in silico Surveillance: Highly detailed agent-based models for surveillance system evaluation and design

Modern public health surveillance systems have great potential for improving public health. However, evaluating the performance of surveillance systems is challenging because examples of baseline disease distribution in the population are limited to a few years of data collection. Agent-based simulations of infectious disease transmission in highly detailed synthetic populations can provide unlimited realistic baseline data.

Objective

To create, implement, and test a flexible methodology to generate detailed synthetic surveillance data providing realistic geo-spatial and temporal clustering of baseline cases.