Models that provide risk maps for zoonoses often lack (i) a spatiotemporal autocorrelation component, yet crucial in understanding the spread of infectious diseases, (ii) accounting for heterogeneity in case reporting, and (iii) a causal framework for explanatory variables. Here, we addressed these limitations with a model system, West Nile virus, a vector-borne pathogen transmitted in a bird reservoir, and affecting humans and horses. We built a spatiotemporal occupancy model and fitted it to notified (human and horse) case data. Based on a directed acyclic graph, we estimated the causal effects of conjectural weather variables (i.e. changing in the short-term) vs. structural variables (i.e. changing in the long-term) on WNV circulation in the bird reservoir, besides assessing variables associated with case reporting. By computing population attributable fractions, we found the contribution of conjectural weather variables to WNV outbreaks in Europe to be globally higher than the structure of the bird community.
J. Bastard, C. Assaad, R. Marti et al.· medRxiv· 0 citations
This paper introduces Regime-aware Constraint-Based and Noise-Based causal discovery with Markov Blankets (RCBNB-MB), a novel causal discovery algorithm for time series that relaxes the common assumption of a single, time-consistent causal structure. Time series are typically observed at discrete time points and often exhibit regime changes that challenge the assumption of a static causal structure, a limitation in many real-world dynamic systems. To address this challenge, RCBNB-MB identifies latent causal regimes, defined as subsets of time points within which a stable causal structure holds. The algorithm follows an iterative strategy that segments the time series into regimes and discovers the causal graph within each regime. By leveraging the Markov blanket rather than direct parents, RCBNB-MB gains robustness to errors in causal discovery and preserves predictive information. We provide theoretical guarantees for RCBNB-MB's ability to recover both regime transitions and causal graphs under reasonable assumptions. Furthermore, we validate its effectiveness through extensive experiments on simulated datasets with known ground truth and real-world IT monitoring data, where taking into account regime shifts is critical. Empirical results show that RCBNB-MB systematically outperforms baseline approaches in accurately detecting regime changes and their associated causal graphs, positioning it as a robust and versatile framework for non-stationary time series analysis.
Lei Zan, C. Assaad, Emilie Devijver et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
