Towards Computational Epidemiology Designing an Infectious Disease Outbreak Simulator

1. Towards Computational EpidemiologyDesigning an Infectious Disease Outbreak Simulator Armin R. Mikler Department of Computer Science and Engineering Department of Biological Sciences University of North Texas Armin R. Mikler

2. Towards Computational Epidemiology • Address broader aspects of Epidemiology • Disease Tracking, Analysis, and Surveillance • High Performance Computing (HPC) • Simulation • Data visualization. • Design and implement computational tools • investigating Tuberculosis outbreaks and risk assessment in spatially delineated environments • modeling and simulating details of specific instances of Tuberculosis occurrences in North Texas • applicable to a wide variety of disease outbreaks in spatially well-defined settings • Contribute towards establishing computational epidemiology as a new research domain!! Armin R. Mikler

3. Disease Outbreak Model Local Global • Global • Demography • Socio-economics • Travel • Transportation • Geography • Culture • Local • Delineated space • Factory, homeless shelter, school • Airflow • Heating and cooling • Distances in feet • Architectural properties Armin R. Mikler

4. Global Stochastic Cellular Automata and the SWARM Top Layer:Cellular Automata Global Middle Layer: Cellular Automata Regional Bottom Layer: SWARM Local Armin R. Mikler

5. The Focus of Study--Locality based This study proposes to model the dynamics of tuberculosis transmission within two facilities in North Texas - a homeless shelter facility providing both long and short-term occupancy with 800 beds, and a factory. Data was previously collected through interviews during targeted surveillance screening of workers in the factory and homeless people who use the shelter. Data has been Deidentified !!! Armin R. Mikler

6. Homeless Shelter Data and Findings • For the homeless shelter, the data set comprises screening records for each case including: • Date tested (relative to t0) • Status of tuberculosis • Location in the facility • Length of time spent in the facility • Other variables Results of initial analysis suggest that TB risk is not uniformly distributed but depends on the location of the sleeping bed and duration and frequency of stay at the night shelter. Armin R. Mikler

7. Armin R. Mikler

8. Factory Data and Findings • In addition to basic screening records as collected for the homeless shelter, other available data for the factory include measures of duration and proximity to infected person such as: • Hours per week in the factory • Hours per week in the same workspace • Hours per week within 3 feet of infected person • Usual work area. Results of initial analysis indicate that proximity of workspace to infected person was a major determinant of infection. In fact 100% of those who worked directly in the same space with one infected person were infected with the same strain of TB. Armin R. Mikler

9. Factory Layout Armin R. Mikler

10. The Paint Area The Restroom Air vent system The Eating Area Armin R. Mikler

11. Modeling Approaches • Agent based modeling • Level of exposure • Emergent behavior defined by individuals’ actions. • The average number of bacilli that are emitted (through coughing, sneezing, etc.) • Spatial interaction. • Stochastic Cellular Automata • Ambient temperature and airflow • Particle Suspension and Dispersion • Intrinsically stochastic. Armin R. Mikler

12. From GIS data to Agent-Based Simulation to Visualization GIS/ Epidemiologic Data Social Interactions Particle suspension & Airflow Visualization Armin R. Mikler

13. Movement and Desire D S/D A B C D … A - B C B … B A - C C … C A B - D … D C C C - … … … … … … … C A B Agent at (xi, yi) Example of functions that model different types of desire as a function of time. Armin R. Mikler

14. Particle Suspension and Dispersion As a function of time, bacilli settle toward the ground and may spread to neighboring cells Armin R. Mikler

15. State of each cell Ci,j depends on Ci,j+1, Ci,j-1, Ci+1,j, Ci-1,j, Ci+1,j-1, Ci+1,j+1,Ci-1,j-1, Ci-1,j+1 • The color of a cell changes based on the majority color of its neighbors T0 T1 Armin R. Mikler

16. Visualization--‘Simulated’ Simulation Pathogen Content Obstructability Armin R. Mikler

17. Armin R. Mikler

18. Armin R. Mikler

19. Armin R. Mikler

20. Armin R. Mikler

21. Faster hardware and new high-bandwidth networks demand that we explore new cluster architectures. Larger, more complex cluster environments make it imperative to invest in new efficient and scalable tools. Grand Challenge problems will continue to drive the development of computing infrastructure. Distributed HPC will become common place. (DOE SciDAC) Management Tools designed for single hosts or small clusters are likely NOT to scale. New types ofMiddleware is needed to decouple the underlying distributed infrastructure from the applications. The Future: Clusters and the GRID Armin R. Mikler

22. Grid Layers…virtualization Data Grid Comp. Grid Bio Grid i.e., Scientific Discovery through Advance Computing Applications Application-Specific Grid Services (APIs) Middleware General Grid Services Grid Engine Grid Engine Grid Engine Grid Engine Grid Engine Grid Engine Grid Access Internet / Private Networks Armin R. Mikler

23. Matter of Facts…. • There is increasing demand for harnessing computational resources • Increasing demand for Grid-based computing at the private sector • Computing Power will become a commodity like Water, Gas, etc. • As with ISPs, Grid Access Providers (GAPs) will have to guarantee Quality of Service. • Through Grid Services, we can provide a global computing infrastructure and facilitate services for a large number of application domains at the private and public sector! Examples: Healthcare, Education, Industrial R&D, Entertainment, Sciences, etc. Armin R. Mikler

24. Networking Interconnect MASTER NODE Cluster Semantics Cluster Nodes Armin R. Mikler

25. Armin R. Mikler

26. Armin R. Mikler

27. People Behind - The Group Armin R. Mikler

28. A Final Push to Control TB Because the number of cases of TB in the U.S. are lower than they’ve ever been, we have the opportunity to finally control TB in the U.S. Yet little research exists on the dynamics of localized TB transmission in homeless shelters. Recent research suggests that focusing on the dynamics of how TB is transmitted in specific locations is a much-needed final push to TB control. Little attention has been given to places like factories, warehouses, healthcare facilities, or schools where people work in close proximity for long periods of time. Homeless shelters and overcrowded areas constitute reservoirs of TB infection. Armin R. Mikler

29. Cray Y-MP & IBM Power4 • “Common” supercomputer in early 1990's • ~$1 million from Cray • Max speed: 2.3 gigaflops (record speed) • Pentium III 1Ghz processors. Same processors sold “off the shelf” • 64 gigaflops • 198th on Top500 list (http://www.top500.org) Armin R. Mikler

30. Big Mac @ Virginia Tech • Macintosh G5 workstations • Infiniband networking interconnect • 3rd fastest supercomputer in the world Armin R. Mikler

31. Cellular Automata (4 Neighbors – von Newman) • State of each cell Ci,j depends on the neighbors Ci,j+1, Ci,j-1, Ci+1,j, Ci-1,j • For example, the color of a cell depends on the majority color of its neighbors T0 T1 Armin R. Mikler