Spatial modelling and the prediction of Loa loa risk: decision making under uncertainty.
Health decision-makers working in Africa often need to act for millions of people over large geographical areas on little and uncertain information. Spatial statistical modelling and Bayesian inference have now been used to quantify the uncertainty in the predictions of a regional, environmental risk map for Loa loa (a map that is currently being used as an essential decision tool by the African Programme for Onchocerciasis Control). The methodology allows the expression of the probability that, given the data, a particular location does or does not exceed a predefined high-risk threshold for which a change in strategy for the delivery of the antihelmintic ivermectin is required.
Diggle
P J
PJ
Department of Medicine, Lancaster University, Lancaster LA1 4YF, UK. p.diggle@lancaster.ac.uk
Thomson
M C
MC
Christensen
O F
OF
Rowlingson
B
B
Obsomer
V
V
Gardon
J
J
Wanji
S
S
Takougang
I
I
Enyong
P
P
Kamgno
J
J
Remme
J H
JH
Boussinesq
M
M
Molyneux
D H
DH
D000818:Animals; D002163:Cameroon / Q000453:epidemiology; D003657:Decision Making; D003661:Decision Support Techniques; D017753:Ecosystem; D019353:Endemic Diseases; D005369:Filaricides / Q000627:therapeutic use; D006801:Humans; D007303:Insect Vectors / Q000469:parasitology*; D008117:Loa / Q000302:isolation & purification*; D008118:Loiasis / Q000453:epidemiology* / Q000469:parasitology; D008377:Maps as Topic; D015995:Prevalence; D018570:Risk Assessment
D005369:Filaricides