@article{fdi:010040793,
  title = {{S}patial modelling and the prediction of {L}oa loa risk : {D}ecision making under uncertainty},
  author = {{D}iggle, {P}. {J}. and {T}homson, {M}. {C}. and {C}hristensen, {O}. {F}. and {R}owlingson, {B}. and {O}bsomer, {V}. and {G}ardon, {J}acques and {W}anji, {S}. and {T}akougang, {I}. and {E}nyong, {P}. and {K}amgno, {J}. and {R}emme, {J}. {H}. and {B}oussinesq, {M}ichel and {M}olyneux, {D}. {H}.},
  editor = {},
  language = {{ENG}},
  abstract = {{H}ealth decision-makers working in {A}frica often need to act for millions of people over large geographical areas on little and uncertain information. {S}patial statistical modelling and {B}ayesian inference have now been used to quantify the uncertainty in the predictions of a regional, environmental risk map for {L}oa loa ( a map that is currently being used as an essential decision tool by the {A}frican {P}rogramme for {O}nchocerciasis {C}ontrol). {T}he 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.},
  keywords = {},
  booktitle = {},
  journal = {{A}nnals of {T}ropical {M}edicine and {P}arasitology},
  volume = {101},
  numero = {6},
  pages = {499--509},
  ISSN = {0003-4983},
  year = {2007},
  DOI = {10.1179/136485907{X}229121},
  URL = {https://www.documentation.ird.fr/hor/fdi:010040793},
}