ISDS Annual Conference Proceedings 2017. This is an Open Access article distributed under the terms of the Creative Commons Attribution-
Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution,  
and reproduction in any medium, provided the original work is properly cited.

ISDS 2016 Conference Abstracts

A Spatial Biosurveillance Synthetic Data Generator in R
Drew Levin* and Patrick Finley
Sandia National Laboratories, Albuquerque, NM, USA

Objective
To develop a spatially accurate biosurveillance synthetic data 

generator for the testing, evaluation, and comparison of new outbreak 
detection techniques.

Introduction
Development of new methods for the rapid detection of emerging 

disease outbreaks is a research priority in the field of biosurveillance. 
Because real-world data are often proprietary in nature, scientists must 
utilize synthetic data generation methods to evaluate new detection 
methodologies. Colizza et. al. have shown that epidemic spread is 
dependent on the airline transportation network [1], yet current data 
generators do not operate over network structures.

Here we present a new spatial data generator that models the 
spread of contagion across a network of cities connected by airline 
routes. The generator is developed in the R programming language 
and produces data compatible with the popular ̀ surveillance’ software 
package.

Methods
Colizza et. al. demonstrate the power-law relationships between 

city population, air traffic, and degree distribution [1]. We generate a 
transportation network as a Chung-Lu random graph [2] that preserves 
these scale-free relationships (Figure 1).

First, given a power-law exponent and a desired number of cities, 
a probability mass function (PMF) is generated that mirrors the 
expected degree distribution for the given power-law relationship. 
Values are then sampled from this PMF to generate an expected 
degree (number of connected cities) for each city in the network. 
Edges (airline connections) are added to the network probabilistically 
as described in [2]. Unconnected graph components are each joined 
to the largest component using linear preferential attachment. Finally, 
city sizes are calculated based on an observed three-quarter power-
law scaling relationship with the sampled degree distribution.

Each city is represented as a customizable stochastic compartmental 
SIR model. Transportation between cities is modeled similar to [2]. 
An infection is initialized in a single random city and infection counts 
are recorded in each city for a fixed period of time. A consistent 
fraction of the modeled infection cases are recorded as daily clinic 
visits. These counts are then added onto statically generated baseline 
data for each city to produce a full synthetic data set. Alternatively, 
data sets can be generated using real-world networks, such as the one 
maintained by the International Air Transport Association.

Results
Dynamics such as the number of cities, degree distribution power-

law exponent, traffic flow, and disease kinetics can be customized. 
In the presented example (Figure 2) the outbreak spreads over a 20 
city transportation network. Infection spreads rapidly once the more 
populated hub cities are infected. Cities that are multiple flights away 
from the initially infected city are infected late in the process. The 
generator is capable of creating data sets of arbitrary size, length, and 
connectivity to better mirror a diverse set of observed network types.

Conclusions
New computational methods for outbreak detection and 

surveillance must be compared to established approaches. Outbreak 
mitigation strategies require a realistic model of human transportation 
behavior to best evaluate impact. These actions require test data that 
accurately reflect the complexity of the real-world data they would 

be applied to. The outbreak data generated here represents the 
complexity of modern transportation networks and are made to be 
easily integrated with established software packages to allow for rapid 
testing and deployment.

Randomly generated scale-free transportation network with a power-law 
degree exponent of λ=1.8. City and link sizes are scaled to reflect their weight.

An example of observed daily outbreak-related clinic visits across a randomly 
generated network of 20 cities. Each city is colored by the number of flights 
required to reach the city from the initial infection location. These generated 
counts are then added onto baseline data to create a synthetic data set for 
experimentation.

Keywords
Simulation; Network; Spatial; Synthetic; Data

Acknowledgments
Funding provided by Sandia National Laboratories LDRD program. 
Sandia National Laboratories is a multi-mission laboratory managed and 
operated by Sandia Corporation, a wholly owned subsidiary of Lockheed 
Martin Corporation, for the U.S. Department of Energy’s National Nuclear 
Security Administration under contract DE-AC04-94AL85000.

References
[1] V. Colizza, A. Barrat, M. Barthelemy, and A. Vespignani. The role of 

the airline transportation network in the pre-diction and predictability 
of global epidemics. Proceedings of the National Academy of 
Sciences, 103(7):2015–2020, feb 2006.

[2] Fan Chung and Linyuan Lu. Connected Components in Random 
Graphs with Given Expected Degree Sequences. Annals of 
Combinatorics, 6(2):125–145, nov 2002

*Drew Levin
E-mail: dlevin@sandia.gov

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 9(1):e6, 2017