Event coincidence analysis for quantifying statistical interrelationships between event time series: on the role of flood events as possible triggers of epidemic outbreaks

TL;DR

This paper introduces event coincidence analysis as a method to quantify and test the statistical interrelationships between event time series, demonstrated by evidence linking flood events to epidemic outbreaks since the 1950s.

Contribution

The paper presents a novel framework for analyzing interrelationships between event series, including hypothesis testing with stochastic process models, applied to climate and health data.

Findings

Flood events have historically triggered epidemic outbreaks globally.

Event coincidence analysis can test hypotheses about causal relationships between events.

The method is relevant for studying impacts of climate change on societal and ecological systems.

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.