Projecting scenarios for the spread of emerging infectious diseases well ahead of a possible next “Disease X” pandemic is a critical global priority. To assess the spillover and diffusion risk of pathogens in source regions, a One-Health approach calls for the simultaneous monitoring of (i) key reservoir species to detect zoonotic emergence, and (ii) local human presence and mobility patterns toward large-scale spread hubs. We propose a data-driven framework to face both challenges, by innovatively estimating the risk of disease emergence and spread from high-resolution ecological and mobile-phone data. Indonesia and India were selected as focal case studies to demonstrate our method for Nipah (or a Nipah-like Disease-X) circulating in Pteropodidae bats, known reservoirs of WHO Blueprint viruses. Using a multicriterial framework, we couple the hazard – from IUCN spatial data on suitability and distributions of reservoir species - with the exposure, proxied through fine-scale mapping of human presence from a large World Bank dataset of mobile devices. In so doing, we show how the likelihood of zoonotic spillover correlates with key ecological stressors - such as extent and rate of deforestation - and how development drivers (like expanding tourism) may amplify the consequent spread potential. Building upon the estimated spillover risk, we assessed the threat of disease spreading to airports and ranked them by potential disease transmission risk (PDTR). Contrary to the common assumption that air traffic alone may satisfactorily capture PDTR, we find that minor yet crucially located airports can be pivotal to control strategies. Overall, our findings underscore the urgency of integrating key ecological drivers – such as habitat transformation – to improve the design of next-generation policy plannings. At the same time, our method provides decision-makers with an innovative tool to identify nodes and connections of greatest vulnerability at regional scales that must be prioritized to avoid international threats.