal been achieved? A natural history modelling study. Lancet 2007; 369: 191200.
3. Edmunds, WJ, et al. The pre-vaccination epidemiology of measles, mumps and rubella in Europe: implications for modelling studies. Epidemiology and Infection 2000; 125: 635650.
4. Enquselassie, F, et al. Seroepidemiology of measles in Addis Ababa, Ethiopia: implications for control through vaccination. Epidemiology and Infection 2003; 130: 507519.
5. Remme, J, Mandara, MP, Leeuwenburg, J. The force of measles infection in East Africa. International Journal of Epidemiology 1984; 13: 332339.
6. Scott, S, et al. Estimating the force of measles virus infection from hospitalised cases in Lusaka, Zambia. Vaccine 2004; 23: 732738.
7. Griffiths, DA. A catalytic model of infection form measles. Applied Statistics 1974; 23: 330339.
8. Muench, H. Catalytic Models in Epidemiology. Cambridge, MA: Harvard University Press, 1959, pp. 110.
9. Whitaker, HJ, Farrington, CP. Estimation of infectious disease parameters from serological survey data: the impact of regular epidemics. Statistics in Medicine 2004; 23: 24292443.
10. Fine, PEM, Clarkson, JA. Measles in England and Wales. 1. An analysis of factors underlying seasonal patterns. International Journal of Epidemiology 1982; 11: 5–14.
11. Grenfell, BT, Bjornstad, ON, Kappey, J. Travelling waves and spatial hierarchies in measles epidemics. Nature 2001; 414: 716723.
12. Schaffer, WM, Kot, M. Nearly one dimensional dynamics in an epidemic. Journal of Theoretical Biology 1985; 112: 403427.
13. Ferrari, MJ, et al. The dynamics of measles in sub-Saharan Africa. Nature 2008; 451: 679684.
14. Anderson, RM, May, RM. Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press, 1991, pp. 768.
15. Grenfell, BT, Anderson, RM. The estimation of age-related rates of infection from case notifications and serological data. Journal of Hygiene 1985; 95: 419436.
16. CIA. World Factbook: Niger. Central Intelligence Agency, USA, 2007.
17. Gilks, W, Richardson, S, Spiegelhalter, DJ. Markov Chain Monte Carlo in Practice. London: Chapman & Hall, 1996, pp. 486.
18. Kanaan, MN, Farrington, CPA. Matrix models for childhood infections: a Bayesian approach with applications to rubella and mumps. Epidemiology and Infection 2005; 133: 10091021.
19. Goddard, AD. Changing family structures among rural Hausa. Africa 1973; 43: 207218.
20. Schenzle, D. An age-structured model of pre- and post-vaccination measles transmission. IMA Journal of Mathematics Applied in Medicine and Biology 1984; 1: 169191.
21. Bolker, BM, Grenfell, BT. Chaos and biological complexity in measles dynamics. Proceedings of the Royal Society of London, Series B: Biological Sciences 1993; 251: 7581.
22. Grais, RF, et al. Unacceptably high mortality related to measles epidemics in Niger, Nigeria, and Chad. PLOS Medicine 2007; 4: 122129.
23. Burstrom, B, Aaby, P, Mutie, DM. Measles in infants – a review of studies on incidence, vaccine efficacy and mortality in East Africa. East African Medical Journal 1995; 72: 155161.
24. Kambarami, RA, et al. Measles epidemic in Harare, Zimbabwe, despite high measles immunization coverage rates. Bulletin of the World Health Organization 1991; 69: 213219.
25. Earn, DJD, et al. A simple model for complex dynamical transitions in epidemics. Science 2000; 287: 667670.
26. Stein, CE, et al. The global burden of measles in the year 2000 – a model that uses country-specific indicators. Journal of Infectious Diseases 2003; 187: S8–S14.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Epidemiology & Infection
  • ISSN: 0950-2688
  • EISSN: 1469-4409
  • URL: /core/journals/epidemiology-and-infection
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords

Type Description Title
PDF
Supplementary materials

Ferrari supplementary material
Figures.pdf

 PDF (547 KB)
547 KB

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed