Early warning signals for emerging infectious diseases

Establishing early warning systems for infectious disease outbreaks could save millions of lives by enabling rapid response and containment. One promising approach draws on the concept of critical slowing down (CSD)—a phenomenon in which complex systems lose resilience before tipping points—detected using resilience indicators (RIs) derived from the statistical properties of time series. While disease outbreaks may exhibit such early warning signals of critical transitions, most prior applications of CSD theory to global health have been limited to single diseases or locations, without broad assessments of predictive accuracy or lead time—the interval between the detection of warning signals and the onset of an outbreak. To address these limitations, we integrate CSD theory with time-to-event analyses to evaluate the predictive performance of 17 RIs across 31 infectious diseases in 134 regions worldwide. We find that both RIs and time- to-event analyses provide ample time to implement control measures, reliably anticipating outbreaks with a mean lead time of 17–21 days. Lead time was greater for pathogens with longer incubation periods and in regions with higher Human Development Index. Additionally, temperature and precipitation exhibited unimodal effects on lead time predictions for vector-borne and viral diseases. These findings highlight the value of incorporating socio-environmental drivers into outbreak forecasting models and lay the foundation for a local-to-global early warning system capable of guiding proactive public health interventions.