  Agent-Based Models are very useful for simulation of physical or social
processes, such as the spreading of a pandemic in a city. Such models proceed
by specifying the behavior of individuals (agents) and their interactions, and
parameterizing the process of infection based on such interactions based on the
geography and demography of the city. However, such models are computationally
very expensive, and the complexity is often linear in the total number of
agents. This seriously limits the usage of such models for simulations, which
often have to be run hundreds of times for policy planning and even model
parameter estimation. An alternative is to develop an emulator, a surrogate
model that can predict the Agent-Based Simulator's output based on its initial
conditions and parameters. In this paper, we discuss a Deep Learning model
based on Dilated Convolutional Neural Network that can emulate such an agent
based model with high accuracy. We show that use of this model instead of the
original Agent-Based Model provides us major gains in the speed of simulations,
allowing much quicker calibration to observations, and more extensive scenario
analysis. The models we consider are spatially explicit, as the locations of
the infected individuals are simulated instead of the gross counts. Another
aspect of our emulation framework is its divide-and-conquer approach that
divides the city into several small overlapping blocks and carries out the
emulation in them parallelly, after which these results are merged together.
This ensures that the same emulator can work for a city of any size, and also
provides significant improvement of time complexity of the emulator, compared
to the original simulator.
