The COVID-19 pandemic revealed that global health, social systems, and economies can be surprisingly fragile in an increasingly interconnected and interdependent world. Yet, during the last half of 2022, and quite remarkably, we began dismantling essential infectious disease monitoring programs in several countries. Absent such programs, localized biological risks will transform into global shocks linked directly to our lack of foresight regarding emerging health risks. Additionally, recent studies indicate that more than half of all infectious diseases could be made worse by climate change, complicating pandemic containment. Despite this complexity, the factors leading to pandemics are largely predictable but can only be realized through a well-designed global early warning system. Such a system should integrate data from genomics, climate and environment, social dynamics, and healthcare infrastructure. The glue for such a system is community-driven modeling, a modern logistics of data, and democratization of AI tools. Using the example of dengue fever in Brazil, we can demonstrate how thoughtfully designed technology platforms can build global-scale precision disease detection and response systems that significantly reduce exposure to systemic shocks, accelerate science-informed public health policies, and deliver reliable healthcare and economic opportunities as an intrinsic part of the global sustainable development agenda.

翻译：COVID-19大流行揭示出，在一个日益互联且相互依存的世界中，全球卫生体系、社会系统及经济体系可能异常脆弱。然而，值得注意的是，在2022年下半年，多个国家开始逐步拆除关键传染病监测项目。若无此类项目，局部生物风险将因我们对新兴健康风险缺乏预见而直接演变为全球性冲击。此外，近期研究表明，超过半数的传染病可能因气候变化而恶化，使大流行防控更加复杂。尽管存在这种复杂性，导致大流行的因素在很大程度上是可预测的，但唯有通过精心设计的全球早期预警系统才能实现。该系统应整合基因组学、气候与环境、社会动态以及医疗基础设施等多源数据。支撑该系统的关键要素包括社区驱动建模、现代化数据物流以及人工智能工具的大众化。以巴西登革热为例，我们可展示精心设计的技术平台如何构建全球规模的精准疾病监测与响应系统，从而显著降低系统性冲击的风险、加速基于科学的公共卫生政策制定，并将可靠医疗与经济机遇内化为全球可持续发展议程的基本组成部分。