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

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

METHODS Dynamic social networks for the Boston area (4.1 million individuals) were constructed based on data for individuals, locations, and activity patterns collected from the real world. We modeled a full season of endemic influenza-like illnesses (ILI), healthcare seeking behavior, and a surveillance system for outpatient visits. The resulting in silico surveillance data contains the demographics and complete history of disease progression for all individuals in the population; those who are in a specified surveillance system create a data stream of ILI visits. Outbreaks of influenza are artificially inserted into this surveillance data. Outbreak detection using space-and-time scan statistics was used to analyze the background with and without the inserted outbreaks. The performance of the algorithm was assessed under different levels of coverage and catchment distributions. One hundred unique baseline data sets were generated. Twelve artificial outbreaks were inserted in each. Six different surveillance system designs were assessed.

RESULTS We detected 32% of outbreaks while generating 1 false positive every 392 days. With tripled surveillance capture we detected 74% outbreaks with 1 false positive every 139 days. Other surveillance system designs had less dramatic effects.

CONCLUSIONS Highly detailed simulations of infectious disease transmission can be configured to represent nearly infinite scenarios, making them a powerful tool for evaluating the performance of surveillance systems and the methods used for outbreak detection.