Event coincidence analysis for quantifying statistical interrelationships between event time series

On the role of flood events as triggers of epidemic outbreaks

The European Physical Journal Special Topics volume 225, pages 471–487 (2016)Cite this article

Abstract

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.

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Affiliations

  1. Potsdam Institute for Climate Impact Research, Telegrafenberg A31, 14473, Potsdam, Germany

    J.F. Donges, C.-F. Schleussner, J.F. Siegmund & R.V. Donner

  2. Stockholm Resilience Centre, Stockholm University, Kräftriket 2B, 114 19, Stockholm, Sweden

    J.F. Donges

  3. Climate Analytics, Friedrichstr. 231, Haus B, 10969, Berlin, Germany

    C.-F. Schleussner

  4. Institute of Earth and Environmental Science, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476, Potsdam-Golm, Germany

    J.F. Siegmund

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  1. J.F. Donges
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  2. C.-F. Schleussner
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  3. J.F. Siegmund
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  4. R.V. Donner
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Correspondence to J.F. Donges.

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Donges, J., Schleussner, CF., Siegmund, J. et al. Event coincidence analysis for quantifying statistical interrelationships between event time series. Eur. Phys. J. Spec. Top. 225, 471–487 (2016). https://doi.org/10.1140/epjst/e2015-50233-y

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