Traditional mechanistic models have tried to dissect the complexity of complex biological and socio-environmental processes. However, because of the boom in data, the need of simplified models, and the recognition of emerging collective-behavior driven patterns, complexity science has raised as the science to purposely decrease the complexity of complex systems by inferring their fundamental processes and providing useful and usable models. The talk will introduce information theoretic models to infer the functional and structural networks - i.e. the "population connectome" - underlying processes leading to complex population patterns. The nature inspired model simulates dynamical processes on complex networks in a Lagrangian framework after learning from data the necessary and sufficient information to reproduce patterns of interests. The model will highlight forecasting and management studies about public health issues related to endemic, epidemic and pandemic infectious diseases (influenza, dengue, and cholera for instance). The utility of the model at smaller socio-biological scales will be shown for population-driven personalized medicine and biological investigation (e.g. for understanding and forecasting pain and identifying biomarker networks). Despite the engineering bias for a systemic epidemiology roadmap, the talk will underline the ability of models to determine universality and scale invariance of population patterns via topological analyses. Reverse engineering uses of the model for multiscale system design, technology fabrication, experiment planning and health/biomedical investigations will be shown. Dr. Convertino is a MnDRIVE Assistant Professor in the School of Public Health, Division of Environmental Health Sciences and Public Health Informatics Program, at the University of Minnesota. At the same institution he is also Faculty Fellow at the Institute on the Environment, Institute for Engineering in Medicine, and at the Bioinformatics and Computational Biology Program. He is actively involved in the Risk Unit of the Center for Animal Health and Food Safety, and in the Ecosystem Health Division both of the College of Veterinary Medicine at the University of Minnesota. Internationally he is a Foreign Fellow of the International Institute for Applied Systems Analysis (IIASA) in Vienna, Austria. Dr. Convertino has been trained in Civil and Environmental Engineering (Structural & Environmental Engineering majors) for the Bachelor of Science, Master in Civil and Environmental Engineering (Fluid Mechanics and Hydrology major), and Civil and Environmental Engineering Sciences (Theoretical and Computational Biocomplexity) for the PhD. His degrees are from the University of Padova, Italy. Dr. Convertino, as director of the HumNat Lab within the large Center for Systems Intelligence and Strategic Design, is highly interested in population pattern analysis, prediction and optimal control.