2. 2.1.1 Geospatial Analysis for Disease Surveillance—1 Case Event Point Data & Areal Unit Count Data Geospatial surveillance Cluster detection and evaluation Spatial scan statistics Choice of zonal parameter space Candidate zones as circular windows of expanding size Elliptical windows: long island breast cancer study Hyperclusters, echelon trees, upper surface sets defined by thresholds-based nodes

3. 2.1.2 Spatio-temporal surveillance Cylinders-based spatio-temporal scan statistics Three-dimensional echelons and echelon trees Candidate zones as upper surface sets defined by thresholds-based nodes Temporal persistence and patterns Cluster alarms, suspect clusters, and their evaluation Geospatial Analysis for Disease Surveillance—2 Case Event Point Data & Areal Unit Count Data

4. 2.1.3 Multiple cancer mortality statistics and maps Multiple disease incidence statistics and maps Across United States over years Across individual states over years Pooling over types of cancer/disease Pooling over types of people Change detection and change analysis In space, in time, in space-time Structure and behavior of chance Persistence and patterns of elevated areas Geospatial and Spatiotemporal Patterns of Change

5. 2.1.4 To evaluate reported spatial or spatiotemporal disease clusters To see if they are statistically significant To test whether a disease is randomly distributed To perform geographical surveillance of disease To detect areas of significantly high or low rates Spatial and Spatiotemporal Scan Statistics—1 SaTScan

6. 2.1.5 Poisson model, where the number of events in an area is Poisson distributed under the null hypothesis Bernoulli model, with 0/1 event data such as cases and controls The program adjusts for the underlying inhomogeneity of a background population With the Poisson model, the program can also adjust for any number of categorical variates provided by the user Spatial and Spatiotemporal Scan Statistics—2 SaTScan

7. 2.1.6 SaTSCAN – 1 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

8. 2.1.7 SaTSCAN – 2 Example: Infected individuals in a tessellated region G with cells (spatial units) A  (A) = # individuals in cell A (known) x(A) = # infected individuals in cell A (data) Individual infection results from independent Bernoulli trials Full model: –Bernoulli parameter is p inside zone Z and q  p outside Z –p, q, Z have unknown parameter values and must be estimated Null model: Bernoulli parameter is constant (but unknown) throughout the region G

9. 2.1.8 SaTSCAN – 3 Estimation: Maximum likelihood Likelihood = L( Z, p, q ) –For fixed Z maximize (analytically) with respect to p and q giving a partial likelihood L(Z) –Maximize L(Z) by explicit search through the list of candidate zones giving the likelihood estimate of Z

10. 2.1.9 SaTSCAN – 4 Hypothesis Testing: Likelihood ratio statistic –Non-standard situation. Traditional ML theory does not apply –Need to determine the null distribution by Monte Carlo simulation of replicate data sets under the null model. For each data set Z must be estimated and the value of the likelihood ratio test statistic computed

11. 2.1.10 SaTSCAN – 5 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.

12. 2.1.11 Scan Statistic Zonation Need for Adaptability Cholera outbreak along a river flood-plain Small circles miss much of the outbreak Large circles include many unwanted cells

13. 2.1.12 Space-Time Scan Statistic Zonation Need for Adaptability Outbreak expanding in time Small cylinders miss much of the outbreak Large cylinders include many unwanted cells Space Time

14. 2.1.13 Obvious solution is to consider all connected unions of cells Usually too many such unions even to enumerate them all, let alone calculate a p-value for each Instead, one needs a reasonably small list of candidate zones whose sizes and shapes are not fixed a prioi (by circles, ellipses, cylinders, etc.) Zone sizes and shapes should adapt locally to the intensity surface of the disease Upper level set approach produces such a list of candidate zones that can then be compared according to p-values Candidate Zones

15. 2.1.14 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

16. 2.1.15 Zonal p-Values -- 2 Sample Size Zonal Intensity Contour of constant p-values (p =.05) Zone 1 Zone 2 Zone 3 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

17. 2.1.16 Dead Bird Clusters as an Early Warning System for West Nile Virus Activity Mostashari, Kulldorff, Hartman, Miller, Kulasekara, 2003. Emerging Infectious Diseases 9, 641-646. Prospective geographic cluster analysis of dead bird reports may provide early warning of increasing viral activity in birds and mosquitoes, allowing jurisdictions to triage limited mosquito-collection and laboratory resources and more effectively prevent human disease caused by the virus… We adapted the spatial scan statistic for prospective detection of infectious disease outbreaks in New York City… This adaptation of the spatial scan statistic could also be useful in other infectious disease surveillance systems, including those for bioterrorism. Prospective geographic cluster analysis of dead bird reports may provide early warning of increasing viral activity in birds and mosquitoes, allowing jurisdictions to triage limited mosquito-collection and laboratory resources and more effectively prevent human disease caused by the virus… We adapted the spatial scan statistic for prospective detection of infectious disease outbreaks in New York City… This adaptation of the spatial scan statistic could also be useful in other infectious disease surveillance systems, including those for bioterrorism.

18. 2.1.17 Geographic Prediction of Human Onset of West Nile Virus Using Dead Crow Clusters: A Quantitative Assessment of Year 2002 Data in New York State Johnson, Eidson, Schmit, Ellis, Kulldorff (2005) The risk of becoming a West Nile Virus (WNV) case in New York State, excluding New York City, was evaluated for individuals whose town of residence was proximal to spatial clusters of dead American crows (Corvus brachyrhynchos). Weekly clusters were delineated for June-October 2002 by the binomial spatial scan statistic and by kernel density smoothing. Relative risk of a human case was estimated for different spatial-temporal exposure definitions after adjusting for population density and age distribution using Poisson regression, adjusting for week and geographic region, and Cox proportional hazards modeling, where the week of a human case was treated as the failure time and baseline hazard was stratified by region.

19. 2.1.18 The risk of becoming a WNV case was positively associated with living in towns that are proximal to dead crow clusters. The highest risk was consistently for towns associated with a cluster in the current or prior 1-2 weeks Weaker, but positive, associations were found for towns associated with a cluster in just the 1-2 prior weeks, indicating an ability to predict onset in a timely fashion. These results are relevant to spatial-temporal surveillance for zoonotic diseases in general, including those of bioterrorism concern.

20. 2.1.19 Geospatial Patterns and Pattern Metrics Geospatial Patterns and Pattern Metrics –Landscape patterns, disease patterns, mortality patterns Surface Topology and Spatial Structure Surface Topology and Spatial Structure –Hotspots, outbreaks, critical areas –Intrinsic hierarchical decomposition, study areas, reference areas –Change detection, change analysis, spatial structure of change Multiscale Advanced Raster Map Analysis

21. 2.1.20 Spatially distributed response variables Hotspot analysis Prioritization Decision support systems Geoinformatic spatio-temporal data from a variety of data products and data sources with agencies, academia, and industry Masks, filters Indicators, weights Masks, filters Geoinformatic Surveillance System

22. 2.1.21 National Applications Biosurveillance Carbon Management Coastal Management Community Infrastructure Crop Surveillance Disaster Management Disease Surveillance Ecosystem Health Environmental Justice Environmental Management Environmental Policy Homeland Security Invasive Species Poverty Policy Public Health Public Health and Environment Robotic Networks Sensor Networks Social Networks Syndromic Surveillance Tsunami Inundation Urban Crime Water Management

23. 2.1.22 Examples of Hotspot Analysis Spatial Disease surveillance Biodiversity: species-rich and species-poor areas Geographical poverty analysis Network Water resource impairment at watershed scales Water distribution systems, subway systems, and road transport systems Social Networks/Terror Networks

24. 2.1.23 Issues in Hotspot Analysis Estimation: Identification of areas having unusually high (or low) response Testing: Can the elevated response be attributed to chance variation (false positive) or is it statistically significant? Explanation: Assess explanatory factors that may account for the elevated response

25. 2.1.24 The Spatial Scan Statistic Move a circular window across the map. Move a circular window across the map. Use a variable circle radius, from zero up Use a variable circle radius, from zero up to a maximum where 50 percent of the population is included.

26. 2.1.25 A small sample of the circles used

27. 2.1.26 Detecting Emerging Clusters Instead of a circular window in two dimensions, we use a cylindrical window in three dimensions. 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 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. 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.

28. 2.1.27 Hospital Emergency Admissions in New York City Hospital emergency admissions data from a majority of New York City hospitals. Hospital emergency admissions data from a majority of New York City hospitals. At midnight, hospitals report last 24 hour of 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 A spatial scan statistic analysis is performed every morning If an alarm, a local investigation is conducted If an alarm, a local investigation is conducted

29. 2.1.28 Syndromic Surveillance Symptoms of disease such as diarrhea, respiratory problems, headache, etc Symptoms of disease such as diarrhea, respiratory problems, headache, etc Earlier reporting than diagnosed disease Earlier reporting than diagnosed disease Less specific, more noise Less specific, more noise New York City New York City Pennsylvania Pennsylvania

30. 2.1.29 (left) The overall procedure, leading from admissions records to the crisis index for a hospital. The hotspot detection algorithm is then applied to the crisis index values defined over the hospital network. (right) The -machine procedure for converting an event stream into a parse tree and finally into a probabilistic finite state automaton (PFSA). Syndromic Surveillance

31. 2.1.30 Crisis-Index Surveillance

32. 2.1.31 Emergent Surveillance Plexus (ESP) Surveillance Sensor Network Testbed Autonomous Ocean Sampling Network Types of Hotspots Hotspots due to multiple, localized, stationary sources Hotspots due to multiple, localized, stationary sources Hotspots corresponding to areas of interest in a stationary mapped field Hotspots corresponding to areas of interest in a stationary mapped field Time-dependent, localized hotspots Time-dependent, localized hotspots Hotspots due to moving point sources Hotspots due to moving point sources

33. 2.1.32 Ocean SAmpling MObile Network OSAMON

34. 2.1.33 Ocean SAmpling MObile Network OSAMON Feedback Loop Network sensors gather preliminary data Network sensors gather preliminary data ULS scan statistic uses available data to estimate hotspot ULS scan statistic uses available data to estimate hotspot Network controller directs sensor vehicles to new locations Network controller directs sensor vehicles to new locations Updated data is fed into ULS scan statistic system Updated data is fed into ULS scan statistic system

35. 2.1.34 SAmpling MObile Networks (SAMON) Additional Application Contexts Hotspots for radioactivity and chemical or biological agents to prevent or mitigate the effects of terrorist attacks or to detect nuclear testing Hotspots for radioactivity and chemical or biological agents to prevent or mitigate the effects of terrorist attacks or to detect nuclear testing Mapping elevation, wind, bathymetry, or ocean currents to better understand and protect the environment Mapping elevation, wind, bathymetry, or ocean currents to better understand and protect the environment Detecting emerging failures in a complex networked system like the electric grid, internet, cell phone systems Detecting emerging failures in a complex networked system like the electric grid, internet, cell phone systems Mapping the gravitational field to find underground chambers or tunnels for rescue or combat missions Mapping the gravitational field to find underground chambers or tunnels for rescue or combat missions

36. 2.1.35 Scalable Wireless Geo-Telemetry with Miniature Smart Sensors Geo-telemetry enabled sensor nodes deployed by a UAV into a wireless ad hoc mesh network: Transmitting data and coordinates to TASS and GIS support systems

37. 2.1.36 Architectural Block Diagram of Geo-Telemetry Enabled Sensor Node with Mesh Network Capability

38. 2.1.37 Data Fusion Hierarchy for Smart Sensor Network with Scalable Wireless Geo-Telemetry Capability

39. 2.1.38 Target Tracking in Distributed Sensor Networks

40. 392.1. Wireless Sensor Networks for Habitat Monitoring

41. 2.1.40 Video Surveillance and Data Streams

42. 2.1.41 Video Surveillance and Data Streams Turning Video into Information Measuring Behavior by Segments Customer Intelligence Enterprise Intelligence Entrance Intelligence Media Intelligence Video Mining Service

43. 2.1.42 Key Crop Areas Crops NOAA Weather Threat Locations Plants Infected Non-infected Sentinel Ground Cameras Air/Space Platforms Hyperspectral Imagery Signature Library Data Processing Anomaly Report Crop Attack Decision Support System Ground Truthing Site Identification Module Signature Development Module

44. 2.1.43 Crop Biosurveillance/Biosecurity

45. 2.1.44 Hyperspectral Imagery Signature Library Image Segmentation (hyperclustering) Proxy Signal (per segment) Disease Signature Similarity Index (per segment) Tessellation (segmentation) of raster grid Signature Similarity Map Hotspot/ Anomaly Detection Crop Biosurveillance/Biosecurity Data Processing Module

46. 2.1.45 Space-Time Poverty Hotspot Typology Federal Anti-Poverty Programs have had little success in eradicating pockets of persistent poverty Federal Anti-Poverty Programs have had little success in eradicating pockets of persistent poverty Can spatial-temporal patterns of poverty hotspots provide clues to the causes of poverty and lead to improved location-specific anti- poverty policy ? Can spatial-temporal patterns of poverty hotspots provide clues to the causes of poverty and lead to improved location-specific anti- poverty policy ?

47. 2.1.46 Hotspot Persistence Hotspot Persistence Space (census tract) 1970 1980 1990 2000 Time (census year) Persistent Hotspot Long Duration Space (census tract) 1970 1980 1990 2000 Time (census year) One-shot Hotspot Short Duration Persistence is a property of space-time hotspots Persistence can be assessed by the projection of the space-time hotspot onto the time axis

48. 2.1.47 Typology of Persistent Space-Time Hotspots-1 Space (census tract) 1970 1980 1990 2000 Time (census year) Stationary Hotspot Space (census tract) 1970 1980 1990 2000 Time (census year) Shifting Hotspot Space (census tract) 1970 1980 1990 2000 Time (census year) Expanding Hotspot Space (census tract) 1970 1980 1990 2000 Time (census year) Contracting Hotspot

49. 2.1.48 Typology of Persistent Space-Time Hotspots-2 Space (census tract) 1970 1980 1990 2000 Time (census year) Bifurcating Hotspot These hotspots are connected in space-time However, certain time slices of the hotspot are disconnected in space Space (census tract) 1970 1980 1990 2000 Time (census year) Merging Hotspot Spatially disconnected time slice

50. 2.1.49 Trajectory of a Persistent Space-Time Hotspot A space-time hotspot is a three-dimensional object Visualization can be done by displaying the sequence of time slices---called the trajectory of the hotspot Time slices of space-time hotspot Space (census tract) 1970 1980 1990 2000 Time (census year) Merging Hotspot

51. 2.1.50 Trajectory of a Merging Hotspot 19701980 1990 2000

52. 2.1.51 Trajectory of a Shifting Hotspot 19701980 1990 2000

53. 2.1.52 Tsunami Inundation and Evacuation

54. Penn State Cooperative Wetlands Center 2.1.53 Application of the Scan Statistic to Wetland and Stream Monitoring: Testing the Method in the Upper Juniata Watershed, Pennsylvania, USA Denice Heller Wardrop, Charles Taillie, Kristen Hychka, G.P. Patil, and Wayne Myers Penn State University, University Park, PA

55. Penn State Cooperative Wetlands Center 2.1.54 Potential advantages of critical areas as an indicator of stress Delineation of contributing areas is labor intensive Delineation of contributing areas is labor intensive Can critical areas give us a first cut as to wetlands in poor condition? Can critical areas give us a first cut as to wetlands in poor condition? Can they serve as an early warning? Can they serve as an early warning?

56. Penn State Cooperative Wetlands Center 2.1.55

57. Penn State Cooperative Wetlands Center 2.1.56 Restoration Targeting with SaTScan Can effectively target areas for restoration activities or best management practices Can effectively target areas for restoration activities or best management practices Can be scaled so that critical area matches management area Can be scaled so that critical area matches management area

58. Penn State Cooperative Wetlands Center 2.1.57 Network Analysis of Biological Integrity in Freshwater Streams

59. Penn State Cooperative Wetlands Center 2.1.58 Network-Based Surveillance Subway system surveillance Subway system surveillance Drinking water distribution system surveillance Drinking water distribution system surveillance Stream and river system surveillance Stream and river system surveillance Postal System Surveillance Postal System Surveillance Road transport surveillance Road transport surveillance Syndromic Surveillance Syndromic Surveillance

60. Penn State Cooperative Wetlands Center 2.1.59

61. 2.1.60

62. 2.1.61

63. 2.1.62 Geospatial Surveillance

64. 2.1.63 Mote, Smart Dust: Small, flexible, low-cost sensor node Sensor Devices RF Component of Alcohol Sensor Miniaturized Spec Node Prototype Giner’s Transdermal Alcohol Sensor

65. 2.1.64 Standards Based Geo-Processing Model

66. 2.1.65 UAV Capable of Aerial Survey

67. 2.1.66

68. 2.1.67 Geographic Surveillance Decision Support System Geoinformatic Surveillance Decision Support System Geoinformatic Surveillance Decision Support System Geographic and Network Surveillance for Arbitrarily Shaped Hotspots Geographic and Network Surveillance for Arbitrarily Shaped Hotspots Next Generation of Geographic Hotspot Detection, Prioritization, and Early Warning Next Generation of Geographic Hotspot Detection, Prioritization, and Early Warning

69. 2.1.68

70. 2.1.69 DATA SOURCESALGORITHMSHOTSPOT CRITERIAINDICATORS ETM+ Pattern-enhanced ETM+ DOQQ ETM+ Land cover Commercial, fine resolution Pattern-relative change vector analysis Contextual occurrence Topological structure Network segments and sequence Filtering variables and nesting Extents of zones and regions of interest Confidence sets Degree of degradation Correlated ranks Spatial context Recovery

71. 2.1.70 National ApplicationCase Studies A1: Carbon Management A2: Coastal Management A3: Community Growth for Infrastructure A4: Disaster Management A5: Homeland Security A6: Invasive Species Management A7: Public Health and Environment A8: Water Management and Conservation PTCS-7; UOCS-2 PTCS-7, PTCS-8; UOCS-3, UOCS-6 UOCS-3, UOCS-5 PTCS-8, UOCS-10 PTCS-1, PTCS-3, PTCS-8; UOCS-4 PTCS-6 PTCS-4, PTCS-8; UOCS-5, UOCS-8, UOCS-9 PTCS-1, PTCS-2, PTCS-5, PTCS-7, PTCS-8, PTCS-9; UOCS-1, UOCS-3, UOCS-7, UOCS-10

72. 2.1.71 Watershed prioritization for impairment and vulnerability

73. 2.1.72 Oil spill detection, monitoring, and prioritization

74. 2.1.73 Tasking of a self-organizing oceanic surveillance mobile sensor network

75. 2.1.74 Network-based analysis of integrity of coastal intertidal wetlands

76. 2.1.75 Development of remote sensing methods for crop bioterrorism

77. 2.1.76 Current state, end users, proposed development, and milestones for applications of the proposed geoinformatic decision support system

78. 2.1.77 Connectivity for tessellated regions. The collection of shaded cells on the left is connected and, therefore, constitutes a zone. The collection on the right is not connected.

79. 2.1.78 The four diagrams on the left depict different types of space-time hotspots. The spatial dimension is shown schematically on the horizontal and time is on the vertical. The diagrams on the right show the trajectory (sequence of time slices) of a merging hotspot

80. 2.1.79 Hasse diagram of a hypothetical poset (left), some linear extensions (middle), and a decision tree giving all 16 possible linear extensions (right). Links shown in dashed/red (called jumps) are not implied by the partial order. The six members of the poset can be arranged in 6!=720 different ways, but only 16 of these are valid linear extensions

81. 2.1.80 (Left) Rank-frequency table for the poset of previous figure. Each row gives the number of linear extensions that assign a given rank r to the corresponding member of the poset. Each row is referred to as a rank-frequency distribution. (Right) Cumulative rank- frequency distributions for the poset of previous figure. The curves are stacked one above the other giving a linear ordering of the elements

82. 2.1.81 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 null- hypothesis of no clusters (Monte Carlo sampling) – Compare most likely clusters in real and random data sets (Likelihood ratio test)

83. 2.1.82 2000 Data - Dead birds reported by the public - Simulation of a daily prospective surveillance system - Start date: June 1, 2000.

84. 2.1.83

85. 2.1.84

86. 852.1. 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.

87. 2.1.86 Ingredients: Ingredients: – Tessellation of a geographic region: –Intensity value G on each cell. Determines a cellular (piece- wise constant) surface with G as elevation. Imagine surface initially inundated with water Imagine surface initially inundated with water Water evaporates gradually exposing the surface which appears as islands in the sea 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? How does connectivity (number of connected components) of the exposed surface change with falling water level? ULS Connectivity Tree -1 a c b k d e f g h i j a, b, c, … are cell labels

88. 2.1.87 Think of the tessellated surface as a landform Think of the tessellated surface as a landform Initially the entire surface is under water Initially the entire surface is under water As the water level recedes, more and more of the landform is As the water level recedes, more and more of the landform isexposed At each water level, cells are colored as follows: 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: 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. Cell is a saddle point Morse index=1. Connectivity decreases. ULS Connectivity Tree -2

89. 2.1.88 a c b k d e f g h i j ULS Connectivity Tree -3 ULS Tree a a a b,c b k d e f g h i j c Newly exposed island Island grows

90. 2.1.89 ULS Connectivity Tree -4 ULS Tree a a b,c b k d e f g h i j c Second island appears d a a b,c b k d e f g h i j c Both islands grow d e f,g New leaf node (local maximum)

91. 2.1.90 ULS Connectivity Tree -5 ULS Tree a a b,c b k d e f g h i j c Islands join – saddle point d e f,g h Junction node a a b,c b d e f g h i j c Exposed land grows d e f,g h k i,j,k Root node

92. 2.1.91 Agreement/Disagreement regarding hotspot locus Agreement/Disagreement regarding hotspot locus Comparative plausibility and accuracy of hotspot delineation Comparative plausibility and accuracy of hotspot delineation Execution time and computer efficiency Execution time and computer efficiency Comparison of Tree-Structured and Circle-Based SATScan

93. 2.1.92 Network State Models Outputs (Reduced dimensional vector) – Connectivity Resource availability Utilization Occupancy distribution Blocking probabilities Outputs (Reduced dimensional vector) – Connectivity Resource availability Utilization Occupancy distribution Blocking probabilities Method of є - machines Network element behavior model Crisis behavior model Formal Language Measure Given Network Symbol Stream Crisis Index Procedure for obtaining the crisis index for network elements.

94. 2.1.93 Disease Mapping and Evaluation Elevated rate cluster detection and assessment Elevated rate cluster detection and assessment Geographic surveillance and evaluation Geographic surveillance and evaluation Comparative studies involving statistical power Comparative studies involving statistical power Geometric accuracy in cluster estimation Geometric accuracy in cluster estimation Real data projects and validations Real data projects and validations Computing and software Computing and software

95. 2.1.94 Agent Orange Dioxin Hotspots, coldspots, and midspots Hotspots, coldspots, and midspots Identification and prioritization Identification and prioritization Persistent hotspot trajectories Persistent hotspot trajectories Spatial disease monitoring, modeling, and tracking Spatial disease monitoring, modeling, and tracking Health effect surveillance system Health effect surveillance system

96. 2.1.95 Agent Orange Dioxin Ecosystem impact assessment Ecosystem impact assessment Deforestation, defoliation, remote sensing Deforestation, defoliation, remote sensing Hyperspectral change detection and accuracy assessment Hyperspectral change detection and accuracy assessment Emergent advanced raster map analysis system Emergent advanced raster map analysis system

97. 2.1.96 Crop Biosurveillance/Biosecurity

98. 2.1.97 Crop Biosurveillance/Biosecurity

99. 2.1.98 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 Surveillance Sensor Network Testbed

100. 2.1.99

101. 2.1.100 Terra Seer Long Island Study-2 August 13, 2002 STRENGTHENING SATSCAN The techniques we employ are not ‘better’ or ‘more accurate’ than the scan statistic 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 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 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

102. 2.1.101 Terra Seer Long Island Study-3 August 13, 2002 HOTSPOTS, PUBLIC AND AGENCIES Public: Being singled out as an elevated cluster community is such a source of dread for many people 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 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 Need: Hotspot rating along with hotspot detection, prioritization, and ranking interval with multicriteria

103. 2.1.102 Terra Seer Long Island Study-4 August 13, 2002 Issues and Approaches Issues and Approaches –Local Moran test, Boundary Detection, Boundary Overlap, Multi-Scalar, and Spatially Constrained Clustering Adjacency: Centroid, common border, upper level set tree neighbor Adjacency: Centroid, common border, upper level set tree neighbor Multiscalar: Multivariate response, Multivariate Binomial, Poisson, Gamma, Lognormal Multiscalar: Multivariate response, Multivariate Binomial, Poisson, Gamma, Lognormal Scale of Study: Spatial extent, Hotspot in Hotspots Scale of Study: Spatial extent, Hotspot in Hotspots Scale of Process: Resolution, Aggregation Level Scale of Process: Resolution, Aggregation Level

104. 2.1.103 Content Prospective Disease Surveillance The Spatial Scan Statistic Thyroid Cancer Incidence in New Mexico Dead Birds and West-Nile Virus Surveillance in New York City Other Current Applications

105. 2.1.104 Prospective Surveillance For a pre-specified geographical area, there are existing statistical methods for the detection of a sudden disease outbreak, e.g. CUSUM methods.

106. 2.1.105 Three Important Issues An outbreak may start locally. Can be used simultaneously for multiple geographical areas, but that leads to multiple testing. Disease outbreaks may not conform to the pre-specified geographical areas.

107. 2.1.106 Level of Aggregation If too little geographical aggregation, the rates are statistically unstable. With aggregation, there is arbitrariness in the boundaries chosen.

108. 2.1.107 Step One: Purely Spatial Scan Statistic Pre-determined time period. Geographical areas with observed number of cases and population at risk. Evaluate overlapping circles of different sizes at different locations.

109. 2.1.108 One-Dimensional Scan Statistic

110. 2.1.109 Example: Thyroid Cancer Incidence in New Mexico Data Source: New Mexico Tumor Registry Gender: Male Aggregation Level: 32 Counties Adjustments for: Age and Temporal Trends

111. 2.1.110 Thyroid Cancer Median age at diagnosis: 44 years United States (SEER) incidence: 4.5 / 100,000 United States mortality: 0.3 / 100,000 Five year survival: 95% Known risk factors: Radiation treatment for head and neck conditions. Radioactive downfall (Hiroshima/Nagasaki, Chernobyl, Marshall Islands) Work as radiologic technician (USA) or x-ray operator (Sweden).

112. 2.1.111 Purely Spatial Scan Statistic 1978 analysis Data Years Cases Expected RRp=Most Likely Cluster 1973-78 Bernadillo,Valencia 46 35 1.3 0.44

113. 2.1.112 Time-Periodic Surveillance New cases are added on a yearly basis. Reanalysis when new data arrives. Data Available: 1973-1992 Surveillance Starts: 1978 Total Cases: 333

114. 2.1.113 Purely Spatial Scan Statistic Data Years CasesExpectedRRp=Most Likely Cluster 1973-78 Bernadillo,Valencia 46 35 1.3 0.44 1973-79 Bernadillo,Valencia 52 40 1.3 0.48 1973-80 Bernadillo,Valencia 58 45 1.3 0.39 1973-81 Bernadillo,Valencia 68 51 1.3 0.10 1973-82 Bernadillo,Valencia 79 58 1.4 0.04 1973-83 Bernadillo,Valencia 84 62 1.4 0.04 1973-84 North Central Counties 113 90 1.3 0.05 1973-85 North Central - SanMiguel 115 95 1.2 0.18 1973-86 North Central + Colfax,Harding 129 108 1.2 0.16 1973-87 North Central + Colfax,Harding 142 117 1.2 0.05 1973-88 North Central - SanMiguel 143 115 1.2 0.02 1973-89 North Central + Colfax,Harding 165 134 1.2 0.01 1973-90 North Central + Torrance 174 144 1.2 0.02 1973-91 North Central + Colfax,Harding 186 144 1.2 0.02 1973-92 North Central + Torrance 199 164 1.2 0.01 North Central Counties = Bernadillo, Los Alamos, Mora, Rio Arriba, Sandoval, San Miguel, Santa Fe and Taos.

115. 2.1.114 North Central Counties

116. 2.1.115 Two Problems While we are adjusting for the multiple testing stemming from many possible cluster locations and cluster sizes, we are not adjusting for the multiple testing due to repeated analyses every year. Low power to quickly detect emerging clusters.

117. 2.1.116 Solution: Space-Time Scan Statistic

118. 2.1.117 Hypothesis Test Find Likelihood for Each Choice of Cylinder Through Maximum Likelihood Estimation, Find the Most Likely Cluster Apply Likelihood Ratio Test Evaluate Significance Through Monte Carlo Simulation

119. 2.1.118 Space-Time Scan Statistic Alive Clusters YearsMost Likely Cluster CasesCluster Period Expected RRp=p= 73-79 LosAlamos, Rio Arriba 75-79 9 3.3 2.7 0.58 73-78 Bernadillo + 7 counties West 75-78 48 36 1.4 0.60 73-80 LosAlamos, Rio Arriba 75-80 10 3.8 2.6 0.54 73-81 North Central – SanMiguel 75-81 72 53 1.4 0.19 73-82 North Central – SanMiguel 75-82 85 62 1.4 0.08 73-83 Bernadillo, Valencia 73-83 84 62 1.4 0.13 North Central Counties = Bernadillo, Los Alamos, Mora, Rio Arriba, Sandoval, San Miguel, Santa Fe and Taos. 73-84 North Central 73-84 113 90 1.3 0.14 73-85 Lincoln 85 3 0.2 13.8 0.23 73-86 North Central + Colfax, Harding 73-86 129 108 1.2 0.49 73-87 North Central + Colfax, Harding 73-87 142 117 1.2 0.21 73-88 North Central – SanMiguel 73-88 143 115 1.2 0.08 73-89 North Central + Colfax,Harding 73-89 165 134 1.2 0.06 73-90 LosAlamos, RioArriba, 79-90 41 22 1.8 0.06 SantaFe, Taos 73-91 LosAlamos 89-91 7 0.9 7.6 0.02

120. 2.1.119 Los Alamos

121. 2.1.120 Problem We have still not adjusted for repeated time- period analyses conducted every year.

122. 2.1.121 Adjusting for Yearly Surveillance While interest is only in ‘alive’ clusters, the p-value will be calculated based on the probability of obtaining a likelihood higher than the observed for any cylinder used during the present or past analyses. This is done using a space-time scan statistic evaluating all cylinders irrespectively of start and end year.

123. 2.1.122 Adjusting for Yearly Surveillance The Los Alamos Cluster 1991 Analysis: p=0.13 (unadjusted p=0.02) 1992 Analysis: p=0.016 (unadjusted p=0.002)

124. 2.1.123 Los Alamos cases

125. 2.1.124 Thyroid Cancer in Los Alamos The New Mexico Department of Health have investigated the individual nature of all 17 male thyroid cancer cases reported in Los Alamos 1970-1995. All were confirmed cases.

126. 2.1.125 Thyroid Cancer in Los Alamos 3/17 had a history of therapeutic ionizing radiation treatment to the head and neck. 8/17 had been regularly monitored for exposure to ionizing radiation due to their particular work at the Los Alamos National Laboratory. 2/17 had had significant workplace-related exposure to ionizing radiation from atmospheric weapons testing fieldwork. A know risk factor, ionizing radiation, is hence a likely explanation for the observed cluster.

127. 2.1.126

128. 2.1.127

129. 2.1.128

130. 2.1.129 2001 Data Real time prospective surveillance Daily analyses starting June 22

131. 2.1.130

132. 2.1.131

133. 2.1.132

134. 2.1.133

135. 2.1.134

136. 2.1.135 Syndromic Surveillance Symptoms of disease such as diarrhea, respiratory problems, headache, etc Earlier reporting than diagnosed disease Less specific, more noise

137. 2.1.136 Other Syndromic Surveillance Data Sources 911 Ambulance Dispatches Pharmacy Sales Employee Sick Leave Physician Visits Veterinary Clinic Visits

138. 2.1.137 Conclusions The space-time scan statistic can serve as an important tool in prospective systematic time- periodic geographical surveillance for the early detection of disease outbreaks. It is possible to detect emerging clusters, and we can adjust for the multiple tests performed over the years. The method can be used for different diseases.

139. 2.1.138 References Early Detection: Kulldorff M. Prospective time-periodic geographical disease surveillance using a scan statistic. J Royal Statistical Society, A164:61-72, 2001. West Nile: Mostashari F, Kulldorff M, Miller J. Dead bird clustering: An early warning system for West Nile virus activity. Under review. Software: Kulldorff M, Rand K, Gherman G, Williams G, DeFrancesco D. SaTScan v.2.1: Software for the spatial and space-time scan statistics. Bethesda, MD: National Cancer Institute, 1998. http://www.cancer.gov/prevention/bb/satscan.html