in silico Public Health: The Essential Role of Highly Detailed Data Synthesis in Support of Public Health Decision-Making

Transcription

1 in silico Public Health: The Essential Role of Highly Detailed Data Synthesis in Support of Public Health Decision-Making Bryan Lewis, MPH April 19th, 2010 Blacksburg, VA 1

2 Thesis Highly-detailed appropriately structured synthetic data has an essential role to play in supporting public health decision-making 2

3 Highly Detailed Data Multiple attributes for multiple classes of objects Level of fidelity consistent across attributes Construction of data consistent with processes from real world Correlations between different layers of data are preserved Models and data become intrinsically linked Models use data to create data Data induces model s form 3

4 Public Health To promote health and quality of life by preventing and controlling disease, injury, and disability. -- CDC mission statement Requires transdisciplinary approach Involves government, non-profits, & academia Credited with extending life expectancy by 25 years in last century 4

5 Decision-making Support Planning & Response Analyzing complex courses of action and rapid responses to emerging crises Situational Awareness Enhancing value of limited data sources and facilitating testing of novel methods Training & Evaluation Improving Public Health practice through immersive training environments and novel study designs 5

6 Form Follows Function Shaker designed chairs, all similar, yet different forms follow intended function Form of models and data should follow its function Some PH questions require models to function in a way that necessitates a form with a high level of detail 6

7 Direct Approach 7

8 Epidemiological Modeling I simply wish that, in a matter which so closely concerns the well-being of mankind, no decision shall be made without all the knowledge which a little analysis and calculation can provide. Bernoulli D. Essai d une nouvelle analyse de la mortalite causee par la petite verole. Mem Math Phy Acad Roy Sci Paris Bernoulli built mathematical model to calculate the age-specific impact of smallpox (1760) Kermack and McKendrick construct compartmental model of disease to explain classic bell-shaped curve of epidemics (1927) 8

9 Detailed Approach 9

10 Detailed Synthetic Populations Individuals - Census Locations - Navteq Activities - Surveys 10

11 Detailed Disease Representations Uninfected S: none I: 0 until infected Level of Susceptibility Fully_susceptible 0 Waned_natural Waned_natural Waned_natural Waning_vaccine Waning_vaccine Waning_vaccine Full_vaccination 0.20 Partial_vaccination Latent S: none I: 0 G(9d,2d) Uninfected_treated S: none I: 0 U(1,1) Uninfected with a different level of susceptibility Coryza S: coryza I: 0.25 G(9d,2d) See table See table See table See table See table Typical_early S: cough I: 1 U(1d,5d) Mild_early S: cough I: 1 U(1d,5d) Typical_middle S: cough I: 1 G(14d,3d) Typical_treated S: minimal I: 0.5 G(5d,2d) Mild_middle S: cough I: 1 G(14d,2d) Mild_treated S: minimal I: 0.5 G(5d,2d) Typical_late S: cough I: 0.8 G(12d,3d) Mild_late S: cough I: 0.8 G(4d,1d) boosted_by_vax S: none I: 0 U(1,1) Natural_immunity Susc: 0.0 S: 0 I: 0 G(8y,180d) Disease Outcomes (depends on susceptibility) Name Typical Fully_susceptible 0.73 Waned_natural Waning_vaccine Waning_vaccine Waning_vaccine Full_vaccination 0.5 Mild Asymp Waned_natural Waned_natural state name S: symptom level I: infectivity level duration in state Untreated Antibiotics Vaccinated Legend Duration functions G=Gaussian(mean,sd) U=Uniform(min,max) Susceptible to infection Treated with Antibiotics Asymptomatic_early S: minimal I: 0.2 U(1,5) Asymptomatic_ treated S: none I: 0.05 G(5d,2d) Asymptomatic_late S: minimal I: 0.2 U(1,13) waned_natural_immunity Susc: 0.85 G(8y,180d) Partial_vaccination

12 Validity The validity (or significance) of a model should be judged by its suitability for a particular purpose. A model is sound and defendable if it accomplishes what is expected of it. This means that validity, as an abstract concept divorced from purpose, has no useful meaning. What may be an excellent model for one purpose may be misleading and therefore worse than useless for another purpose JW Forrester. Chapter 13. Judging Model Validity. Industrial dynamics (1961) Judge validity based on utility of model for a particular purpose Highly detailed data supports models that can maintain utility across many purposes Multiple layers of data and the correlations between them support this flexibility of use 12

13 Building Credibility Establishment of behavior similar to trusted source Demonstrate utility and benefits of new methods Ensure new methods can be accessed and used New methods don t always have gold standard equivalents, piece-meal approach can be used 13

14 Real World Consistency Real World "#$%&' World "#$%&'(" "# $# %# /%-01(&%" " 5-,67013&" ($$# "$$# )*+$&,"-."/%-01(&%" school work college shop other home &# $$# '# days VBI Modeling Result from PNAS study Glezen WP, Couch RB. Interpandemic influenza in the Houston area, N Engl J Med 1978;298:587. Halloran ME, Ferguson NM, Eubank S, Longini IM Jr, Cummings DA, Lewis B, Xu S, Fraser C, Vullikanti A, Germann TC, Wagener D, Beckman R, Kadau K, Barrett C, Macken CA, Burke DS, Cooley P. Modeling targeted layered containment of an influenza pandemic in the United States. Proc Natl Acad Sci U S A Mar 25;105(12):

15 Chapter I Flexible support for rapid decision-making Manuscript: Integrating Highly Detailed Agent-Based Models in Real-time Public Health Decision Making Anticipated submission: late-june 15

16 Planning and Response Questions: Emerging diseases Applying new technologies Finding Solutions: Comparison of different courses of action Optimizing cost benefit of different policies N a t i o n a l S t r a t e g y f o r p a n d e m i c i n f l u e n z a h o m e l a n d s e c u r i t y c o u n c i l n o v e m b e r Estimating the effects of complex combinations of interventions 16

17 Role for Detailed Data Implementation Easily modified or augmented to match specifics of scenario of interest Analysis Familiar and well-developed techniques are applicable to simulated data Communication Similarity with real world frames results in a familiar context for decision makers 17

18 Planning for Pandemic Flu Halloran ME, Ferguson NM, Eubank S, Longini IM Jr, Cummings DA, Lewis B, Xu S, Fraser C, Vullikanti A, Germann TC, Wagener D, Beckman R, Kadau K, Barrett C, Macken CA, Burke DS, Cooley P. Modeling targeted layered containment of an influenza pandemic in the United States. Proc Natl Acad Sci U S A Mar 25;105(12):

19 Planning for Pandemic Flu TLC and prophylaxis have potential to mitigate epidemic Effects could not be well estimated without the social networks Halloran ME, Ferguson NM, Eubank S, Longini IM Jr, Cummings DA, Lewis B, Xu S, Fraser C, Vullikanti A, Germann TC, Wagener D, Beckman R, Kadau K, Barrett C, Macken CA, Burke DS, Cooley P. Modeling targeted layered containment of an influenza pandemic in the United States. Proc Natl Acad Sci U S A Mar 25;105(12):

20 Antiviral Medkits 20

21 Antiviral Medkits No private stockpile 5% at random 15% at random 25% at random 21

22 H1N1 Emergence Web-based interface for modeling support developed Disease details were unknown Quick studies conducted to explore the implications of the most recent parameter estimates 22

23 H1N1 Emergence Government analysts explored courses of action Availability of this tool allowed it and only one other model to be used in the 24 hour decision cycle Daily Infections Day sdg=50d100,sds=50d100_2+1 sdg=50d125,sds=50d125_2+1 sdg=50d150,sds=50d150_2+1 sdg=50d175,sds=50d175_2+1 sdg=50d200,sds=50d200_2+1 sdg=none,sds=none 23

24 H1N1 3rd wave Vaccine production slightly behind schedule H1N1 peaked before sufficient vaccine was in arms Vaccine demand reduced Concern over 3rd wave of H1N1 Number of Infections "#$%&'()*+,) *-&.(/*&%"0,()*'1*232*)&'&* "00(5* 232* #&%%(5* May 29 Jun 12 Jun 26 Jul 10 Jul 24 Aug 07 Aug 21 Sep 04 Sep 18 Oct 02 Oct 16 Oct 30 Nov 13 Nov 27 Dec 11 Dec 25 Jan 08 Jan 22 Feb 05 Feb 19 Mar 05 Mar 19 Apr 02 Apr 16 Apr 30 May 14 May 28 24

25 H1N1 3rd wave 25

26 Decision Making Support Federal plans for pandemic influenza Estimated impact of TLC non-pharmaceutical interventions Estimated impact of private stockpiling of antivirals Emerging disease response Modeling support in 24 hour decision cycle during the emergence of pandemic H1N1 influenza Estimation of conditions needed for 3rd wave of H1N1 26

27 Chapter II Enabling research through adaptable data structures Manuscript: in silico Surveillance: Using Agent-based Models to Evaluate Outbreak Detection Algorithms American Journal of Epidemiology 27

28 Situational Awareness The perception of elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future Endsley, M. R. Toward a theory of situation awareness in dynamic systems Human Factors 37(1) Gathering data from environment and projecting short term implications Main focus of epidemiology but not of epidemiological modeling Problems: Outbreak detection Estimation of population and disease characteristics 28

29 Role for Detailed Data Representation of realistic environments Aid in interpretation of newly gathered information Facilitate fusion of disparate data sources gathered from the environment Model data gathering from environment Provide framework to evaluate these processes Creation of data through a means that can capture causation rather than rely on correlation 29

30 Outbreak Detection Early outbreak detection leads to more effective responses Time from data collection to analysis is very important 30

31 Outbreak Detection Challenges Detection of outbreaks of serious disease that initially present as an Influenza-like Illness (ILI) Proportion of Population with Influenza-like Illness Boston Many diseases present with non-descript flu-like symptoms Challenge to separate ILI of interest from background noise Surveillance systems for ILI are clinic based Justin Pendarvisa, Erin L. Murrayb, Marc Paladinib, Julia Gunna, Donald R. Olson. Age Specific Correlations between Influenza Laboratory Data and Influenza-like Syndrome Definitions in Boston and New York City. Presentation, 2008 Syndromic Surveillance Conference, Indianapolis, IN. 31

32 Study Design Represent ILI surveillance Based on Harvard Pilgrim Health Insurance plan in Boston Simulate background ILI disease Include seasonality, delays in seeking healthcare Insert outbreak of interest into background ILI disease data stream Assess outbreak detection performance under different conditions 32

33 Real ILI vs. in silico ILI Clinic visits and Recurrence Interval Real HPHC data Days Visits to the Clinic Recurrence Interval Clinic visits and Recurrence Interval Run 1 Days Visits to the Clinic Recurrence Interval 33

34 Variability between Instances 34

35 Variability within Population Infections All Clinic visits All Number Infected Number Visited Clinic Days Days Captures the structure of the HMO and Population 35

36 Decision Making Support Framework for evaluating surveillance data Hone the utility of outbreak detection algorithms Explore cost-benefit of surveillance system modifications Establish guidelines for when and how to act on data gathered from environment 36

37 Chapter III Supporting novel training and evaluation methods Manuscript: Enabling Scientific Assessment of Public Health Decision Making with Agent-based Models American Journal of Public Health 37

38 Training and Evaluation Excellence is an art won by training and habituation. Aristotle Needed tools: Environments to facilitate training of higher-level analysis and practice Framework for studying the translation of data into practice Increase the impact of instruction of students 38

39 Role for Detailed Data Support software development of novel tools Detailed data can provide better test cases for software developers with non-obvious conditions Create data structures to support training and evaluation Data can be configured to enable human performance evaluations Large numbers of similarly structured yet unique instances of detailed data can be generated 39

40 Virtual Training Commonly used for dangerous and expensive tasks: flight, medical diagnosis, warfare, etc. 40

41 in silico PH Environment Detailed visualization of simulated PH info Dynamic analytic tools to interact with data Support for policy option selection 41

42 Experiment Setting Endemic pertussis in the entire state of Utah Contact tracing for each infected individual Every disease state and health care interaction 20 individual schools seeded with an outbreak 3 policies enacted at 2 different points following the outbreak (9 different paths) Policy Total of 180 individual runs Outbreak seeded Policy choice 1 choice 2 42

43 Simulated Data Summary 43

44 Decision Making Support Enabled creation of platform capable of studying multiple aspects of public health decision making Created data for situation awareness study based on pertussis Created data for course of action studies based on influenza Study participants confirm utility of the environment and data for training purposes 44

45 Summary Planning & Response Quick responses to complex questions, integration into 24-hour decision cycle, support for incorporating rapidly updated data Situational Awareness Realistic spatial and temporal spread of disease, frameworks for surveillance improvement studies Training & Evaluation Immersive training environments, support of study of complex behaviors, intensify instruction 45