Location of Repository

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

By M J Ferrari, A Djibo, R Grais, B T Grenfell and O N Bjørnstad

Abstract

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

Publisher: Cambridge University Press
Year: 2010
DOI identifier: 10.1017/S0950268809990173
OAI identifier: oai:fieldresearch.msf.org:10144/98914
Provided by: MSF Field Research
Journal:

Suggested articles

Preview

Citations

  1. (1974). A catalytic model of infection form measles.
  2. (2000). A simple model for complex dynamical transitions in epidemics. Science
  3. (1984). An age-structured model of pre- and postvaccination measles transmission.
  4. (1982). An analysis of factors underlying seasonal patterns.
  5. (1959). Catalytic Models in Epidemiology.
  6. (1973). Changing family structures among rural Hausa. Africa
  7. (1993). Chaos and biological complexity in measles dynamics.
  8. Clarkson JA.Measles in England andWales.
  9. (2004). Estimating the force of measles virus infection from hospitalised cases in
  10. (2004). Estimation of infectious disease parameters from serological survey data: the impact of regular epidemics.
  11. (2007). Factbook: Niger. Central Intelligence Agency,
  12. (2007). Has the 2005 measles mortality reduction goal been achieved? A natural history modelling study. Lancet
  13. (1991). Infectious Diseases of Humans: Dynamics and Control.
  14. (1996). Markov Chain Monte Carlo in Practice.
  15. (2005). Matrix models for childhood infections : a Bayesian approach with applications to rubella and mumps. Epidemiology and Infection
  16. (1993). Measles : An Historical Geography of a Major Human Viral Disease from Global Expansion to Local Retreat, 1840–1990.
  17. (1991). Measles epidemic in Harare, Zimbabwe, despite high measles immunization coverage rates.
  18. (1995). Measles in infants – a review of studies on incidence, vaccine efficacy and mortality in East Africa.
  19. (1985). Nearly one dimensional dynamics in an epidemic.
  20. (2003). Seroepidemiology of measles in Addis Ababa, Ethiopia : implications for control through vaccination. Epidemiology and Infection
  21. (2008). The dynamics of measles in subSaharan Africa. Nature
  22. (1985). The estimation of agerelated rates of infection from case notifications and serological data.
  23. (1984). The force of measles infection in East Africa.
  24. (2000). The global burden of measles in the year
  25. (2000). The pre-vaccination epidemiology of measles, mumps and rubella in Europe: implications for modelling studies. Epidemiology and Infection
  26. (2001). Travelling waves and spatial hierarchies in measles epidemics.
  27. Unacceptably high mortality related to measles epidemics in

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.