  Studying event time series is a powerful approach for analyzing the dynamics
of complex dynamical systems in many fields of science. In this paper, we
describe the method of event coincidence analysis to provide a framework for
quantifying the strength, directionality and time lag of statistical
interrelationships between event series. Event coincidence analysis allows to
formulate and test null hypotheses on the origin of the observed
interrelationships including tests based on Poisson processes or, more
generally, stochastic point processes with a prescribed inter-event time
distribution and other higher-order properties. Applying the framework to
country-level observational data yields evidence that flood events have acted
as triggers of epidemic outbreaks globally since the 1950s. Facing projected
future changes in the statistics of climatic extreme events, statistical
techniques such as event coincidence analysis will be relevant for
investigating the impacts of anthropogenic climate change on human societies
and ecosystems worldwide.
