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Mapping Malaria with Gaussian Processes
Nick Golding
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part 1:
malaria
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≈ 450,000,000 cases &
≈ 1,000,000 deaths per year
caused by: Plasmodium falciparum
P. vivax
P. malariae
P. ovale
P. knowlesi
it is controllable and treatable
malaria
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open-access spatial info
for control & elimination
global malaria risk maps
estimates of clinical burden
blood disorder maps
mosquito maps & more
malaria atlas project
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malaria is spatially heterogeneous
maps can:
identify populations at risk
evaluate impact of interventions
objectively evaluate options for control
why maps?
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1930
why new maps?
1946
1957
1978 2004
1968
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previous maps:
poorly described evidence-base
poorly defined, subjective methods
no estimates of reliability
why new maps?
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Mapping P. falciparum malaria mapping endemicity
transmission limits
regional endemic status
biological masks
infection prevalence
P.f parasite rate surveys
formal statistical model (MBG)
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P.f. transmission limits
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P.f. prevalence survey data
22,212 surveys
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2
1
,0~
,GP~|
),(logit),(
),(,Bin~),(,|
N
CMf
fP
PNPNN iiiiiii
txtx
txtx
P.f. model
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Smith et al.(2007) Malaria Journal
prevalence is age-dependent
surveys of different ages not
equivalent
needed to standardise
(2-10 year-olds)
used a Bayesian sub-model
tweaks - age stratification
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co
va
ria
nce
temporal
covariance
temporal lag
12 months
tweaks - seasonality
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space-time covariance
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PyMC
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mapping endemicity
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new work: more temporal
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part 2:
all the other diseases
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expert panel
data triage
modelling
Genbank
occurrence data
risk map
abraid
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modelling
data on many diseases
many risk maps
abraid
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fitting accurate models with
poor data
expressing uncertainty
‘one size fits all’ model
challenges
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Bhatt et al. (2013)
Nature
sparse, presence-only data
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classification (species present/absent)
function of environmental space
(fundamental niche)
require pseudo-absence data
well studied, widely used in ecology
species distribution modelling
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assumes fundamental niche is key
- communicable diseases?
assumes equilibrium
- emerging diseases?
discards spatial info
- disease control?
problems with SDM
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dengue
Bhatt et al. (2013) Nature
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Elith et al. (2008)
boosted regression trees
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Gaussian process SDM
GP classification
outperforms best
SDMs
GP BRT truth
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add prior knowledge (mean function)
marginalise uncertainty from polygon
occurrences
efficient (approximations)
extendable (spatial models ++)
advantages of GPs
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human population predicted movement
movement models
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environmental spatial
movement all
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correlation between diseases or control measures
convolved GPs
biotic interactions
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map.ox.ac.uk seeg.zoo.ox.ac.uk
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GP captures environmental
drivers
these are not isotropic
deformation of sphere
inclination angle
axis ratio
tweaks - anisotropy