Traditional pathogen surveillance methods such as contact tracing can now be combined with other data streams, from genomics²⁰ to social media. For instance, improvements in DNA sequencing mean it is now feasible to rapidly reconstruct how a disease outbreak occurred, including tracing it back to source.²¹ An improved understanding of human factors that affect spread,²² such as malnutrition and genetic vulnerabilities, could significantly aid the tracking of epidemics.²³

However, successfully integrating and using these datasets remains a challenge,²⁴ especially in low- and middle-income countries where resources are limited. It is likely that our initial attempts to predict the course of outbreaks will underperform due to siloed systems, data latency, and interoperability barriers. While such systems may nevertheless offer some insight, it is likely to be too coarse or arrive too late to be used as the basis for effective action.²⁵

Several major opportunities exist. An improved understanding of the interactions between pathogens, which affect when and where outbreaks arise, could enable better predictions. A biobank of infection samples from patients would be a valuable resource for both experimental and computational research. And there is considerable potential to train AI on outbreak datasets and use it to make predictions,²⁶ but so far little has been done — in part due to data-access limitations.

The most effective data ecosystems are likely to be decentralised and trust-based, rather than centralised and coercive. West Africa is a good prototype: pathogen surveillance systems there blend formal and informal health intelligence. Truly resilient systems will arise from diversity, redundancy and local agency, not from top-down architectures. One possible model is to create regional “epidemic foresight nodes” that can detect patterns and simulate response scenarios.