Nick Golding

Challenges and opportunities of large-

scale automated disease risk mapping

expert panel

data triage

modelling

Genbank

occurrence data

risk map

modelling

data on many diseases

many risk maps

challenges

accurate models with poor data expressing uncertainty ‘one size fits all’ model

share information between diseases find general rules where to sample next? develop new methods

opportunities

Bhatt et al. (2013) Nature

sparse, presence-only data

species distribution modelling

function of environment (fundamental niche) require pseudo-absence data

problems with PO-SDM

assumes fundamental niche is key - communicable diseases? assumes equilibrium - emerging diseases? discards spatial info - disease control?

Gaussian process SDM

add prior knowledge (mean function) marginalise uncertainty from polygon occurrences efficient (Laplace approximation) extendable (spatial models ++)

advantages

compositional kernels

Duvenaud et al. (2013) arXiv

movement models

movement models

movement models

human population predicted movement

movement models

environmental spatial

movement all

biotic interactions

interactions between diseases (e.g. LF / malaria?) or with control measures convolved GPs

polygon occurrence data

prevent regression dilution