Outline Dimensions of human movement - Scales of movement - - - PowerPoint PPT Presentation

Linking fine scale mobility and dynamic contacts to understand the spatial dimension of pathogen transmission Gonzalo Vazquez-Prokopec

Department of Environmental Studies and Global Health Institute, Emory University. Fogarty International Center, National Institutes of Health. gmvazqu@emory.edu www.prokopeclab.org

Ohio State University September 18, 2012

Dimensions of human movement

• Scales of movement
• Movement and infectious diseases
• Methods for quantifying movement
• Importance of resource-poor settings

Case studies

• Human influenza virus
• Dengue fever
• Cryptosporidium spp.

Conclusions

Outline

Human mobility and social contacts

Significant factor determining disease dynamics and pathogen mixing, propagation, and evolution.

Eames & Keeling, PNAS 2002

Dimensions of movement and transmission

Stoddard et al. 2009

Human movement determines individual exposure and ID transmission dynamics

Local movement

Key scale for understanding exposure and local spread e.g. Within-city dynamics. Poorly studied due to data scarcity. Recently: Cell-phone and GPS.

Gonzalez et al. 2008. Science

Models of ID epidemic spread and containment What if…?

Eubank et al. 2004. Science

Quantitative approaches

Contiguity analysis (probability of movement i-j) Distance from point i Statistical characterization by groups Network spatial analysis Lattice models, cellular automata Diffusion, dispersal kernels Meta-population models, gravity models Network dynamic models

Statistical vs mathematical.

Analytical and computational complexity

Adapted from Riley 2007. Science

RS GPS

Spatial Analysis Modeling

GIS

Case studies:

• Dengue fever
• Human influenza virus
• Cryptosporidium spp.

Dengue epidemics: explosive and widespread

Neff et al. 1967. American Journal of Epidemiology

Space-time analysis & modeling

PP MN WC MB

250/383 cases (65%) belonged to 18 space-time clusters Spatial heterogeneity driven by housing type (queenslander)

Speed of virus propagation: Kernel of transmission Slow local propagation due to mosquito flight Fast circulation due to human movement

Need for spatially explicit consideration

• f exposure and transmission patterns

Human movement and dengue transmission in Iquitos, Peru

Immediate Goal of the Study Determine the locations most visited by participants and assess the risk of acquiring dengue in such locations Collaborators

Thomas W. Scott, Amy Morrison, Steven Stoddard – UC Davis John Elder – San Diego State Valerie Paz Soldan – Tulane Gonzalo Vazquez-Prokopec, Uriel Kitron – Emory Tad Kochel – NMRCD (US Navy- NAMRU)

Funded by NIH/NIAID

Human spatial behavior

• Human geography, behavioral sciences,

neurosciences, physics, mathematics

• Activity space: the local areas within

which people move or travel in the course of their daily activities

• Can be used to summarize routines
• Represented by Nodes (locations)

connected by Paths (routes)

• Two neighborhoods (Maynas and

Tupac Amaru)

• Pilot study: 120 participants
• Final study: 2,500 participants
• Retrospective Activity Space

(Cluster Studies): Characterizes key locations visited when infected

• Prospective Activity Space: Key

locations visited in an individual’s monthly routine

Study design

M T

Challenges in the estimation of activity spaces

• Issues of recall, reliability, reproducibility,

compliance, behavioral change, and privacy

• Alternative: use of GPS technology

– Used in the past – Costly and technology challenging

• Traditional methods: direct observations, diaries and

interviews

Using GPS to track human movements

Key features: memory and battery life; durable and tamper-proof; light weight; design widely accepted by participants; little to no maintenance required of participants; low cost ($50). Accuracy: 4-10 m

Strong input from social scientists

Pamphlet developed to provide information about GPS

What data do we obtain from GPS?

• Latitude, longitude, date, time, elevation

Participant A Participant B 61 participants

Representing movement data

Contact networks – nodes represent individuals (or locations), links represent relationships allowing pathogen transmission

Bipartite & spatial topology

Vazquez-Prokopec & Bansal, in prep.

Mobility Networks

Distribution of number of potential contacts a person has due to mobility

Vazquez-Prokopec et al. Submitted.

Human mobililty and dengue transmission

Sampled contact networks:

• DENV-4 positive
• DENV-4 negative

IC

Stoddard et al. submitted

Two seasons. Balanced design (positive and negative clusters)

Human mobility influences dengue transmission

Attack rates (# infected / total tested) and infestation (proportion of houses with ≥1 infection) significantly higher in networks of infected individuals

DENV + DENV -

Stoddard et al. submitted.

Tracking ~600 individuals with GPS

GPS: latitude, longitude, elevation, time. 2,500,000 data points. Sample balanced between ages and sexes. Goal: estimate mobility parameters and dengue exposure.

Vazquez-Prokopec et al. in prep.

Characterizing mobility parameters

Mobility kernels (Pb of moving m meters from home Δd)

Most ~75-82% movements within 1km from home. Males: higher probability of moving further than females

?

(m) (m)

Brokman et al. Nature. 2006 Bank notes (US) Gonzalez et al. Nature. 2008 Cell phone (Europe)

Characterizing mobility parameters (2)

Number and type of locations

People routinely visited, on average, 4-6 locations. Ages 36-45 had the highest dispersion in their visitation patterns. Most places visited were residential, followed by commercial (stores, markets).

?

Eubank et al. Science 2004

Transportation models predict people visit 2-4 locations and a maximum of 14 locations.

Transportation (US)

Dynamic movements and contacts

Individuals present highly dynamic and unstructured routines.

Song et al. Science 2010

Cell phone data shows people in developed cities follow highly structured routines

Dynamic movements and contacts

Unstructured routines and influenza dynamics

Agent-based stochastic model including “mobility rules”

• Distance
• Number of locations
• Type of location

Structure of routines-> μ

• Distribution of hours/visit

μ = 1 -> highly unstructured μ = 4 -> highly structured Iquitos μ=3 10,000 people & 4,000 locations

Simulating movements

Perkins et al. in prep Bisanzio et al. in prep.

Model: Tractable contact structure. 50 simulations running hourly contacts and lasting for 200 days. Run in net-logo (first) and python (now).

Modeling influenza dynamics

The consideration

• f temporally

unstructured routines increased both an epidemic’s final size and effective reproduction number by 20% in comparison to models assuming temporally structured contacts.

Cryptosporidium spp. spillover

Introduction of human Crypto into Gombe Chimpanzee populations (Tanzania). Sources:

• Humans
• Domestic animals (dogs, goats, sheep)
• Environmental overlap in crop-raiding areas
• Drainage networks.

Are areas of intense activity potential spillover hot-spots?

Parsons et al. in prep

Are goats the “vectors”? Is the crop-raiding area the spillover hot-spot? How does the landscape impact persistence and transmission?

Conclusions

• GISscience can complement other quantitative

methods, but will require adaptation to new challenges (big data, dynamic visualization and simulation).

Multi-disciplinary approach

• Integration of good data and proper models allow:
• Identifying sources of infection.
• test impact of interventions (location/people)
• Gain deeper eco-epidemiological understanding
• Empiric consideration of fine-scale mobility can

help unveil inherent complexities of pathogen transmission (esp. in resource-poor settings).

Acknowledgements

Funding:

• Emory University
• NIH/NIAID

Cairns: Scott Ritchie, Peter Horne, Jeffrey Hanna, Brian Montgomery Iquitos: Movement team; phlebotomists; entomologists; GIS/data entry; Iquitos residents