This poster was featured at the 2013 Perot Museum of Nature and Science's 'Social Science' exhibit. The poster discusses a framework to simulate and visualize epidemics

Camp, Taylor

Helsing, Joseph

Indrakanti, Saratchandra

Mikler, Armin R.

O'Neill, Marty

UNT Digital Library

Public health planners need an understanding of how disease outbreaks propagate in different populations to formulate effective control strategies. To this end, a framework to simulate and visualize infectious disease epidemics in a population was created. Contacts between individuals drive the spread of infectious disease outbreaks. Since actual behavior patterns of the population are unknown, the likelihood of a simulated contact between two individuals is determined by demographic similarities and geographic proximity. Geographic spread of the outbreak is visualized using a color coded map to represent proportions of populations affected. Geographic and demographic data from the U.S. Census Bureau are used in the simulation to facilitate exploration of outbreak propagation in real populations.ABSTRACTPopulation distribution in Dallas County, Texas. Above is the color code used for the heat-map. METHODOLOGYSEIR ModelProgression of the disease within each individual is modeled by moving each individual between the disease states Susceptible                                                              (S), Exposed (E), Infected (I) or Recovered (R)  as shown in the diagram below.Population DistributionThe region is partitioned into census blocks with different population densities. Within a census block, individuals are stratied into demographic groups. Individuals belonging to each group are classied into one of the disease states S, E, I, or R.Population Distribution Model Contact Generation ModelSpread of disease is modeled by means of infectious contacts between demographic groups in the different census blocks.Infectious contacts can either be local (within a single block) or global (between different blocks). Contacts are generated between population groups on the basis of:Contact Rate: The total number of contacts an individual makes with all other individuals in the population per time unit.Mobility: Likelihood of a contact made by an individual with an individual in an external block.Afnity: Likelihood of a demographic group contacting a group in an external block.Reach: A measure of maximum contact distance.VisualizationA color-coded map of the region is used to show the disease dynamics. At every time-step of the epidemic, intensity of the infection in each census block is displayed on the geographic map using a color code. Intensity of infection is measured on the basis of proportion of infected individuals in that block at a given time.EPIDEMIC SPREAD PATTERNThe heat-maps below show the propagation of a simulated epidemic in Dallas County, Texas. The population in one of the census blocks is initially infected to trigger the disease outbreak. The outbreak spreads through the population, reaches a peak, and then subsides. Chronological order of disease spread starting from index blockOutbreak Begins Outbreak ProgressesOutbreak Peaks Outbreak SubsidesHeat-maps displaying the infection state in the county during the course of the epidemic. (Each integer on the scale represents a tenth of the population infected.)Parameters used for the simulationLocal outside age groupLocal within age groupGlobal within age groupGlobal outside age groupA FRAMEWORK TO SIMULATE AND VISUALIZE EPIDEMICSCenter for Computational Epidemiology and Response AnalysisTHE UNIVERSITY OF NORTH TEXASMOTIVATIONWhy do we need epidemic simulation?Planning the response to an epidemic outbreak in advance can help minimize human and monetary costs. •Lack of reliable historical data and under-reporting of diseases complicate planning public health planning. •What factors do we need to account for when building an epidemic spread model?Non-uniform distribution of population over a geographic region. •Differences in daily habits and social interactions of different demographic groups. •Computational overhead - if it takes too long to get results, the model is impractical.•Interaction Coefcients between census blocksSocial-behavioral Characteristics of demographic groupsThe Interaction Coefcient between any two census blocks is a measure of the strength of interaction between them. It is directly proportional to the populations of the participating blocks and inversely proportional to the distance between them. Thus, pairs of census blocks that are more populous and are closer to each other are likely to have a larger number of contacts between them.  Social-behavioral Characteristics are used to determine the likelihood of contacts between different demographic groups. The following parameters are used to capture the social-behavioral characteristics:

A Framework to Simulate and Visualize Epidemics

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A Framework to Simulate and Visualize Epidemics

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