Modern Epidemiology A New Computational Science

Transcription

1 Modern Epidemiology A New Computational Science Facilitating Epidemiological Research through Computational Tools Armin R. Mikler Computational Epidemiology Research Laboratory Department of Computer Science and Engineering University of North Texas EpiGrid 2007

2 Searching for the cause of Death Epidemiology has been in existence for hundreds of years. Its beginnings were motivated by the questions: What causes people to die? The answer to this question would lead to new ways to prevent some of the causes - although the inevitable still cannot be avoided. EpiGrid 2007

3 Medicine Epidemiology Dictionaries offer the following definitions: Medicine - the science and art dealing with the maintenance of health and the prevention, alleviation, or cure of disease. Epidemiology - a branch of medical science that deals with the incidence, distribution, and control of disease in a population. Notice that "art" is missing in the definition for Epidemiology!! EpiGrid 2007

4 Some Historical events in Epidemiology Epidemiologic accounts date back to the time of Hippocrates ( B.C.) and ancient Greeks. In the 1300s, Europe lost 25% of its population of 100 million to the Black Death or Plague. A Smallpox outbreak in 1521 eradicated half of the Aztecs Empire of 3.5 million people. In 1918, the Spanish Flu (pandemic Influenza) caused an excess death of over 20 million world wide. London's Cholera Epidemic in EpiGrid 2007

5 Containing and Controlling the Disease A better understanding how diseases spread among the population has led to greater sophistication in methods that prevent or at least contain outbreaks. Examples: Quarantining entire villages. Mapping of disease clusters (by John Snow in 1854 during London's Cholera Epidemic). Social distancing (quarantining) was advocated during the 1918 Influenza pandemic. Question: How are we dealing with emerging and re-emerging diseases today? EpiGrid 2007

6 Epidemiology Computational Epidemiology Methodical Approaches John Snow's effort to identify the source of the 1854 Cholera epidemic in London was one of the earliest applications of GIS in Epidemiology. EpiGrid 2007

7 Morbidity in Context John Snow s map of cholera cases has led to the identification of the point source of the disease. The Water Pumps on Broad Street! The Geographic Information in addition to the Case Data has established a CONTEXT to display Relationships in Time and Space! EpiGrid 2007

8 A motivating example! The following data was collected in a single retrospective observational study. What was the cause of death?? Excess deaths by gender: Exposed ( Deaths ) Social Class Male Female Both High 180 ( 118 ) 145 ( 4 ) 325 ( 122 ) Middle 179 ( 154 ) 106 ( 13 ) 285 ( 167 ) Low 510 ( 422 ) 196 ( 106 ) 706 ( 528 ) Other 862 ( 670 ) 23 ( 3 ) 885 ( 673 ) Total 1731 ( 1364 ) 470 ( 126 ) 2201 ( 1490 ) EpiGrid 2007

9 more data.no context Excess deaths by age group: Exposed ( Deaths ) Social Class Adult Child Both High 319 ( 122 ) 6 ( 0 ) 325 ( 122 ) Middle 261 ( 167 ) 24 ( 0 ) 285 ( 167 ) Low 627 ( 476 ) 79 ( 52 ) 706 ( 528 ) Other 885 ( 673 ) 0 ( 0 ) 885 ( 673 ) Total 2092 ( 1438 ) 109 ( 52 ) 2201 ( 1490 ) EpiGrid 2007

10 some Geographical Information.. EpiGrid 2007

11 some temporal information. April 14/15, 1912 EpiGrid 2007

12 A modern Epidemiological study. Results of a Tuberculosis survey in Tarrant County, TX Problem: Insufficient Context GIS data with greater detail! EpiGrid 2007

13 The Epicenter After identifying the homeless shelter as the epicenter, granularity of the study change again: more contextual detail People sleeping inside homeless shelter People standing outside homeless shelter Pictures by Patrick Moonan, 2003 EpiGrid 2007

14 Different arrangements at different $ in the same shelter. Pictures by Patrick Moonan, 2003 EpiGrid 2007

15 John Snows approach in the Shelter The Map TB Prevalence From Spatial to Social Epidemiology! EpiGrid 2007

16 Towards Computational Epidemiology Its all about the CONTEXT! How can we prepare for Epidemiologic Emergencies including Epidemics, Pandemics, and Bioterrorism if there is no current morbidity or mortality data available? We may use historical or anecdotal data! Questions: Can we build a model that reproduces the historic event? How would the event manifest itself in a modern context? Demographics may have changed; Infrastructure may have changed; Medical practices may have changed; EpiGrid 2007

17 Models in Computational Epidemiology From mathematical models to simulation: - Basic SIR Model based on Differential Eqs. Dynamic System Modeling Data Storage and Analysis Simulation Data visualization. GIS/EPI Data Model Visualization Investigating disease outbreaks and risk assessment in spatially delineated environments Investigating intervention strategies to control the spread of diseases Investigating spread of disease in demographic, and geographic space! Computation Epidemiology is more than the sum of its parts: Epidemiology, Computer Science, Mathematics, Dynamic Systems, Public Health. EpiGrid 2007

18 Tools of the trade o o Mathematical Models SIR, SEIR, SIS, etc. Population Ecology Graph Theoretical Models Computational solutions: Simulations Agent based Cellular Automata Stochastic Field Simulation Social Networks Spatial-Temporal Databases GIS Visualization Web Interfaces High Performance Computing EpiGrid 2007

19 Some current issues. The following is a collection of current problems for which computational models are being developed at CERL: Contact Models to predict and quantify Pandemic Influenza Infectious Disease Outbreaks in the K-12 School System Social Network Models of Social/Intimate Relationships STD Spread Models: HPV & HIV Points of Distribution (PODs) Traffic Analysis EpiGrid 2007

20 PODs Traffic Analysis Federal and State funding has been used by counties to develop a comprehensive disaster preparedness plan. This plan identifies several sites in the county at which citizens can obtain medication or vaccination in the case of a Bio / Medical disaster. Questions: Can PODs sustain traffic Can roads sustain traffic Placement of PODs How many people can get service in how little time? PODs EpiGrid 2007

21 We need to experiment with intervention strategies When, How, Who, Where should we vaccinate? What are the predicted outcomes of specific strategies? How should mass-intervention be organized? Waiting on line to get smallpox vaccine during New York City smallpox epidemic ( 1947 ) EpiGrid 2007

22 The Global Outbreak Model Population Disease Parameters Vaccination Demographics Interaction factors Distances Data Sets Visualization EpiGrid 2007

23 Disease Parameters Latent period Infectious period Incubation period Infectivity Index case Multiple index cases Location of index case ( influenza ) Illustrates time-line for infection EpiGrid 2007

24 Model Parameters o o o o o o o o o Population per cell Demographics i.e. Age Distribution Ethnic Distribution Gender Distribution etc. Geography/Hotspots ( Rate(s Contact avg. when symptomatic Vaccinated Population Vaccine Efficacy Natural Immunity Immune Deficient Population Public Health Events Population distribution over the North Denton region. Total Population of 110,000 distributed over a grid size of 50 * 100. EpiGrid 2007

25 The Complexity of Contacts Contact is any interaction that facilitates successful disease transmission. Contact includes Exposure Duration of exposure Infectivity/ Virulence of the ( infection ) virus Immunity Infectious Age of individual Infection transmits? Function of the infectivity parameter Common meeting area other demographic characteristics Epidemics are driven by Contacts and Exposures! EpiGrid 2007

26 Social Networks determine contacts o Clusters indicate strongly connected subgroups. o Measures of Affinity o Who is likely to contact whom? EpiGrid 2007

27 Population Distributions for Different Age Groups 0-9 years years years 60+ years EpiGrid 2007

28 Global Stochastic Field Model demographic layers Age group 60+ Probability of interactions among various age groups Age group Age group Individual Probable area of interaction Age group 0-9 Probability of interactions based on distances EpiGrid 2007

29 Visualizing Spatial Spread of Influenza simulated over Northern Denton County Local Interaction EpiGrid 2007

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77 A composite SIR Model 100 small regional outbreaks EpiGrid 2007

78 Observed Epidemic Σ Regional outbreaks We can extend the model from geographic regions to demographic sub-populations EpiGrid 2007

79 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 subregion with a larger proportion of a different demographic Composition model reflects the spread of the infection in each subregion. Cell interaction is controlled by age proportions and population densities. Observed Cumulative Epidemic caused by Temporally and Spatially Distributed Local Outbreaks EpiGrid 2007

80 Mathematical Modeling of Epidemics Susceptibles Infectives Removals (SIR) model ( SEIR ) Susceptibles Exposed Infectives Removals ( SIS ) Susceptibles Infectives Susceptibles Susceptible S Infectives I Removed R ds = β SI dt di =+ β SI γi dt dr =+γi dt Let β be the transmission rate based on contact rate and infectivity Let γ be the rate of infectives becoming non-infectious EpiGrid 2007

81 Susceptibles Infectives Removals (SIR) model The naïve SIR assumes: ohomogeneous mixing of people oevery individual makes same contacts ono demographics considered ogeographical distances not considered Things can get unwieldy when adding demographics!! EpiGrid 2007

82 Composition Model -Experiment o o o The population distribution over the region is non-uniform. Contacts made between cells depends on the population of the cell. Assumption : Regions with high population make more contacts than regions with low population. Simulation parameters: Disease Simulated : Influenza like disease Incubation period : 3 days Infectious period: 3 days Recovery period: 5 days Infectivity : Contact rate/person : 11 EpiGrid 2007

83 Composition Model -Experiment Population distribution over the north Denton region. Total Population of distributed over a grid size of 50 * 100. Infected Population distribution over the north Denton region. Total Population infected at the end of simulation: EpiGrid 2007

84 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. othe results show lower level of prevalence of disease in that region compared to other regions. Region Immunized EpiGrid 2007

85 The need for Computational Horse Power Large geographic region Many cells/objects Many cells/objects Complex interactions Many cells/objects Simulation of Multiple Populations Complex interaction and Multiple Populations Large Computational Complexity We need multiple computers to execute small pieces of the simulation simultaneously. EpiGrid 2007

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87 The Future: Clusters and the GRID o Faster hardware and new high-bandwidth networks demand that we explore new cluster architectures. o o Grand Challenge problems will continue to drive the development of computing infrastructure. Distributed HPC will become ( SciDAC common place. (DOE o Larger, more complex cluster environments make it imperative to invest in new efficient and scalable tools. o o Management Tools designed for single hosts or small clusters are likely NOT to scale. New types of Middleware is needed to decouple the underlying distributed infrastructure from the applications. EpiGrid 2007

88 Grid Layers virtualization Data Grid Comp. Grid Applications Bio Grid EPI Grid? i.e., Scientific Discovery through Advanced Computing ( APIs ) Application-Specific Grid Services General Grid Services Middleware Grid Engine Grid Engine Grid Engine Grid Engine Grid Engine Grid Engine Grid Access Internet / Private Networks EpiGrid 2007

89 Validate, validate, validate Great Model, Compelling Results, nice tool BUT.HOW DID YOU VALIDATE ITS CORRECTNES?? Problems: No Data on Emerging Infectious Diseases to compare against Insufficient Domain Knowledge HIPPA & Data Privacy Incomplete or Missing Data Complexity, Complexity, Complexity Much Ado About Nothing!? EpiGrid 2007

90 Domain Knowledge and Expectation Don t kid yourself we do not understand the details of how society works and how people interact!! We can only theorize how an epidemic might manifest itself and prepare for the worst-case scenario! If data of previous epidemics (of same or similar disease) is available, expectations can be based on observation. HOWEVER, circumstances have most likely changed. Idea: Develop computational tools that allow experts to express their expectations. Validate against DOMAIN EXPERTISE even if it is just a theory or hunch! EpiGrid 2007

91 Conclusion There are many different methodologies to chose from: Mathematical Models, Agent Based Models, CAs, GSFS, etc. Chose the most appropriate modeling/simulation technique based on the domain characteristics Spatially Delineated, Regional, When developing a computational tool, keep in mind whose work is going to be facilitated!! Visualize & Parameterize & Animate Facilitate WHAT-IF-ANALYSES and support quantification of policy and/or strategic decision making. Validate against domain expertise if no reliable data source is available! EpiGrid 2007

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