NISAC PUBLIC HEALTH SECTOR: Disease Outbreak Consequence Management

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NISAC PUBLIC HEALTH SECTORDisease Outbreak
Consequence Management

• Stephen Eubank
• Los Alamos National Laboratory
• April 2003

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Interdependent Infrastructure Simulations
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Public Health Infrastructure

• Includes
• Distribution of medicines and health care
• Command / control for isolation, quarantine,
  emergency response
• Monitoring / outbreak detection
• Operates on mobile people
• No mobility ? no consequence management problem
• Disease spread intricately connected to mobility
• People defined as users of transportation
  infrastructure
• Interactions with other sectors
• Food or water-borne disease
• Demand for distribution of basic life-support
  (food, water, energy)
• Robust against disease ? susceptible to natural
  disaster ??

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Simulate outbreak to evaluate objective (cost)
function

• Path of outbreak determined by individuals use
  of infrastructure
• Public Health controls behavior and response of
  individuals
• Interaction with other sectors mediated by
  individuals
• Model complex interactions between aggregate
  systems
• OR
• Simulate much simpler interactions between many
  individuals

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Individual-Based Models Complement Traditional
Epidemiological Models

• Traditional rate equations model subpopulations
• Subpopulation based on a few demographics
• Subpopulation mixing rates unknown
• Reproductive number not directly observable

Under age 15
age 15 - 55
Susceptible
Reproductivenumber
Mixing rate
Infected
Recovered
Over age 55
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Individual-Based Models Complement Traditional
Epidemiological Models

• Individual-Based Model
• Individuals carry many demographics
• Individual contact rates estimated independently
• Reproductive number emerges from transmission

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Top Down Structuring is Ambiguous
Homogenous Isotropic
?
alternativenetworks
. . .
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Why Instantiate Social Networks?

• N vertices -gt 2(N2) graphs(non-identical
  people -gt few symmetries)
• E edges -gt N(2E) graphs
• Degree distribution -gt ?? graphs
• Clustering coefficient -gt ?? graphs
• What additional constraints -gt graphs equivalent
  w.r.t. epidemics?

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Measures of Centrality

• Same degree distribution (green vertices are
  degree 4, orange degree 1)
• Different assortative mixing by degree

High betweenness
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Gaps in existing technology

• Need novel combination of scale and resolution
• Ackerman, Halloran, Koopman individual
  resolution, only 1000 people
• Murray, Hethcote, Kaplan, many othersmixing in
  infinitely large populations, no resolution
• EpiSims millions of individual people
  interacting with other sectors
• Initial stages crucial for response
• Individual based simulation only tool focused
  there

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Individual-based epidemiology a road map
Familys activities
Contact matrix for entire population
Epidemic snapshot
Epidemic curves
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A Typical Familys Day
Work
Lunch
Work
Carpool
Carpool
Shopping
Home
Home
Car
Car
Daycare
Bus
School
Bus
time
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Others Use the Same Locations
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Time Slice of a Typical Familys Day
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Whos in contact doing what at 10 AM?
Work
Shopping
Daycare
School
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A Scared Familys Possible Day
Home
Home
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Representing contacts adjacency matrix
Contact matrix for entire population(at 10 AM)
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Representing Contact Patterns Social Network
Graph
Household of 4 (distance 0)
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Representing Contact Patterns Social Network
Graph
Contacts of people in the household (distance 0 ?
1)
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Representing Contact Patterns Social Network
Graph
Contacts among the households contacts (within
distance 1)
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Representing Contact Patterns Social Network
Graph
Contacts contacts (distance 1 ? 2)
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Representing Contact Patterns Social Network
Graph
Contacts among the contacts contacts (within
distance 2)
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distance 2 ? 3
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Within distance 3
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Local network to distance 3
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Local network to distance 3 (Side view)
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Disease Progression Model
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Transmission Implementation I
If contagious, a person sheds into environment
at a rate proportional to his/her load.
environment
Each person absorbs from environment ata
different rate proportional to its contamination.
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Transmission Implementation II

• Stochastic transmission from contagious to
  susceptibles in the same location

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How Technology Answers Specific Questions

• Assess mitigation strategies (OHS study)
• Identify critical path for disease spread (OHS
  request)
• Determine optimal sensor deployment
• Support tabletop exercises
• Evaluate logistical requirements for responders
• Develop requirements for effective vaccine
• Decision support for medical surveillance

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Example 1 mitigation strategies

• Attacks on complementary demographics
• Shopping mall
• University
• Responses
• Baseline no response
• Mass vaccination
• Targeted vaccination isolation
• Targeted, but with limited resources
• Implementation delay 4, 7, 10 days
• Policy self-imposed isolation (withdrawal to the
  home)
• Before becoming infectious (EARLY)
• 12-24 hours after becoming infectious (LATE)
• NEVER

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Example 1 targeted vax isolation
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Example 1 targeted, limited resources
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Example 1 overall results
( dead by day 100) / ( attacked)
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Example 1 overall results
( dead by day 100) / ( attacked)
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Example 1 overall results
( dead by day 100) / ( attacked)
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Example 2 critical path

• Study properties of social network directly
• Study random graphs resembling social networks
• Simulate to find disease mixing rates

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Example 2 contact pattern variability
Strangers contacts
Infecteds contacts
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Example 2 metrics for social networks

• Vertex degree, clustering too local
• Other classical graph-theoretical measures of
  centrality
• Betweenness too global to compute efficiently
  (but sampling may give provably good
  approximations)
• Finite-radius betweenness?
• e.g. how many paths of length ? d use a
  particular edge
• reflects importance of incubation period

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Example 2 mixing rate experimental design

• Infect samples of a very specific demographic
  group
• E.g. households with at least 3 children under 18
  and 1 child between 5 and 10
• Not intended to model attack or natural
  introduction
• Pick groups at extremes of gregariousness
• Estimate demographics of each cohort (disease
  generation)
• Compare to demographics of entire population

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Example 3 optimal sensor deployment

• Suppose we have a bio-sensor that detects
  infected people.
• How many sensors must be deployed to cover a
  fixed fraction of the population?
• Where?
• Who is covered?
• Evaluate cost/benefit of sensor refinements

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Algorithms for coverage

• Dominating set
• on bipartite graph (locations and people)
• 2 million vertices, 10 million edges
• but with little overlap between high degree
  locations
• Fast, very good approximate solutionsMarathe,
  Wang, Vullikanti, Ravindra

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Example 4 Tabletop exercises

• Compare with scripted casualties as in Dark
  Winter
• Reacts to decisions
• Connects to evacuation planning and other sectors
  addressed in most exercises

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Example 5 responder logistics

• Resources required to implement response
• Demand placed on resources by sick, worried well
• Demand placed on other infrastructures
• Public health
• Transportation (evacuation, service delivery)
• Communication (phone networks overloaded)
• Power, water, food distribution

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Example 6 vaccine design

• Postulate vaccine properties
• Contra-indications
• Communicability of vaccine induced illness
• Time between vaccination and protection
• Efficacy at preventing infection / transmission
• Simulate trials to establish consequences
• Disease casualties
• Direct casualties of vaccination
• Indirect casualties of vaccination
• Interruption of social enterprise

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Example 7 medical surveillance

• Anomaly in number of people presenting certain
  symptoms provokes suspicion of disease outbreak
• Simulation estimates populations health state
  over near future under hypothesis
• Verify against observations

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Possible Future Directions

• Licensing software, partnering, outreach
• Generic / parameterized cities
• Software development
• User interface
• More flexible health characteristics generator
• Multiple days / seasonality / weekends
• Multiple co-circulating (interacting) diseases
• Simulation state manipulation
• Additional exogenous events