NJ DHSS CES SEER G P Patil January

NJ DHSS CES SEER G. P. Patil January 17, 2003 1

This report is very disappointing. What kind of software you using? 2

Stone-Age Space-Age Syndrome Stone-age data Space-age data Stone-age analysis Space-age analysis 3

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PROJECT GEOINFORMATIC SURVEILLANCE DECISION SUPPORT SYSTEM Geographic and Network Surveillance for Arbitrary Shaped Hotspots -Next Generation of Geographic Hotspot Detection, Prioritization, and Early Warning System. G. P. Patil, W. L. Myers, C. Taillie, and D. Wardrop Pennsylvania State University, University Park Email: gpp@stat. psu. edu Webpage: http: //www. stat. psu. edu/~gpp 6

Geographical Surveillance n n n n Discrete response Hotspot detection and upper level set scan Hotspot delineation and hot-spot rating Multiple hotspot detection and delineation Hotspot prioritization and poset ranking Space-time detection and early warning Continuous response User friendly software and downloadable website 7

Areas of Application n n Biodiversity, species-rich, and species-poor areas Water resources at watershed scales Power lines and their effects Networks of water distribution systems, subway systems, and road transport systems Urban and regional planning Disease epidemiology Medical imaging Reconnaissance Astronomy Archaeology 8

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Spatial Scan Statistic Limitations and Needs n • Circles capture only compactly shaped clusters Want to identify clusters of arbitrary shape Circles handle only synoptic (tessellated ) data Want to handle data on a network Circles provide point estimate of hotspot Want to assess estimation uncertainty (hotspot confidence set) 10

Hotspot Detection Innovation Upper Level Set Scan Statistic Attractive Features n n n n n Identifies arbitrarily shaped clusters Data-adaptive zonation of candidate hotspots Applicable to data on a network Provides both a point estimate as well as a confidence set for the hotspot Uses hotspot-membership rating to map hotspot boundary uncertainty Computationally efficient Applicable to both discrete and continuous syndromic responses Identifies arbitrarily shaped clusters in the spatial-temporal domain Provides a typology of space-time hotspots with discriminatory surveillance potential 11

Prioritization Innovation Partial Order Set Ranking We also present a prioritization innovation. It lies in the ability for prioritization and ranking of hotspots based on multiple indicator and stakeholder criteria without having to integrate indicators into an index, using Hasse diagrams and partial order sets. This leads us to early warning systems, and also to the selection of investigational areas. 12

Syndromic Surveillance n Symptoms of disease such as diarrhea, respiratory problems, headache, etc n Earlier reporting than diagnosed disease n Less specific, more noise 13

The Spatial Scan Statistic n Move a circular window across the map. n Use a variable circle radius, from zero up to a maximum where 50 percent of the population is included. 14

A small sample of the circles used 15

For each circle: – Obtain actual and expected number of cases inside and outside the circle. – Calculate Likelihood Function Compare Circles: – Pick circle with maximum likelihood. This is the most likely cluster, i. e. , the cluster least likely to have occurred by chance Inference: – Generate random replicas of the data set under the nullhypothesis of no clusters (Monte Carlo sampling) – Compare most likely clusters in real and random data sets 16 (Likelihood ratio test)

Zonal p-Values -- 1 • Zonal p-values depend upon: § zonal intensity § zonal sample size • Example: § 1 case among 5 persons is not significant § 1 thousand cases among 5 thousand persons might well significant 17

Zonal p-Values -- 2 Zonal Intensity Contour of constant p-values (p =. 05) Zone 1 Zone 2 Zone 3 Sample Size • Zone 1 is not significant (high intensity but small sample size) • Zone 2 is significant even though its intensity is smaller than that of Zone 1 • Zone 3 is not significant due to low intensity 18

Spatial Scan Statistic: Properties – – – Adjusts for inhomogeneous population density. Simultaneously tests for clusters of any size and any location, by using circular windows with continuously variable radius. Accounts for multiple testing. Possibility to include confounding variables, such as age, sex or socio-economic variables. Aggregated or non-aggregated data (states, counties, census tracts, block groups, households, individuals). 19

Detecting Emerging Clusters n n n Instead of a circular window in two dimensions, we use a cylindrical window in three dimensions. The base of the cylinder represents space, while the height represents time. The cylinder is flexible in its circular base and starting date, but we only consider those cylinders that reach all the way to the end of the study period. Hence, we are only considering ‘alive’ clusters. 20

West Nile Virus Surveillance in New York City 2000 Data: Simulation/Testing of Prospective Surveillance System n 2001 Data: Real Time Implementation of Daily Prospective Surveillance n 21

2000 Data - Dead birds reported by the public - Simulation of a daily prospective surveillance system - Start date: June 1, 2000. 22

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Major epicenter on Staten Island Dead bird surveillance system: June 14 n Positive bird report: July 16 (coll. July 5) n Positive mosquito trap: July 24 (coll. July 7) n Human case report: July 28 (onset July 20) n 25

Hospital Emergency Admissions in New York City n n Hospital emergency admissions data from a majority of New York City hospitals. At midnight, hospitals report last 24 hour of data to New York City Department of Health A spatial scan statistic analysis is performed every morning If an alarm, a local investigation is conducted 26

Bird richness in the hexagons. 27

Statewide echelon map based on EMAP hexagons. The 4 -digit number in each hexagon is the EPA-EMAP identifier, while the number below is species richness. 28

Aggregation-Resolution Issues 29

Aggregation-Resolution Issues 30

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Hotspots within Hotspots Ridge and Valley hotspots within hotspots. 32

Candidate Zones for Hotspots n n Goal: Identify geographic zone(s) in which a response is significantly elevated relative to the rest of a region A list of candidate zones Z is specified a priori – This list becomes part of the parameter space and the zone must be estimated from within this list – Each candidate zone should generally be spatially connected, e. g. , a union of contiguous spatial units or cells – Longer lists of candidate zones are usually preferable – Expanding circles or ellipses about specified centers are a common method of generating the list 33

Poor Hotspot Delineation by Circular Zones Hotspot Circular zone approximations Circular zones may represent single hotspot as multiple hotspots 34

Circular spatial scan statistic zonation • Cholera outbreak along a river flood plain • • • Small circles miss much of the outbreak • • • Large circles include many unwanted cells 35

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Urban Heat Islands n n Use of aircraft and spacecraft remote sensing data on a local scale to help quantify and map urban sprawl, land use change, urban heat island, air quality, and their impact on human health (e. g. pediatric asthma) 37

Baltimore Asthma Project Interdisciplinary Analysis of Childhood Asthma in Baltimore, MD The impact of asthma is escalating within the U. S. and children are particularly impacted with hospitalization increasing 74% since 1979. This study is investigating climate and environmental links to asthma in Baltimore, Maryland, a city in the top quintile for children’s asthma in the U. S. Inner Harbor Aerosol Size 1. Collect and integrate in-situ measurements, remotely sensed measurements and clinical records that have possible relationships to the occurrence of asthma in the Baltimore, Maryland region. 2. Identify key trigger variables from the data to predict asthma occurrence on a spatial and temporal basis. 3. Organize a multidisciplinary team to assist in model design, analysis and interpretation of model results. 4. Develop tools for integrating, accessing and manipulating relevant health and remote sensing data and make these tools available to the scientific and health communities. Time Partners: • • • Baltimore City Health Department Baltimore City School System Baltimore City Planning Council, Mayor’s Office State of Maryland Department of the Environment State of Maryland Department of Health and Human Services University of Maryland Asthma Assessment from GIS techniques 38

Crime Rate Hotspots 39

Time Cylindrical space-time scan statistic zonation Space Outbreak expanding in time • Small cylinders miss much of the outbreak • Large cylinders include many unwanted cells 40

Time Some Space-Time Hotspots and Their Cylindrical Approximations Hotspot Cylindrical approximation sees single hotspot as multiple hotspots Space 41

ULS Candidate Zones n n Question: Are there data-driven (rather than a priori) ways of selecting the list of candidate zones? Motivation for the question: A human being can look at a map and quickly determine a reasonable set of candidate zones and eliminate many other zones as obviously uninteresting. Can the computer do the same thing? A data-driven proposal: Candidate zones are the connected components of the upper level sets of the response surface. The candidate zones have a tree structure (echelon tree is a subtree), which may assist in automated detection of multiple, but geographically separate, elevated zones. Null distribution: If the list is data-driven (i. e. , random), its variability must be accounted for in the null distribution. A new list must be developed for each simulated data set. 42

ULS Scan Statistic n Data-adaptive approach to reduced parameter space 0 n Zones in 0 are connected components of upper level sets of the empirical intensity function Ga = Ya / Aa n n Upper level set (ULS) at level g consists of all cells a where Ga g Upper level sets may be disconnected. Connected components are the candidate zones in 0 n These connected components form a rooted tree under set inclusion. – Root node = entire region R – Leaf nodes = local maxima of empirical intensity surface – Junction nodes occur when connectivity of ULS changes with falling intensity level 43

Upper Level Set (ULS) of Intensity Surface Hotspot zones at level g (Connected Components of upper level set) 44

Changing Connectivity of ULS as Level Drops g 45

ULS Connectivity Tree Schematic intensity “surface” A B C N. B. Intensity surface is cellular (piece-wise constant), with only finitely many levels A, B, C are junction nodes where multiple zones coalesce into a single zone 46

ULS Connectivity Tree -1 n Ingredients: – Tessellation of a geographic region: j c a b e k h f d i g a, b, c, … are cell labels – Intensity value G on each cell. Determines a cellular (piecewise constant) surface with G as elevation. n n n Imagine surface initially inundated with water Water evaporates gradually exposing the surface which appears as islands in the sea How does connectivity (number of connected components) of the exposed surface change with falling water level? 47

ULS Connectivity Tree -2 n n n Think of the tessellated surface as a landform Initially the entire surface is under water As the water level recedes, more and more of the landform is exposed At each water level, cells are colored as follows: – Green for previously exposed cells (green = vegetated) – Yellow for newly exposed cells (yellow = sandy beach) – Blue for unexposed cells (blue = under water) For each newly exposed cell, one of three things happens: – New island emerges. Cell is a local maximum. Morse index=2. Connectivity increases. – Existing island increases in size. Cell is not a critical point. Connectivity unchanged. – Two (or more) islands are joined. 48 Cell is a saddle point Morse index=1. Connectivity decreases.

ULS Connectivity Tree -3 Newly exposed island j a b e c k ULS Tree h f d g a b, c i Island grows j c k a b e h f d i 49

ULS Connectivity Tree -4 ULS Tree Second island appears j a b e c k h f d g i c k a b e h f d i d New leaf node (local maximum) Both islands grow j a b, c g a b, c e d f, g 50

ULS Connectivity Tree -5 a Islands join – saddle point j a b e c k h f d c k a b e b, c g d f, g e i h Junction node a Exposed land grows j ULS Tree h f d i g b, c e d f, g h i, j, k Root node 51

Comparison of Tree-Structured and Circle-Based SATScan n Agreement/Disagreement regarding hotspot locus n Comparative plausibility and accuracy of hotspot delineation n Execution time and computer efficiency 52

Confidence Region on ULS Tree A hotspot confidence set with two connected components is shown on the ULS tree. The connected components correspond to different hotspot loci while the nodes within a connected component correspond to different delineations of that hotspot – all at the 53 appropriate confidence level.

Estimation Uncertainty in Hotspot Delineation MLE Outer envelope Inner envelope 54

Hotspot Delineation and Hotspot Rating n n n Determine a confidence set for the hotspot Each member of the confidence set is a zone which is a statistically plausible delineation of the hotspot at specified confidence Confidence set lets us rate individual cells a for hotspot membership Rating for cell a is percentage of zones in confidence set that contain a. (More generally, use weighted proportion. ) Map of cell ratings: – Inner envelope = cells with 100% rating 55 – Outer envelope = cells with positive rating

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Ranking Possible Disease Clusters in the State of New York Data Matrix 57

Regions of comparability and incomparability for the inherent importance ordering of hotspots. Hotspots form a scatterplot in indicator space and each hotspot partitions indicator space into four quadrants 58

Hotspot Prioritization and Poset Ranking n n Multiple hotspots with intensities significantly elevated relative to the rest of the region Ranking based on likelihood values, and additional attributes: raw intensity values, socio -economic and demographic factors, feasibility scores, excess cases, seasonal residence, atypical demographics, etc. Multiple attributes, multiple indicators Ranking without having to integrate the multiple indicators into a composite index 59

Multiple Criteria Analysis, Multiple Indicators and Choices, Health Statistics, Disease Etiology, Health Policy, Resource Allocation n First stage screening – Significant clusters by Sa. TScan and/or upper level sets n Second stage screening – Multicriteria noteworthy clusters by partially ordered sets and Hass diagrams n Final stage screening – Follow up clusters for etiology, intervention based on multiple criteria using Hass diagrams 60

HUMAN ENVIRONMENT INTERFACE LAND, AIR, WATER INDICATORS for land - % of undomesticated land, i. e. , total land area-domesticated (permanent crops and pastures, built up areas, roads, etc. ) for air - % of renewable energy resources, i. e. , hydro, solar, wind, geothermal for water - % of population with access to safe drinking water RANK COUNTRY 1 2 3 5 13 22 39 45 47 51 52 59 61 64 77 78 81 Sweden Finland Norway Iceland Austria Switzerland Spain France Germany Portugal Italy Greece Belgium Netherlands Denmark United Kingdom Ireland LAND AIR WATER 69. 01 76. 46 27. 38 1. 79 40. 57 30. 17 32. 63 28. 34 32. 56 34. 62 23. 35 21. 59 21. 84 19. 43 9. 83 12. 64 9. 25 35. 24 19. 05 63. 98 80. 25 29. 85 28. 10 7. 74 6. 50 2. 10 14. 29 6. 89 3. 20 0. 00 1. 07 5. 04 1. 13 1. 99 100 98 100 100 82 100 98 100 100 100 61

Hasse Diagram (Western Europe) 62

Ranking Partially Ordered Sets – 5 Linear extension decision tree Poset (Hasse Diagram) a c e b d c e f b b b d e f 1 e d Jump Size: b a f f 3 3 d c d e f d d e e d e f d e d f e a c c c f d a c f e f e f e 2 3 5 4 3 3 2 4 3 4 4 2 63 2

Cumulative Rank Frequency Operator – 5 An Example of the Procedure In the example from the preceding slide, there a total of 16 linear extensions, giving the following cumulative frequency table. Rank Element 1 2 3 4 5 6 a 9 14 16 16 b 7 12 15 16 16 16 c 0 4 10 16 16 16 d 0 2 6 12 16 16 e 0 0 1 4 10 16 f 0 0 6 16 64 Each entry gives the number of linear extensions in which the element (row label) receives a rank equal to or better that the column heading

Cumulative Rank Frequency Operator – 6 An Example of the Procedure 16 The curves are stacked one above the other and the result is a 65 linear ordering of the elements: a > b > c > d > e > f

Cumulative Rank Frequency Operator – 7 An example where F must be iterated F 2 F Original Poset a f b c e g h d g e b f a f e (Hasse Diagram) a b d a c g h c h 66

Cumulative Rank Frequency Operator – 8 An example where F results in ties Original Poset (Hasse Diagram) a a b c d F b, c (tied) d • Ties reflect symmetries among incomparable elements in the original Hasse diagram • Elements that are comparable in the original Hasse diagram will not 67 become tied after applying F operator

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Incorporating Judgment Poset Cumulative Rank Frequency Approach • Certain of the indicators may be deemed more important than the others • Such differential importance can be accommodated by the poset cumulative rank frequency approach • Instead of the uniform distribution on the set of linear extensions, we may use an appropriately weighted probability distribution , e. g. , 69

Network-based Surveillance Subway system surveillance n Drinking water distribution system surveillance n Stream and river system surveillance n Postal System Surveillance n Road transport surveillance n 70

• Figure 1. Upper Juniata river network with superimposed network of Pennsylvania Department of Environmental Protection (DEP) sampling stations. In Phase 1, scan statistics methods will be used to identify hotspots of biological impairment. Phase 2 incorporates potential explanatory factors and identifies residual hotspots that are subjected to detailed network 71 modeling in Phase 3.

Disease Mapping and Evaluation n n n Elevated rate cluster detection and assessment Geographic surveillance and evaluation Comparative studies involving statistical power Geometric accuracy in cluster estimation Real data projects and validations Computing and software 72

Agent Orange Dioxin Hotspots, coldspots, and midspots n Identification and prioritization n Persistent hotspot trajectories n Spatial disease monitoring, modeling, and tracking n Health effect surveillance system n 73

Agent Orange Dioxin Ecosystem impact assessment n Deforestation, defoliation, remote sensing n Hyperspectral change detection and accuracy assessment n Emergent advanced raster map analysis system n 74

Crop Biosurveillance/Biosecurity 75

Crop Biosurveillance/Biosecurity 76

Crop Biosurveillance/Biosecurity 77

Emergent Surveillance Plexus Surveillance Sensor Network Testbed Autonomous Ocean Sampling Network Types of Hotspots n n Hotspots due to multiple, localized, stationary sources Hotspots corresponding to areas of interest in a stationary mapped field Time-dependent, localized hotspots Hotspots due to moving point sources 78

Ocean SAmpling MObile Network OSAMON 79

Ocean SAmpling MObile Network OSAMON Feedback Loop n Network sensors gather preliminary data n ULS scan statistic uses available data to estimate hotspot n Network controller directs sensor vehicles to new locations n Updated data is fed into ULS scan statistic system 80

Surveillance Sensor Network Testbed High - - - • Indoor / Outdoor GPS for cooperative navigation • Cross-platform joint service experiments • Operational testing for more realistic adaptation • Aerial Mapping adds new perspective • Expand data storage and processing • Larger cross-platform teams capture realistic cooperation and dynamic adaptation 81

SAmpling MObile Networks (SAMON) Additional Application Contexts n Hotspots for radioactivity and chemical or biological agents to prevent or mitigate the effects of terrorist attacks or to detect nuclear testing n Mapping elevation, wind, bathymetry, or ocean currents to better understand protect the environment n Detecting emerging failures in a complex networked system like the electric grid, internet, cell phone systems n Mapping the gravitational field to find underground chambers or tunnels for rescue or combat missions 82

Environmental Justice n n Is environmental degradation worse in poor and minority communities? Identification of persistent poverty and its trajectories Identification of pollution proximity and vulnerability Co-location research hampered by the lack of methods and tools of investigation 83

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Space-Time Poverty Hotspot Typology n Federal Anti-Poverty Programs have had little success in eradicating pockets of persistent poverty n Can spatial-temporal patterns of poverty hotspots provide clues to the causes of poverty and lead to improved locationspecific anti-poverty policy ? 85

MI NJ TN , C A it, Oa kla nd tro De n, de m s, ph i em Ca M ng wi ng Sh ifti Gr o d te tra en nc Co t te n is rs Pe Dimensions of Tract Poverty in Four Metropolitan Areas 1970 -1990 86

Growing poverty 87

Persistent poverty 88

Shifting poverty Oakland 1970 Poverty data Oakland 1980 Poverty data Oakland 1990 Poverty data 89

Camden 1970 Poverty data Camden 1980 Poverty data Concentrated poverty Camden 1990 Poverty data 90

1990 1980 Long Duration 2000 Persistent Hotspot 1970 Space (census tract) 2000 1990 1980 One-shot Hotspot Short Duration Persistence can be assessed by the projection of the space-time hotspot onto the time axis Time (census year) Persistence is a property of space-time hotspots Time (census year) Hotspot Persistence 1970 Space (census tract) 91

2000 1990 Stationary Hotspot 1980 1970 Time (census year) Typology of Persistent Space-Time Hotspots-1 2000 1990 1980 1970 1990 Shifting Hotspot 1980 1970 Space (census tract) Time (census year) Space (census tract) 2000 Expanding Hotspot 2000 1990 1980 1970 Contracting Hotspot Space (census tract) 92

However, certain time slices of the hotspot are disconnected in space Spatially disconnected time slice 2000 1990 1980 Bifurcating Hotspot 1970 Space (census tract) Time (census year) These hotspots are connected in space-time Time (census year) Typology of Persistent Space-Time Hotspots-2 2000 Merging Hotspot 1990 1980 1970 Space (census tract) 93

Trajectory of a Persistent Space-Time Hotspot A space-time hotspot is a three-dimensional object Time slices of space-time hotspot Time (census year) Visualization can be done by displaying the sequence of time slices---called the trajectory of the hotspot 2000 1990 Merging Hotspot 1980 1970 Space (census tract) 94

Trajectory of a Merging Hotspot 1970 1980 1990 2000 95

Trajectory of a Shifting Hotspot 1970 1980 1990 2000 96

Multiscale Advanced Raster Map Analysis n Geospatial Patterns and Pattern Metrics – Landscape patterns, disease patterns, mortality patterns n Surface Topology and Spatial Structure – Hotspots, outbreaks, critical areas – Intrinsic hierarchical decomposition, study areas, reference areas – Change detection, change analysis, spatial structure of change 97

Needed n n n n Downloadable geoinformatic analysis system Identification of arbitrary shape hotspots, coldspots, midspots Multi-criteria prioritization and ranking Synoptic and network-based surveillance Multiple scales and aggregation levels Space, time, and space-time Zonal tree scan system 98

Terra Seer Long Island Study-1 August 13, 2002 GEOGRAPHIC TRACE OF CANCER n n n The Geographic Distribution of BLC Cancers in Long Island NY Geographic Trace of Cancer (the pattern of cases over time and space) may even become as important as genetic research in the search for causes Need of an Exploratory, Integrative, Multi-scalar Approach 99

Terra Seer Long Island Study-2 August 13, 2002 STRENGTHENING SATSCAN n n n The techniques we employ are not ‘better’ or ‘more accurate’ than the scan statistic Using a battery of approaches allows us to quantify different aspects of univariate and multivariate clusters, to explore different scales of clustering, and to evaluate how sensitive the results are to different definitions of clustering The methods we have used are not designed to replace the scan approach, rather they should be used with the scan statistic to develop a more comprehensive understanding of geographic variation of cancer morbidity 100

Terra Seer Long Island Study-3 August 13, 2002 HOTSPOTS, PUBLIC AND AGENCIES n n n Public: Being singled out as an elevated cluster community is such a source of dread for many people Agency: Hotspot status creates intense pressure to study individual clusters instead of broader cancer patterns Need: Hotspot rating along with hotspot detection, prioritization, and ranking interval with multicriteria 101

Terra Seer Long Island Study-4 August 13, 2002 n Issues and Approaches – Local Moran test , Boundary Detection, Boundary Overlap, Multi-Scalar, and Spatially Constrained Clustering n n Adjacency: Centroid, common border, upper level set tree neighbor Multiscalar: Multivariate response, Multivariate Binomial, Poisson, Gamma, Lognormal Scale of Study: Spatial extent, Hotspot in Hotspots Scale of Process: Resolution, Aggregation Level 102

Recommendations n n n n Research, education, and outreach initiative National Center Multi-focal multi-disciplinary thrust Concepts, methods, tools, validations Solid, sound, sophisticated and yet userfriendly Priority activities and regional projects Downloadable user-friendly software system Innovative Synergistics 103

Penn State Involvements n n n n Center for Statistical Ecology and Environmental Statistics/NSF, EPA Poverty Research Center/Ford Foundation Geo. Vista Center/NCI, NSF Geovisualization and Spatial Analysis of Cancer Data NCI Program For: Geographic-based Research in Cancer Control and Epidemiology Appalachia Cancer Network/NCI Environmental Consortium on Biosurveillance Cooperative Wetlands Center/EPA Center for Remote Sensing of Earth Resources/NASA, EPA 104

Logo for Statistics, Environment, Health, Ecology, and Society 105