  Population censuses are vital to public policy decision-making. They provide
insight into human resources, demography, culture, and economic structure at
local, regional, and national levels. However, such surveys are very expensive
(especially for low and middle-income countries with high populations, such as
India), time-consuming, and may also raise privacy concerns, depending upon the
kinds 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 synthetic
population of humans. Critically, our population maintains family structures
comprising individuals with demographic, socioeconomic, health, and geolocation
attributes: 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 in
India.
  To gauge the quality of our synthetic population, we use both machine
learning and statistical metrics. Our experimental results show that synthetic
population can realistically simulate the population for various administrative
units of India, producing real-scale, detailed data at the desired level of
zoom -- from cities, to districts, to states, eventually combining to form a
country-scale synthetic population.

Machine Learning;Computers and Society