Punctuated interventions, such as SIAs, necessarily result in fluctuating dynamics – e.g. the proportion susceptible, disease burden, or risk of outbreak fluctuates in time – but this fluctuation can, in principle, be predicted if the underlying rates (birth, vaccination, transmission) are known and remain constant. Recognizing that local variation in demographic rates may require locally tailored intervention frequency that is difficult to fully asses a priori, or that population and transmission dynamics may change in unpredictable ways in between scheduled SIAs, Lessler et al Lessler 2016 suggested that campaigns may be triggered by observable metrics, such as serological surveys, that are indicative of high outbreak risk.  This analysis suggests that triggered campaigns can be more effective at reducing measles burden that relying on a pre-determined SIA schedule.  Given the costs in both the necessary surveillance system with which to trigger such campaign and the reactive implementation of campaigns, dynamic models are a useful tool for a priori assessment of the potential impact of such programs.  Here, the interventions themselves are tied to the dynamics of the system.  Thus, understanding of the expected dynamics, i.e. trends or cycles, and the variation in those dynamics, i.e. stochastic fluctuations, is critical to develop reactive systems that meet operational needs for sensitivity and specificity.  By comparison, prior to the introduction of a conjugate vaccine, reactive vaccination campaigns for outbreaks of meningococcal meningitis in the African meningitis belt were triggered when reported cases increased above an operational outbreak threshold; high thresholds result in slow response with low potential impact to limit the outbreak, low thresholds trigger frequent, costly interventions in locations that may not necessarily experience large outbreaks, which draws resources away from other areas in need. Defining operational thresholds required an understanding of the temporal variation in outbreak risk, meningitis outbreaks are highly seasonal and are correlated with environmental conditions, and the stochastic nature of outbreak spread Lewis 2001.