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The COVID-19 pandemic revealed that global health, social systems, andeconomies can be surprisingly fragile in an increasingly interconnected andinterdependent world. Yet, during the last half of 2022, and quite remarkably,we began dismantling essential infectious disease monitoring programs inseveral countries. Absent such programs, localized biological risks willtransform into global shocks linked directly to our lack of foresight regardingemerging health risks. Additionally, recent studies indicate that more thanhalf of all infectious diseases could be made worse by climate change,complicating pandemic containment. Despite this complexity, the factors leadingto pandemics are largely predictable but can only be realized through awell-designed global early warning system. Such a system should integrate datafrom genomics, climate and environment, social dynamics, and healthcareinfrastructure. The glue for such a system is community-driven modeling, amodern logistics of data, and democratization of AI tools. Using the example ofdengue fever in Brazil, we can demonstrate how thoughtfully designed technologyplatforms can build global-scale precision disease detection and responsesystems that significantly reduce exposure to systemic shocks, acceleratescience-informed public health policies, and deliver reliable healthcare andeconomic opportunities as an intrinsic part of the global sustainabledevelopment agenda.

The Challenges and Opportunities in Creating an Early Warning System for Global Pandemics