Public health prioritizes community medical conditions and population health factors. Promoting population health and preventing disease outbreaks and epidemics are the main goals. Targeting populations based on territorial factors, socio-economic and environmental determinants, and phenotypic profiles is essential for developing precise preventive or health promotion measures. Digital Twins (DTs) technology enables data acquisition, hypothesis generation, and in-silico experiments and comparisons. Thanks to Internet of Things and Artificial Intelligence, digital twins can collect a wider range of real-time data from various sources in addition to traditional data sources like Electronic Health Records. Thus, comprehensive simulations of physical entities, their functionality, and their evolution can be created and maintained. This position paper proposes using DT technology, Public Health instruments, knowledge graphs, and AI to enable Precision and Predictive Public Health for population health. In particular, it introduces Neuro-symbolic DTs, which combine semantic reasoning supported by a knowledge graph, deep-learning’s predictive power, and a DT’s agility to simulate public health interventions in a virtual environment.< Leer menos