Post on 22-Jul-2020
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
MODULE 5: Spatial Statistics in Epidemiologyand Public Health
Lecture 9: Disease Ecology
Jon Wakefield and Lance Waller
1 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Disease Ecology: What do we want to do?Pattern and ProcessGaps and Bridges: Ecology and Statistics
Point Processes: Chagas Disease in PeruCluster detectionSpatial relative risk
Spatial Regression: Raccoon RabiesStatistical estimation of landscape barriersWomblingSpatially varying coefficients
2 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Pattern and ProcessGaps and Bridges: Ecology and Statistics
Disease Ecology
I Interactions between virus, host, landscape.
I Landscape epidemiology (Pavlovsky, 1967), landscape ecology(Manel et al. 2003, TrEE), spatial epidemiology (Osfeld et al.2005, TrEE), landscape genetics (host and virus) (Biek et al.2006, Science), conservation medicine (Aguirre et al. 2002).
I People, animals, diseases, ecology, environment!
I Spatio-temporal data, mathematical models, geneticsequences, missing data, GIS!
3 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Pattern and ProcessGaps and Bridges: Ecology and Statistics
Epizoology and Epidemiology
I Most emergent infectious diseases have animal reservoir(WNV, Ebola, Avian influenza, Monkeypox, SARS, HIV/SIV).
I History of animal/human disease (Torrey and Yolken, 2005,Beasts of the Earth).
I Interesting intersection of modelers, ecologists, statisticians,medical geographers, ecological geneticists, public healthresearchers, epidemiologists.
I One Health.
4 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Pattern and ProcessGaps and Bridges: Ecology and Statistics
The “big picture”
Map oftruecasesin hosts
Map ofdiag’dcasesin hosts
Map ofrep’dcasesin hosts
Reportingfilter
Diagnosisfilter
Surveillance of Host Disease
Landscapeand
Climate
Hostdistribution
Vectordistribution
Pathogendistribution
Modeling Disease Ecology
Ecology Public Health
5 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Pattern and ProcessGaps and Bridges: Ecology and Statistics
Not a new idea (Koch, 2005, Cartographies of Disease)
6 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Pattern and ProcessGaps and Bridges: Ecology and Statistics
Pattern and Process
I Our ultimate goal is understanding the ecological processesdriving the patterns we see in our observations.
I When linking process (model or reality) to pattern (data),typically:I Ecology focus: Process to pattern
I Emphasis on mathematical model, link to available data
I Statistics: Pattern to processI Collected data, hypothesis test or analytic (e.g., regression)
model.
7 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Pattern and ProcessGaps and Bridges: Ecology and Statistics
Ultimately futile exercise?
I Process may not yield unique pattern (e.g., chaos,stochasticity).
I Pattern may not reveal unique process without additionalinformation (e.g., spatial point patterns, Bartlett (1964)).
I But the real question is, “Can we learn more than we alreadyknow?”
I If not, what additional data do we need?
8 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Pattern and ProcessGaps and Bridges: Ecology and Statistics
The whirling vortex
1
Questions you
want to answer
Data you need
to answer the
questions.
Questions you
can answer with
the data you have.
Data you can
get.
How close?
9 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Pattern and ProcessGaps and Bridges: Ecology and Statistics
Exactly wrong or approximately correct?
I John Tukey noted an approximate answer to the right questionis better than a precise answer to the wrong question.
I Particularly important here...if available data redefine ouranswerable questions, we may be changing course withoutrealizing it!
I Let’s look at how modelers and statisticians address thesequestions...
10 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Pattern and ProcessGaps and Bridges: Ecology and Statistics
Gaps and Bridges
Conceptual gapI Mathematical modelers
I Build assessments using families of models and derivingproperties.
I Data used to calibrate models.I Using process (model) to understand pattern (data).
I StatisticiansI Build inference from probability model defining observations.I Data define a likelihood function.I Using pattern (data) to understand process (model).
11 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Pattern and ProcessGaps and Bridges: Ecology and Statistics
Gaps and Bridges
Training gap
I In current modes of training, mathematical modelers oftentake one (or fewer) courses in statistics.
I Statisticians often take one (or fewer) courses inmathematical modeling.
I Furthermore, the importance of one area is seldom stressed inthe other.
I Few working at the intersection of the two but there is a lot ofinteresting work to be done!
To see how this might work, consider the following...
12 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Pattern and ProcessGaps and Bridges: Ecology and Statistics
How statistics might help...(Ecology, 2010)
1
True
model
Observed
data
Observed
associations
Proposed
model
Generated
data
Induced
associations
Goodness of fit
Regressions,
correlations, etc.
Parameter estimation
13 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Cluster detectionSpatial relative risk
Point Processes in Disease Ecology: Chagas disease in Peru
I Joint work with Michael Levy (Fogarty International Center,NIH)
I Chagas disease: Vector borne disease (infection with T. cruzi).
I Vector (in southern Peru): Triatoma infestans.
I Study area: Guadalupe, Peru (peri-urban).
I Fields surrounding rocky hilltops with houses.
I GPS all household locations.
I Spraying campaign, identify house locations, houses withvectors (“infested”), and houses with infected vectors(“infected”).
14 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Cluster detectionSpatial relative risk
How to find a cluster?
I Consider two approaches: scan statistic and intensityestimators.
I Spatial scan statistic:I Define set of potential clusters (elements of scanning window).I Assign “score” to each potential cluster.I Find “most likely cluster” (MLC) as potential cluster with
extreme score.I Evaluate significance of most likely cluster via Monte Carlo
simulation.I Compare observed “score” of MLC to distribution of scores
MLCs (regardless of location) under random assignment.I SaTScan software (www.satscan.org).
15 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Cluster detectionSpatial relative risk
SaTScan, Infested among households, Most likely cluster(p=0.002)
−71.592 −71.591 −71.590 −71.589 −71.588 −71.587
−16.441
−16.440
−16.439
−16.438
−16.437
−16.436
−16.435
Longitude
Latitude
SaTScan, Most Likely Cluster, Infested, p−value = 0.002
16 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Cluster detectionSpatial relative risk
SaTScan, Infected among infested, Most likely cluster(p=0.181)
−71.592 −71.591 −71.590 −71.589 −71.588 −71.587
−16.441
−16.440
−16.439
−16.438
−16.437
−16.436
−16.435
Longitude
Latitude
OOOOO OOOO
OOO
O
OOOOOOOO
O OO
O
O
OOOOOOOOOOOOO OOOO
O
OOOOO
OOO
OOOO
OO
OO OOO
O
OOOO
OOOO
SaTScan, Most Likely Cluster, Infected, p−value = 0.181
17 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Cluster detectionSpatial relative risk
Chagas SaTScan conclusions
I Statistically significant cluster of infested households amongall households.
I No statistically significant cluster of infected householdsamong infested households.
I Note circular most likely cluster may include gaps (top of hill).
I What about non-circular clusters?
18 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Cluster detectionSpatial relative risk
Kernel intensity estimates, infested vs. all households
−71.592 −71.590 −71.588
−16.441
−16.439
−16.437
−16.435
Longitude
Latitude
Infested sites
−71.592 −71.590 −71.588
−16.441
−16.439
−16.437
−16.435
Longitude
Latitude
Non−infested sites
19 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Cluster detectionSpatial relative risk
Ratio of kernel intensity estimates, infested vs. allhouseholds
−71.592 −71.591 −71.590 −71.589 −71.588 −71.587
−16.441
−16.440
−16.439
−16.438
−16.437
−16.436
−16.435
Longitude
Latitude
Log relative risk surface: Infested
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||| ||||||||||||||||||| |||||||||||||||||| ||||||||||||||||||| |||||||||||||||||||| ||||||||||||||||||||| |||||||||||||||||||||| ||||||||||||||||||||||| |||||||||||||||||||||||| ||||||||||||||||||||||||| |||||||||||||||||||||||||||| ||||||||||||||||||||||||||||| |||||||||||||||||||||||||||||||| ||||||||||||||||||||||||||||||||||| |||||||||||||||||||||||||||||||||||| |||||||||||||||||||||||||||||||||| ||||||||||||||||||||||||| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−
20 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Cluster detectionSpatial relative risk
Kernel intensity estimates, infected vs. infested households
−71.592 −71.590 −71.588
−16.441
−16.439
−16.437
−16.435
Longitude
Latitude
O OOOOOOOO
OOOO
OOOOOOOOOOOO
O
OOOOOOOOOOOOOOOOOOOOO
OOOOO
OOOO
OOOOOOO
OOOOOOOOO
Infected sites
−71.592 −71.590 −71.588
−16.441
−16.439
−16.437
−16.435
Longitude
Latitude
Infested not infected sites
21 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Cluster detectionSpatial relative risk
Ratio of kernel intensity estimates, infected vs. infested
−71.592 −71.591 −71.590 −71.589 −71.588 −71.587
−16.441
−16.440
−16.439
−16.438
−16.437
−16.436
−16.435
Longitude
Latitude
OOOOO OOOO
OOO
O
OOOOOOOO
O OO
O
O
OOOOOOOOOOOOO OOOO
O
OOOOO
OOO
OOOO
OO
OO OOO
O
OOOO
OOOO
Log relative risk surface: Infected
||||||||||||||||||||||| ||||||||||||||||||||||||||||||||||||||
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−
−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−
22 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Cluster detectionSpatial relative risk
Cluster conclusions
I Relative risk surface adds more geographical precision topatterns initially revealed by SaTScan.
I Large risk of infestation in the south.
I Within this some pockets of increased risk of infection.
I Area of lower risk missed by circular scan statistic, due to itsirregular shape.
I Identifies areas for future studies.
23 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Cluster detectionSpatial relative risk
Chagas conclusions
I Significant cluster of infested households, but no clusters ofinfected households (circular clusters).
I Relative risk surface also suggests area of low risk (bothinfestation and infection) in northeast.
I K functions suggest significant clustering of infected but notinfested households.
I Taken together, results reveal different aspects of theunderlying process.
I A single cluster does not define clustering, nor does clusteringimply a single cluster.
24 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Cluster detectionSpatial relative risk
Questions?
25 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Cluster detectionSpatial relative risk
Chagas conclusions
I Infestation: pockets of higher and lower relative risk, but levelof clustering not different between cases and controls.
I Infection: More clustered at small distances than infestation,but resulting clusters are smaller and more diffuse.
I Scale of clustering different between infestation and infection,and larger than typical range of individual vectors.
I Scale of clustering useful in targeted surveillance for humancases (Levy et al., 2007, PLoS NTD).
26 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Cluster detectionSpatial relative risk
Questions?
27 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Raccoon rabies
28 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
What is rabies?
I Virus in family of Lyssa (“frenzy”) virus.
I Behavioral impact on host.
I Reportable disease.
I Various strains associated with primary host (bat, dog, coyote,fox, skunk, and raccoon).
I Host cross-over, typically transmitted via bite/scratch.
I Most human infection from bat strains.
29 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Raccoon rabies
I Endemic in Florida and South Georgia.
I Translocation of rabid animal(s) to VA/WV border circa 1977.
I Wave-like spread since.
I Connecticut first appearance 1991-1996.
I Ohio 2005.
I Joint work with Leslie Real’s lab in Population Biology,Evolution, and Ecology (David Smith, Colin Russell, RomanBiek, Scott Duke-Sylvester).
30 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Barrier estimation: What do we want?
I Goal: Measure effect of landscape features, (e.g., mountainsand rivers) on the speed of raccoon rabies diffusion.
I Elevation, river or road presence significantly related toraccoon rabies counts (Recuenco et al. 2007) andtransmission time (Russell et al. 2004).
I Landscape features may serve as either barriers or gateways tothe spread of infectious disease.
I Find and visualize barriers: Do they align with certainlandscape features?
31 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Data: What do we have?
I Time in months to first reported raccoon rabies case in 428contiguous counties in the Eastern US (CDC).I 0 for origin county: Pendleton, WV.
I Mean elevation by county (USGS - Geographic NamesInformation System).
I Indicator for major river presence in county (ESRI data and ageographic information system (GIS)).
I Population density by county (US Census and ESRI).
I Distance between origin county and all counties.
32 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Data
33 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Wombling
I Joint work with David Wheeler (Wheeler and Waller, JABES,2008).
I Wombling: determine boundaries on a map by finding wherelocal spread (change) is slower than elsewhere (Womble, 1951Science).
I William H. Womble a bit of an elusive figure...
34 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
William H. Womble (?)
35 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Google search: W.H. Womble Professor Robert Stencel
36 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Which leads to...
37 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Are you weady to womble?
I Consider a set of potential boundaries and decide if each is a“real” boundary or not.
I Many algorithmic approaches both deterministic and “fuzzy”.
I Adopt a Bayesian hierarchical model for wombling (Lu andCarlin 2005).
I Bayesian approach provides a direct estimate of theprobability that a line segment between two adjacent areas isa barrier (fuzzy boundary) in contrast to algorithmic versionsbased on thresholds, etc.
38 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Bayesian areal wombling
I Model time to first reported raccoon rabies case Yi :
Yi |µi , τ ∼ N(µi , 1/τ)
whereµi = α + φi
is the expected value of time to first case per county.
I Spatial random effects follow a conditionally autoregressive(CAR) prior φ ∼ CAR(η) with a mean random effectdetermined by its neighboring values.
39 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Bayesian areal wombling
I Boundary likelihood value (BLV) assigned to each potentialboundary (here, edge between two counties), based ondifference in expected (modeled) time to first appearance.
∆ij = |µi − µj |
I Use MCMC to draw sample from posterior [∆ij |y ] based ondraws from posteriors [µi |y ] and [µj |y ].
I This assigns a posterior probability for each edge, then displayedges with with p(∆ij > c|y) for some threshold probability c .
40 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Wombling boundaries: p(∆ij > c |y)
41 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Linking to local covariates
I Bayesian areal wombling provides estimates of barriers butdoes not allow direct inference regarding the impact ofparticular landscape barriers on the evidence for barriers.
I We could expand our fixed effect α to X ′β to include localcovariates (e.g., elevation, boundary based on a river).
I However, what it if the effect of elevation or presence of rivervaries from place to place?
I Russell et al. (2003, PNAS) suggest that river effect dependson direction of movement of the wave (perpendicular? Slower.Parallel? Faster.)
42 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Spatially varying coefficients
I We consider a spatially varying coefficient model with CARpriors on the covariate effects β, i.e.,
Yi |µi , τ ∼ N(µi , 1/τ)
whereµi = X ′iβi + φi
I Spatial priors on elements of βi .
I More specifically, assign a multivariate CAR prior on the set ofβ (Banerjee et al. 2004).
43 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
MultiCAR details
I βi = (βi1, βi2, . . . , βip)′
I βi |(β(−i),1,β(−i),2, . . . ,β(−i),p) ∼ N(βi ,Ω/mi )where
βi = (βi1, βi2, . . . , βip)′
andβi1 =
∑k∈κi
βk1/mi
where κi = neighbor set for region i , and |κi | = mi .
I Ω ∼ Inverse-Wishart(ν, 0.02 · Ip×p).
44 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Including covariates
I Include effects of (mean) elevation, presence of a major river,and the natural log of the (human) population density.
I Best fitting (via DIC) model includes spatial variation in allthree (and intercept).
E [Yi ] = βi1 +βi2(mean elev) +βi3(river) +β4i (log(pop dens))
45 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
β1: int, β2: elev, β3: river, β4: log pop
46 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Findings/interpretations
I Map of posterior mean (MU): shows the overall wave orspread.
I Random intercept reveals local adjustments.
I River effect indicates increases in time until first appearanceacross Potomac and Susquehanna Rivers, decreases time forHudson River and others.
I Elevation is not difference in elevation so not directlyinforming on elevation gradients as barriers, simply elevationimpact on time until appearance.
47 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
SVC wombled boundaries: p(∆ij > c |y)
48 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Including covariates → better wombling?
49 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Raccoon rabies: What do we have so far?
I A Monte Carlo approach to assess whether observed dataseems consistent with our model.
I Spatial analysis of patterns of appearance, inference regardinglocal barriers to geographic spread.
I But where do the data come from? How accurate are they?
50 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Overall Conclusions
I Much to be done to link mathematical models to statisticalideas.
I Disease ecology offers great setting for exploration.
I Models of transmission, interaction, observation.
I Mathematical models can inform statistics, statistics caninform models.
I Room to move past “ad-hockery”.
I Linking landscape features in a more meaningful (inferential)and spatial way.
51 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
Closing
“... Nature’s dice are always loaded, ... in her heaps and rubbishare concealed sure and useful results.”Ralph Waldo Emerson, Nature.
52 / 54
OutlineDisease Ecology: What do we want to do?
Point Processes: Chagas Disease in PeruSpatial Regression: Raccoon Rabies
Statistical estimation of landscape barriersWomblingSpatially varying coefficients
References
I Smith et al. (2002) Predicting the spatial dynamics of rabiesepidemics on heterogeneous landscapes. PNAS 99,3668-3672.
I Waller et al. (2003) Monte Carlo assessments of fit forecological simulation models. Eco Mod 164, 49-63.
I Waller (2010) Bridging gaps between statistical andmathematical modeling in ecology. Ecology 91, 3500-3502.
I Wheeler and Waller (2008) Mountains, valleys, and rivers:The transmission of raccoon rabies over a heterogeneouslandscape. JABES 13, 388-406.
53 / 54