In this presentation we will define Synthetic populations and illustrate the value it provides to modelers and policy makers. Accuracy of models optimizing response to disease, natural disasters, or distributions of various resources among people depends on the accuracy of the knowledge about the population. This knowledge is not limited to demographics, but has to consider geography, ethnography, social connectivity, and many other factors. Synthetic populations are computational representations of every person in a country. They provide an opportunity to probabilistically link multiple datasets into an accurate database that could be then used by modelers to simulate outcomes of interest, such as evacuation routes, distributions of vaccines, optimal location of first responders, etc. With large amounts of information that is publicly available linking multiple databases poses a threat to privacy. Synthetic populations are natural means to provide census-like accuracy without violating anyone’s privacy. Finally, Synthetic populations could be projected into the future so that the forecasts of 2020 epidemics would be based on 2020 projection and not on 2010 data. This is critical for forecasting the consequences of climate change, population aging and depletion of natural resources.