Convergent cross mapping (CCM) method has been widely applied to investigate environmental drivers of infectious disease dynamics, particularly for seasonal influenza. However, its robustness to temporal gaps and missing observations—common features in surveillance data—remains largely unexplored. Using seasonal influenza surveillance data from Hong Kong, we systematically assessed the sensitivity of inferred environment–disease relationships to different data-exclusion scenarios, including the removal of low-activity periods and targeted time points. We compared CCM with quasi-binomial generalized linear models (GLM) and the Peter–Clark Momentary Conditional Independence (PCMCI) framework.+
Across all scenarios, CCM-based inference exhibited pronounced sensitivity to data gaps, with both causal strength and inferred relationships varying substantially across gap configurations. In contrast, GLM and PCMCI estimates remained stable, consistently indicating a negative association between ozone and influenza transmission. These findings highlight a critical limitation of CCM when applied to incomplete time series and underscore the need for caution in interpreting causality from gap-affected epidemiological data.