1. A Data Intensive High Performance Simulation & Visualization Framework for Disease Surveillance Arif Ghafoor, David Ebert, Madiha Sahar Ross Maciejewski, Shehzad Afzal, Farrukh Arslan Acknowledgement: Project Partially Funded by Cyber Center

2. Objective and Goals Objective: To address the infectious disease surveillance challenges and develop a collaborative capability for all the stakeholders for monitoring and managing outbreaks infectious diseases in large cities Objective: To address the infectious disease surveillance challenges and develop a collaborative capability for all the stakeholders for monitoring and managing outbreaks infectious diseases in large cities Approach: Develop a high performance computing (HPC) framework employing robust and novel infectious disease epidemiology models with real-time inference and pre/exercise planning capabilities. Approach: Develop a high performance computing (HPC) framework employing robust and novel infectious disease epidemiology models with real-time inference and pre/exercise planning capabilities.

3. Objective and Goals Real-time data analysis capabilities, providing a model for infrastructure development where lessons learned can be used to develop best practice models Real-time data analysis capabilities, providing a model for infrastructure development where lessons learned can be used to develop best practice models A comparative assessment of disease modeling techniques by focusing on the tradeoff between the level of granularity used in creating the model and the model efficacy A comparative assessment of disease modeling techniques by focusing on the tradeoff between the level of granularity used in creating the model and the model efficacy Novel visual analytics paradigms integrating decision support and resource allocation tools with live streaming data and disease simulation scenarios Novel visual analytics paradigms integrating decision support and resource allocation tools with live streaming data and disease simulation scenarios

4. 4 Conceptual view of Proposed Infectious Disease Surveillance Framework

5. Tasks: Task A: Data Intensive Multi- Resolution Simulation Modeling Task A: Data Intensive Multi- Resolution Simulation Modeling Task B: High Performance Simulation Modeling on HADOOP Task B: High Performance Simulation Modeling on HADOOP

6. Task A: Initial Research Results Challenge: The notion of context, is important for syndromic surveillance. For syndromic data set we need: Challenge: The notion of context, is important for syndromic surveillance. For syndromic data set we need: Contextual attributes Contextual attributes Behavioral attributes Behavioral attributes We have proposed an HPC data mining framework for contextual and behavioral attributes using Syndrome Ontology (Assumption: Domain Knowledge is available) We have proposed an HPC data mining framework for contextual and behavioral attributes using Syndrome Ontology (Assumption: Domain Knowledge is available) Currently pursuing system Implementation -- WEKA: Machine Learning & Data Mining in Java. (http://www.cs.waikato.ac.nz/ml/weka/index.html) Currently pursuing system Implementation -- WEKA: Machine Learning & Data Mining in Java. (http://www.cs.waikato.ac.nz/ml/weka/index.html)

7. Task A: Data Intensive Multi-Resolution Simulation Modeling (initial results) 7 Proposed HPC framework for mining of contextual (eg. spatio-temporal) and behavioral attributes using Syndrome Ontology. Domain knowledge is available through domain ontology

8. 8 Ontological Syndromic and Climate Classifiers Exploration towards decision trees spanning over distributed multi-domains, representing semantic knowledge at temporal, spatial and socio-economic level.

9. 9 Patien t ID DateAgeGende r LocationChief Complaint s 93981/10/1120FemaleKot BegumFlu 108161/14/1124MaleFaisal ParkChills 14911/27/1128MaleBhammanBodyaches 162372/1/1120FemaleChah MiranAnxiety CoCo Classifier

10. 10 Epidemic Spread Visualization

11. 11 Developing Novel Statistical Heterogeneous Agent Based SIR Model Adding age based and gender based classification Adding age based and gender based classification Demographic impacts on spread rate (socioeconomic classification) Demographic impacts on spread rate (socioeconomic classification) Capturing seasonal trends of disease spread Capturing seasonal trends of disease spread Effect of decision making considering preventive measures (inoculation of population, resource allocation of healthcare) Effect of decision making considering preventive measures (inoculation of population, resource allocation of healthcare)

12. Components of Proposed HPC HADOOP Platform 12

13. Task B: High Performance Simulation Modeling on HADOOP (in progress) Objective: Development of agent-based and multi-granularity homogenous mixing model for HPC-based simulation. Objective: Development of agent-based and multi-granularity homogenous mixing model for HPC-based simulation.

14. TASK B: High Performance Simulation Modeling on HADOOP Development of Agent-Based SIR Model for Heterogeneous Networks Development of Agent-Based SIR Model for Heterogeneous Networks Simulation Based Disease Spread Behavior Simulation Based Disease Spread Behavior Analysis of Decision making for Preventive Measures Analysis of Decision making for Preventive Measures

15. SIR IN HETEROGENEOUS NETWORKS Each node can have three states: Susceptible, Infected, and Recovered (S, I, R) Each node can have three states: Susceptible, Infected, and Recovered (S, I, R) Once infected, a node can transmit infection to neighboring susceptible nodes with a probability β Once infected, a node can transmit infection to neighboring susceptible nodes with a probability β Infected nodes stay infected for a duration d Infected nodes stay infected for a duration d Recovery rate of infected nodes υ is 1/d Recovery rate of infected nodes υ is 1/d Susceptibility of an individual may vary depending upon the number of infected neighbors Susceptibility of an individual may vary depending upon the number of infected neighbors Within a group interaction: Within a group interaction: Figure: State diagram of SIR Model β: probability of getting disease during a contact d: duration of infection υ: Recovery Rate ( 1/d) N: Total Population

16. SOCIAL NETWORK MODELING FOR PREDICTION & MANAGEMENT OF EPIDEMICS Development of an Agent Based social networking model to simulate the infectious disease spread Development of an Agent Based social networking model to simulate the infectious disease spread Population is divided into groups depending upon age, gender, occupation, and location – a phenomenon known as Assortative Mixing Population is divided into groups depending upon age, gender, occupation, and location – a phenomenon known as Assortative Mixing Distribution of contacts play a key role in determining the onset of expansion phase of epidemic Distribution of contacts play a key role in determining the onset of expansion phase of epidemic

17. Population Classification Attributes

18. HETEROGENEOUS GRAPH MODEL FOR MULTI-GROUP POPULATION INTERCATION

19. CURRENT STATUS Development of Heterogeneous Models & evaluation of their fidelity. Simulation in NETLOGO Development of Heterogeneous Models & evaluation of their fidelity. Simulation in NETLOGO Simulation Objectives Simulation Objectives Effect of demographic properties Effect of demographic properties Effect of weather on epidemic disease spread and seasonal trends Effect of weather on epidemic disease spread and seasonal trends Effect of pharmaceutical and other decision measures on epidemic spread Effect of pharmaceutical and other decision measures on epidemic spread

20. Summary and Status Proposed an HPC-based data mining framework for contextual and behavioral attributes using Syndrome Ontology (Assumption: Domain Knowledge is available). Currently pursuing system Implementation --WEKA: Machine Learning & Data Mining in Java. Proposed an HPC-based data mining framework for contextual and behavioral attributes using Syndrome Ontology (Assumption: Domain Knowledge is available). Currently pursuing system Implementation --WEKA: Machine Learning & Data Mining in Java. Development of agent-based SIR heterogeneous population model for HPC-based simulation for large cities (in progress). Development of agent-based SIR heterogeneous population model for HPC-based simulation for large cities (in progress). Proposal (in preparation): Proposal (in preparation): Gates Foundation Grand Challenges Explorations for Global Health Gates Foundation Grand Challenges Explorations for Global Health Potential collaboration with MSR Potential collaboration with MSR