Incorporating Geographical Contacts into Social Network Analysis for Contact Tracing in Epidemiology

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Incorporating Geographical Contacts into Social
Network Analysis for Contact Tracing in
Epidemiology A Study of Taiwan SARS Data

• Hsinchun Chen Yida Chen Cathy Larson Chunju
  Tseng The BioPortal Team, Artificial
  Intelligence Lab, University of Arizona
• Chwan-Chuen King, Tsung-Shu Joseph Wu, National
  Taiwan University
• Acknowledgements NSF ITR Program

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Research Objectives

• Investigate the necessity of incorporating
  geographical contacts into SNA for contact
  tracing
• Explore the strengths of multi-mode networks with
  patients and geographical locations for disease
  investigation
• Examine the possibility of using SNA with
  geographical nodes to identify epidemic phases

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Social Network Analysis in Epidemiology

• Conceptualizing a population as a set of
  individuals linked together to form a large
  social network provides a fruitful perspective
  for better understanding the spread of some
  infectious diseases. (Klovdahl, 1985)
• Social Network Analysis in epidemiology has two
  major activities
• Network Construction
• Link the whole set of persons in a particular
  population with relationships or types of
  contacts
• Network Analysis
• Measure and make inferences about structural
  properties of the social networks through which
  infectious agent spread

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A Taxonomy of Network Construction
CDC Centers for Disease Control and Prevention
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A Taxonomy of Network Analysis
CDC Centers for Disease Control and Prevention
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Network Visualization

• Utilize social network to visualize the
  transmission of an infectious agent from one
  person to another within a particular population
• Focus on the identification of
• Subgroups within the population
• Characteristics of each subgroup
• Bridges between subgroups which transmit a
  disease from a subgroup to another

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Epidemic Phases and Social Networks

• Potterat et al. (2001) proposed that structure of
  sexual networks is a more reliable indicator of
  STD epidemic phase.
• Two sexual networks in Colorado Springs, U.S.
  were compared
• Bacterial STD from 1990 to 1991 (a STD outbreak)
• Chlamydia from 1996 to 1999 (stable or declining
  phase)
• Sexual network in stable or declining phase was
  relatively
• Fragmented
• Dendritic
• Lack of cyclic structures
• Cunningham et al. (2004) further examined the
  relationship between network characteristics and
  epidemic phases.
• After epidemic
• Macro-level structure
• Average distance declined.
• Density increased.
• Micro-level structure
• Numbers of n-cliques and k-plexes declined.

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Research Questions

• What are the differences in connectivity between
  personal and geographical contacts in the
  construction of contact networks?
• What are the differences in network topology
  between one-mode networks with only patients and
  multi-mode networks with patients and
  geographical locations?
• Whether SNA with geographical nodes can be used
  to identify epidemic phases of infectious
  diseases with multiple transmission modes?

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Research Test Bed

• We use Taiwan SARS data as our research test bed.
• SARS (Severe Acute Respiratory Syndrome) is a
  novel infectious disease which emerged in 2002.
• The first human case was identified in Guangdong
  Province, China on November 16, 2002. (Donnelly
  et al., 2004)
• A 65-years-old doctor from Guangdong Province
  stayed at a hotel in Hong Kong in February 2003
  and infected at least 17 other guests and
  visitors at the hotel, some of whom later came to
  other countries and initiated local transmission
  of SARS. (Peiris et al., 2006)
• 26 countries, including Vietnam, Singapore,
  Canada, and Taiwan, reported SARS cases.
• Financial impact 50B

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SARS in Taiwan

• The first SARS case in Taiwan was a Taiwanese
  businessman who traveled to Guangdong Province
  via Hong Kong in the early February 2003.
• Had onset of symptoms on February 26, 2003
• Infected two family members and one healthcare
  worker
• Eighty percent of probable SARS cases were
  infected in hospital setting.
• The first outbreak began at a municipal hospital
  in April 23, 2003.
• Total seven hospital outbreaks were reported.
• Hospital shopping and transfer were suspected to
  trigger such sequential hospital outbreaks.

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Taiwan SARS Data

• Taiwan SARS data was collected by the Graduate
  Institute of Epidemiology at National Taiwan
  University during the SARS period.
• In this dataset, there are 961 patients,
  including 638 suspected SARS patients and 323
  confirmed SARS patients.
• The contact-tracing data of patients in this
  dataset has two main categories, personal and
  geographical contacts, and nine types of
  contacts.
• Personal contacts family member, roommate,
  colleague/classmate, and close contact
• Geographical contacts foreign-country travel,
  hospital visit, high risk area visit, hospital
  admission history, and workplace

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Taiwan SARS Data (Cont.)

• Hospital admission history is the category with
  largest number of records (43).
• Personal contacts are primarily comprised of
  family member records.

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Research Design
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Phase Analysis

• In the phase analysis, we want to examine whether
  epidemic phases of an infectious disease with
  multiple transmission modes, such as SARS, could
  be identified through SNA with geographical
  nodes.
• SARS transmission in Taiwan has two main phases
• Importation (February to the middle of April
  2003)
• Small clusters of local transmission were
  initiated by the imported cases of SARS.
• Patients were primarily infected through
• Travels in the mainland China and Hong Kong
  (Geographical contacts)
• Family Transmission
• Hospital Outbreaks (The middle of April to July
  2003)
• Patients were primarily infected through
• Hospital related contacts (Geographical contacts)
• Close personal contacts

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Phase Analysis (Cont.)

• Network Partition
• We partition each contact network on a weekly
  basis with linkage accumulation.
• From 2/24 to 5/4, there are 10 weeks in total.

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Phase Analysis (Cont.)

• Network Measurement
• We investigate two factors that contribute to the
  transmission of disease in macro-structure
• Density the degree of intensity to which people
  are linked together
• Density
• Average degree of nodes
• Transferability the degree to which people can
  infect others
• Betweenness
• Number of components

Higher density
Lower density
Lower Transferability
Higher Transferability
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Phase Analysis (Cont.)

• Measuring weekly changes

for i 2 to n
where
Ai a network measure of Week i partition
An a network measure of the last week partition
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Connectivity Analysis

• Geographical contacts provide much higher
  connectivity than personal contacts in the
  network construction.
• Decrease the number of components from 961 to 82
• Increase the average degree from 0.31 to 108.62

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Connectivity Analysis (Cont.)

• The hospital admission history provides the
  highest connectivity of nodes in the network
  construction.
• The hospital visit provides the second highest
  connectivity.
• This result is consistent with the fact that most
  of patients got infected in the hospital
  outbreaks during the SARS period.

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One-Mode Network with Only Patient Nodes
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Contact Network with Geographical Nodes
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Potential Bridges Among Geographical Nodes

• Including geographical nodes helps to reveal some
  potential people who play the role as a bridge to
  transfer disease from one subgroup to another.

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Network Visualization (Cont.)

• For a hospital outbreak, including geographical
  nodes and contacts in the network is also useful
  to see the possible disease transmission scenario
  within the hospital.
• Background of the Example
• Mr. L, a laundry worker in Heping Hospital, had a
  fever on 2003/4/16 and was reported as a
  suspected SARS patient.
• Nurse C took care of Mr. Liu on 4/16 and 4/17.
• Nurse C and Ms. N, another laundry worker in
  Heping Hospital, began to have symptoms on 4/21.
• Heping Hospital was reported to have an SARS
  outbreak on 4/24.
• Nurse Cs daughter had a fever on 5/1.

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Phase Analysis Density

• Normalized density and average degree show
  similar patterns
• In the importation phase, foreign-country contact
  network increases dramatically in Week 4
  (3/17-3/23), followed by personal contact
  network.
• In the hospital outbreak phase, both personal and
  hospital networks increase dramatically. But in
  Week 10, personal network still increases while
  hospital network decreases.

Density
Average Degree
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Phase Analysis Transferability

• From betweenness, we can see that personal
  network doesnt have enough transferability until
  Week 9.
• Personal network just forms several small
  fragments without big groups in the importation
  phase.
• From the number of components, hospital network
  is the only one which can consistently link
  patients together.

Hospital Outbreak
Hospital Outbreak
Importation
Importation
Betweenness
Number of Components
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Phase Analysis Hospital Outbreak

• We further partition hospital network by patients
  and healthcare workers (HCW).
• From density and betweenness, we can see that
  before Week 9 hospital network is mainly affected
  by patients hospital contacts. However, after
  Week 9, healthcare worker contacts lead the trend.

Hospital Outbreak
Hospital Outbreak
Importation
Importation
Density
Betweenness
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Conclusions

• Geographical contacts provide much higher
  connectivity in network construction than
  personal contacts.
• Introducing geographical locations in SNA
  provides a good way not only to see the role that
  those locations play in the disease transmission
  but also to identify potential bridges between
  those locations.
• SNA with geographical nodes can demonstrate the
  underlying context of transmission for the
  infectious diseases with multiple modes.

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Future Directions

• Include transportation contacts in the network
  construction and visualization
• Extract potential disease transmission paths from
  a contact network
• Incorporate statistical tests with SNA for
  identifying epidemic phases