2025/08/25 by David Danko, John C Papciak, James Golden +9 · 1 voice
Mathematics · Medicine · #COVID-19 epidemiological studies #Viral Infections and Outbreaks Research #Zoonotic diseases and public health
paper · pdf · doi:10.1016/j.crsus.2025.100485
openalex publication_date 2025/08/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22
The COVID-19 pandemic's lessons on the interconnectedness of human health with the natural world, social systems, and economies have been ignored with the dismantling of essential infectious-disease-monitoring programs across the world. Furthermore, over half of all infectious diseases could be made worse by climate change, complicating pandemic containment. Despite these complexities, the factors leading to pandemics are largely predictable and can be realized through a well-designed global early warning system. We have developed and deployed the first climate-informed global infectious disease platform, GeoSeeq. It integrates data from genomics, climate and environment, social dynamics, and healthcare infrastructure. It leverages community-driven modeling, modern logistics of data, and democratization of AI tools. Using the example of dengue fever in Brazil, we demonstrate how technology platforms can build global-scale precision disease detection and response systems to reduce exposure to systemic shocks, accelerate science-informed public health policies, and deliver reliable healthcare and economic opportunities.