A disease-specific surveillance system that permits the quantification of absolute disease incidence across space and time is an ideal that is challenging to implement in all but a handful of specific cases.  Even passive systems from which explicit models to estimate disease burden can be developed are challenging and the analyses above require a level of computational sophistication that is often prohibitive.  However, an understanding of the behavior of epidemic dynamics can be useful in interpreting phenomenological patterns that are indicative of program performance. 

Changes in demographic rates, transmission rates, and control efforts can result in phenomenological changes pattern of disease incidence in space or time.  Thus, even in the absence of methods to explicitly estimate incidence, we can assess disease trends through qualitative shifts.  Identifying these shifts, and their sensitivity and specificity to changes in absolute disease incidence requires formal analysis of the dynamic models.  However, once done, these can provide simple benchmarks or alerts us to counter-intuitive patterns that may arise from non-linear dynamics that may indicate the need or more exhaustive study. 

The shift in the cycle period for measles in England and Wales is a classic example of this. The increase in birth rates following WWII led to an increase in the frequency of measles outbreaks (from a 2-year period to a 1-year period) for nearly a decade. As birth rates declined in the 1960’s, prior to the introduction of the measles vaccine, the cycles returned to a 2-year period Grenfell 2002. While this is a very specific historical example, an analogous phenomenon has resulted from the introduction of measles vaccine worldwide, which has reduced the effective birth rate (i.e. the rate at which children who will remain unvaccinated and thus available to become infected Earn 2000.  These multi-annual shifts in demographics and health systems may coincide with changes in surveillance and reporting rates that can confound inference based on cases alone; however, large qualitative shifts, such as changes in cycle period are robust to changes in absolute reporting rate. 

As the incidence of infection declines, so does the force of infection – defined as the rate at which susceptible individuals encounter infection.  Lower force of infection results in a shift in the age distribution of cases, as each individual is less likely to be exposed per unit time and thus will be older, on average, when first exposed Anderson 1991 Ferrari 2013.  Thus, the observed shift in the age distribution of cases provides an additional population-scale diagnostic that is robust to reporting rate; i.e. cases are expected to be older whether 5% or 50% are seen by the surveillance system. 

Merler et al Merler 2014 analyzed the age distribution of measles seroprevalence to show that declining birth rates in Italy prior to 1982 were a major contributing factor to the reductions in measles incidence, and that increasing vaccination rates have driven continued declines since 1983 when birth rates leveled off.