1. Showcase 2005 05/06/2005 Towards Computational Epidemiology Using Stochastic Cellular Automata in Modeling Spread of Diseases Sangeeta Venkatachalam, Armin R. Mikler Computational Epidemiology Research Laboratory University of North Texas Email: {venkatac, mikler}@cs.unt.edu This research is in part supported by the National Science Foundation award: NSF-0350200

2. Showcase 2005 05/06/2005 Overview o Mathematical Epidemiology o Cellular Automata and Epidemiology o Stochastic Cellular Automata - A Global Model o Composition Model o Experiments o Summary

3. Showcase 2005 05/06/2005 Mathematical Epidemiology Susceptibles Infectives Removals (SIR) model ◦ A SIR model simulation of a disease spread ◦ The graph shows the transient curves for the susceptibles, infectives and removals during the course of a disease epidemic in a given population. SIR state diagram

4. Showcase 2005 05/06/2005 Susceptibles Infectives Removals (SIR) model o Homogeneous mixing of people o Every individual makes same contacts o No demographics considered o Geographical distances not considered

5. Showcase 2005 05/06/2005 The Model Vaccination Population Demographics Disease Parameters Data Sets Visualization Interaction factors Distances

6. Showcase 2005 05/06/2005 Parameters considered o Latent period o Infectious period o Contact o Infectivity o Population o Index case  Multiple index cases  Location of index case Illustrates time-line for infection (influenza)

7. Showcase 2005 05/06/2005 Stochastic Cellular Automata A Global Model Definition of a Fuzzy Set Neighborhood of cell Ci,j is global SCA G i,j := {(C k,l, Υ C i,j, C k,l ) |for all C k,l Є C, 0 ≤ Υ C i,j, C k,l ≤ 1} C is a set of all cells in the CA. Υ C i,j, C k,l represents an interaction coefficient that controls all possible interactions between a cell Ci,j and its global neighborhood Gi,j. A function of inter-cell distance and cell population density.

8. Showcase 2005 05/06/2005 Interaction Metrics Interaction Coefficient defined as 1/Euclidean distance between the cells Interaction coefficient based on distance Interaction coefficient based on distance and population Global Interaction Coefficient Infection factor is calculated as the ratio of interaction coefficients between the cells and the global interaction coefficient

9. Showcase 2005 05/06/2005 Experiments o Spread of a disease for different contact rates. o Disease parameters o Contact rates of 8, 15, 25 o Infectivity of 0.005 o As the contact rate decrease spread of disease is slower and prolonged. o Spread of different diseases on a specific population with fixed contact rate. o Disease parameters such as latency, infectious period, infectivity and recovery different with respect to a disease. o The graph illustrates different diseases spread differently in a given population set. Spread of a disease for different contact rates. Spread of different diseases in a given population

10. Showcase 2005 05/06/2005 Experiments – Behavior change ◦ Assumption : Sick or infected individuals are less likely to make contacts during the infectious period. ◦Model adjusts the contact rate of individuals based on the number of days infected. ◦The graph compares the infection spread for the model with the behavior change and without behavior changes. ◦Infection spread is slower if behavioral change is considered.

11. Showcase 2005 05/06/2005 Distance dependence of disease spread Comparison of spread of disease considering and not considering distance dependence for contacts ◦ Assumption : Individual is more likely to make contact with some one closer than some one farther. ◦Spread of disease is slower when the assumption is considered. ◦Spread of disease is distance dependent

12. Showcase 2005 05/06/2005 Composition Model Assumption : Sub-regions (or cells) with a larger proportion of a certain demographic may display increased or decrease prevalence of a certain disease as compared to a sub- region with a larger proportion of a different demographic Cell interaction is controlled by age proportions and population densities. Composition model reflects the spread of the infection in each sub- region. Observed Cumulative Epidemic caused by Temporally and Spatially Distributed Local Outbreaks

13. Showcase 2005 05/06/2005 Composition Model -Experiment Simulation parameters: Disease Simulated : Influenza like disease Incubation period : 3 days Infectious period: 3 days Recovery period: 5 days Infectivity : 0.020 Contact rate/person : 11 o The population distribution over the region is non-uniform. o Contacts made between cells depends on the population of the cell. o Assumption : Regions with high population make more contacts than regions with low population.

14. Showcase 2005 05/06/2005 Composition Model -Experiment Population distribution over the north Denton region. Total Population of 110000 distributed over a grid size of 50 * 100. Total Population infected at the end of simulation: 48000 Infected Population distribution over the north Denton region.

15. Showcase 2005 05/06/2005 Contact Rate o Contact rate defines the number of contacts an individual is involved in a day. o May vary depending on the age or occupation of the individual. o Contact is considered as any situation which may lead to a successful disease transmission. d o The graph illustrates the epidemic curves for the same disease parameters with varying contact rates. o Can be thought of as for different demographics such as age groups and occupation. o Evidently incidence is lower for lower rates of contact.

16. Showcase 2005 05/06/2005 Experiment-- Infectivity Epidemic curves for varied levels of infectivity o The probability of a contact resulting in a successful disease transmission depends on the infectivity/virulence parameter o Experiment was conducted to analyze the prevalence of influenza for varied levels of infectivity. o Incidence is lower for lower levels of infectivity.

17. Showcase 2005 05/06/2005 Experiment-- Immunity o The probability of a contact with an infectious person resulting in a successful disease transmission depends on the immunity of the individual. o Experiment was conducted considering that people residing in a particular region were immune to the particular virus as means of either vaccination or previous infection. o The results show lower level of prevalence of disease in that region compared to other regions. Region Immunized

18. Showcase 2005 05/06/2005 Composition Model – Modeling Distance Dependence o Dichotomy introduced between local and global interactions. o Global interactions are between any two cells in the grid o Local interactions are within a Manhattan block distance of given distance k. o Incidence of disease prevalence decreases with higher proportions of local mixing. o Disease prevails over a longer period of time with higher proportions of local mixing. Epidemic curves for the different rates of global and local mixing.

19. Showcase 2005 05/06/2005 Spatial Spread of Influenza simulated over Northern Denton County with local contacts

20. Showcase 2005 05/06/2005

24. Index case

25. Showcase 2005 05/06/2005

45. Spatial Spread of Influenza simulated over Northern Denton County with local and global contacts

46. Showcase 2005 05/06/2005

68. Summary o Designing tools for investigating local disease clusters through simulation. o What’s New?  Utilizing GIS and EPI information for modeling  Combining different simulation paradigms  Designing of a Global Stochastic CA o The goal: Contribute to establish computational epidemiology as a new research domain.