Comparative Approaches for Detecting Critical Transitions in Food Crises

Anticipating and defining food crises remain primary challenges for humanitarian and governmental actors [1]. Traditional frameworks rely on predefined risk thresholds for different levels of food intake, but they neglect sudden-onset or "flash" events that abruptly alter the status quo [2]. This research proposes a data-driven methodology to identify and characterize these events, framing them as critical transitions in food security. By leveraging high-frequency, district-level data of food intake, we examine the evolution of food consumption across highly vulnerable countries and compare these findings with qualitative assessments from domain experts.

Building on previous research, this work evaluates the efficacy of multiple quantitative methods, ranging from time series analysis (variance, autocorrelations), unsupervised statistical change-point detection [3], dynamical systems theory [4], and deep learning [5], for defining food crises directly from raw data streams. To validate this framework, we first present results from synthetic experiments designed to simulate the noisy, daily measurements typical of this setting. Then, we assess the capacity of these methods to discern system-wide changes to real-world events. These experiments showcase the feasibility of objectively distinguishing between noise and genuine system transitions.

This study highlights the necessity of moving beyond static metrics toward a multi-method detection framework. We aim to provide humanitarian actors with a data-driven trigger for intervention, ensuring that flash deteriorations are no longer obscured by the limitations of static indicators and noisy measurements. Ultimately, this unified approach contributes to the development of more effective early warning systems and supports evidence-based decision-making for global food security.

References:

[1] P. Foini, M. Tizzoni, G. Martini, D. Paolotti, and E. Omodei, ‘On the forecastability of food insecurity’, Sci Rep, 2023, doi: 10.1038/s41598-023-29700-y.

[2] Herteux et al., ‘Forecasting trends in food security with real time data’, Commun Earth Environ, 2024, doi: 10.1038/s43247-024-01698-9.

[3] Wu, D., Gundimeda, S., Mou, S., Quinn, C. ‘Unsupervised Change Point Detection in Multivariate Time Series’, AISTATS 2024, PMLR,  https://proceedings.mlr.press/v238/wu24g.html

[4] Zoeter, Onno, and Tom Heskes, ‘Change point problems in linear dynamical systems’, JMLR, 2005, https://www.jmlr.org/papers/volume6/zoeter05a/zoeter05a.pdf

[5] T. De Ryck, M. De Vos and A. Bertrand, ‘Change Point Detection in Time Series Data Using Autoencoders With a Time-Invariant Representation,’ IEEE Tran Signal Process, 2021, doi: 10.1109/TSP.2021.3087031