Dynamics in Age: Though much has been gained through analysis of long-term time
series, data are often limiting in many cases. For immunizing infections, cross-sectional
analysis of age-specific serology or incidence can provide insights into
historical disease rates and the impact of past controls. The force of infection, or the rate at which
susceptible individuals are exposed to infection, generates age-specific
patterns in the distribution of population immunity (e.g. the age-dependent
proportion of those ever previously infected) that connect the current age
distribution of epidemic states (e.g. the age distribution of susceptible,
infected, and/or recovered and immune individuals) to the historical prevalence
of infection. When disease rates are
high, then individuals are more likely to encounter infection early in life,
and conversely when rates are low. Thus,
individual birth cohorts carry a cumulative history of prior disease exposure.
One of the most common methods for estimating R0 from the mean age of infection
arises from this phenomenon: R0 = L/A, where L is the life expectancy and A is
the mean age of infection Anderson 1991. Thus, when R0 is high,
the mean age of infection will be low relative to life expectancy.