title: Geostatistical modeling of malaria endemicity using serological indicators of exposure collected through school surveys.
abstract: Ethiopia has a diverse ecology and geography resulting in spatial and temporal variation in malaria transmission. Evidence-based strategies are thus needed to monitor transmission intensity and target interventions. A purposive selection of dried blood spots collected during cross-sectional school-based surveys in Oromia Regional State, Ethiopia, were tested for presence of antibodies against Plasmodium falciparum and P. vivax antigens. Spatially explicit binomial models of seroprevalence were created for each species using a Bayesian framework, and used to predict seroprevalence at 5 km resolution across Oromia. School seroprevalence showed a wider prevalence range than microscopy for both P. falciparum (0-50% versus 0-12.7%) and P. vivax (0-53.7% versus 0-4.5%), respectively. The P. falciparum model incorporated environmental predictors and spatial random effects, while P. vivax seroprevalence first-order trends were not adequately explained by environmental variables, and a spatial smoothing model was developed. This is the first demonstration of serological indicators being used to detect large-scale heterogeneity in malaria transmission using samples from cross-sectional school-based surveys. The findings support the incorporation of serological indicators into periodic large-scale surveillance such as Malaria Indicator Surveys, and with particular utility for low transmission and elimination settings.
authors.forename: Ruth A
authors.initials: RA
authors.lastname: Ashton
authors.forename: Takele
authors.initials: T
authors.lastname: Kefyalew
authors.forename: Alison
authors.initials: A
authors.lastname: Rand
authors.forename: Heven
authors.initials: H
authors.lastname: Sime
authors.forename: Ashenafi
authors.initials: A
authors.lastname: Assefa
authors.forename: Addis
authors.initials: A
authors.lastname: Mekasha
authors.forename: Wasihun
authors.initials: W
authors.lastname: Edosa
authors.forename: Gezahegn
authors.initials: G
authors.lastname: Tesfaye
authors.forename: Jorge
authors.initials: J
authors.lastname: Cano
authors.forename: Hiwot
authors.initials: H
authors.lastname: Teka
authors.forename: Richard
authors.initials: R
authors.lastname: Reithinger
authors.forename: Rachel L
authors.initials: RL
authors.lastname: Pullan
authors.forename: Chris J
authors.initials: CJ
authors.lastname: Drakeley
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Brooker
mesh_terms: D000293:Adolescent; D000913:Antibodies, Protozoan / Q000276:immunology*; D000953:Antigens, Protozoan / Q000276:immunology; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D019353:Endemic Diseases / Q000706:statistics & numerical data*; D004797:Enzyme-Linked Immunosorbent Assay; D005002:Ethiopia / Q000453:epidemiology; D006801:Humans; D016778:Malaria, Falciparum / Q000453:epidemiology* / Q000276:immunology; D016780:Malaria, Vivax / Q000453:epidemiology* / Q000276:immunology; D008297:Male; D008565:Membrane Proteins / Q000276:immunology; D020066:Merozoite Surface Protein 1 / Q000276:immunology; D015999:Multivariate Analysis; D010963:Plasmodium falciparum / Q000276:immunology* / Q000302:isolation & purification; D010966:Plasmodium vivax / Q000276:immunology* / Q000302:isolation & purification; D015800:Protozoan Proteins / Q000276:immunology; D012574:Schools; D016036:Seroepidemiologic Studies; D062206:Spatial Analysis
chemical_list: D000913:Antibodies, Protozoan; D000953:Antigens, Protozoan; D008565:Membrane Proteins; D020066:Merozoite Surface Protein 1; D015800:Protozoan Proteins; C090077:apical membrane antigen I, Plasmodium; C072492:glutamate-rich protein, Plasmodium