Refuting causal relations for synchronized pathogen dynamics

Identifying causal relations is a critical challenge in the study of ecological systems, particularly for studying the spread of pathogens in host populations. Whether dealing with measles in humans, foot and mouth disease in livestock, or white-nose syndrome in bats—understanding causal relations is essential for understanding the underlying factors driving growth, decline and re-emergence of pathogen dynamics. This knowledge is vital for informing policy and management strategies aimed at maintaining ecological balance or controlling the spread of pathogens. Unfortunately, existing tools designed to identify causal directions from disease incidence data, especially in synchronized systems, often suffer from high false-detection rates. To address this challenge, we propose Bootstrap Comparison of Attractor Dimensions (BCAD), a novel method that focuses on refuting false causal relations using a dimensionality-based criterion. We test the performance of BCAD, demonstrating its efficacy in correctly refuting false causal relations on two datasets: a model system that consists of two strains of a pathogen driven by a common environmental factor, and a real-world pneumonia and influenza incidence time series from the United States. We compare BCAD to Convergent Cross Mapping (CCM), a prominent method of causal detection in nonlinear systems. In both datasets, BCAD correctly refutes the vast majority of spurious causal relations which CCM falsely detects as causal. The utility of BCAD is emphasized by the fact that our models and data displayed synchrony, a situation known to challenge other causal detection methods. In conclusion, we demonstrate that BCAD is a useful tool for refuting false causal relations in nonlinear dynamical systems arising in the study of the spread of pathogens. BCAD offers an approach for discerning true causal relations from false ones and may also find applicability beyond the study of pathogens and their spread in host populations.