No evidence for critical slowing down before measles outbreaks in the US, 2006–2025

AIMS: Measles, once eliminated in the United States, has resurged with major outbreaks in 2014, 2018/2019, and 2025. Anticipating such resurgences is a public health priority. Early warning signals (EWS) based on critical slowing down predict increases in variance and autocorrelation near epidemic thresholds. We assessed the reliability of these statistical EWS for anticipating US measles outbreaks. METHODS: We analyzed weekly US measles case data from 2006-2025, harmonizing US Centers for Disease Control and Prevention (CDC) surveillance reports with a curated domestic case dataset. Outbreaks were defined using baseline thresholds, identifying three major events. Using the spaero R package, we estimated variance and lag-1 autocorrelation in rolling windows and tested for trends using autocorrelation-adjusted Spearman correlations. Sensitivity analyses evaluated robustness to detrending and bandwidth choices. RESULTS: No consistent increases in variance or autocorrelation were detected in the full time series or in pre-outbreak windows. Outbreak duration, size, and fade-out frequency showed no significant trends across the study period. CONCLUSIONS: Statistical EWS did not reliably anticipate US measles outbreaks. More research is needed to evaluate their applicability to vaccine-preventable disease resurgence, particularly in settings shaped by heterogeneous susceptibility and outbreak responses.