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Population censuses are vital to public policy decision-making. They provideinsight into human resources, demography, culture, and economic structure atlocal, regional, and national levels. However, such surveys are very expensive(especially for low and middle-income countries with high populations, such asIndia), time-consuming, and may also raise privacy concerns, depending upon thekinds of data collected.  In light of these issues, we introduce SynthPop++, a novel hybrid framework,which can combine data from multiple real-world surveys (with different,partially overlapping sets of attributes) to produce a real-scale syntheticpopulation of humans. Critically, our population maintains family structurescomprising individuals with demographic, socioeconomic, health, and geolocationattributes: this means that our ``fake'' people live in realistic locations,have realistic families, etc. Such data can be used for a variety of purposes:we explore one such use case, Agent-based modelling of infectious disease inIndia.  To gauge the quality of our synthetic population, we use both machinelearning and statistical metrics. Our experimental results show that syntheticpopulation can realistically simulate the population for various administrativeunits of India, producing real-scale, detailed data at the desired level ofzoom -- from cities, to districts, to states, eventually combining to form acountry-scale synthetic population.

Synthpop++: A Hybrid Framework for Generating A Country-scale Synthetic Population