A great deal of modeling work has focused upon the population dynamics of measles infection (see Anderson and May, 1991). In part this is because there are good data sets resulting from a long history of case notifications against which model performance may be judged; in some cases, there are data also from serological surveys carried out prior to the introduction of vaccination. However, it is also in part because measles provides the classic example of an infection with strong epidemic cycles: in the case of England and Wales, for example, with numbers of cases oscillating over a 2-year period, whereas in others the period of oscillation is greater (Anderson et al., 1984; Manfredi et al., 2005). From a global public health perspective, however, the prime interest is the fact that until recently up to 1 million children each year are believed to have died as a result of measles infection. Even now, with safe and effective vaccination widely available, substantial numbers of deaths and serious disease occur in the developing world. Measles case mortality rates decline sharply with age in the first few months and early years of life, but then increase again towards adult years so that by early adulthood the risk is of a similar order to that seen in infants only a few months old, and case mortality rates appear to continue to increase towards old age. Thus although the majority of older individuals will have experienced measles infection in childhood and, because of the particular patterns of contact between age groups, may in any event be at reduced risk of infection, those who do become infected in later life may be at greatly enhanced risk of death or disease. As noted above, interventions in the form of vaccination and demographic change can result in increases in the average age at infection with a resulting skewing of the distribution of infection towards older age groups, so that there is the potential for these processes to bring about an increase in disease burden at older ages (Figure 15.9) (Williams and Manfredi, 2004). In such circumstances, the modeling of plausible scenarios of vaccination and demographic change can help
(1) to indicate which age groups are more at risk,
(2) to quantify the broad range of the increase in disease burden, (3) to identify to what extent this is a function of a transient change in demography or a side effect of gradual elimination of the infection from the population and to what extent it is a more long lasting phenomenon, and (4) to inform design of vaccination strategies. An approach of this type can be applied to any infection where case fatality rates or case morbidity rates change with age.
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