Spatio-temporal modeling and prediction of Malaria transmission in Amhara Region, Ethiopia

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dc.contributor.authorTeshager Zerihun
dc.date.accessioned2024-04-18T08:43:23Z
dc.date.available2024-04-18T08:43:23Z
dc.date.issued2024
dc.descriptionThe monthly malaria case incidence and environmental data were collected from Amhara Public Health Institute, NASA, CHIRPS and World Global Climate Databases. We have employed advanced statistical models such as parametric and nonparametric spatio-temporal trend models, Bayesian generalized Poisson model, Kulldorff’s retrospective space-time scan statistic, spatio-temporal generalized additive models, classification and regression training for spatio-temporal data (CAST), and Bayesian spatio-temporal predictive models.
dc.identifier.urihttps://rdmc.aphi.gov.et/handle/123456789/84
dc.language.isoen
dc.titleSpatio-temporal modeling and prediction of Malaria transmission in Amhara Region, Ethiopia
dspace.entity.type
local.access.levelAccessible upon reasonable request
local.contributor.emailteshagerzm@gmail.com
local.contributor.organizationAmhara Public Health Institute
local.contributor.phone+251-980540948
local.contributor.unitPublic Health Emergency Management Directorate
local.coverage.ageYes
local.coverage.geographicRegional
local.coverage.sexYes
local.criteria.exclusionSome town districts have general, referral, and specialized hospitals where patients may come from other districts for treatment, either by referral letters or not, that have their own weekly malaria surveillance report to the APHI. Aggregating the specialized hospital cases and the town district cases, where the higher hospitals located, might overestimate monthly malaria cases in the corresponding district. Hence, monthly malaria cases reported from the referral and specialized hospitals were excluded to improve the effects of over-estimation in malaria cases and incidence trends.
local.criteria.inclusionThe districts encompass various healthcare institutions such as health posts, health centers, primary hospitals, general, referral, and specialized hospitals.
local.data.qualityGood
local.datatypeRoutine/admin
local.date.dissemination2023-03-15
local.date.finalization2023-02-15
local.disseminatedbyBahir Dar University
local.formatEXCEL
local.has.geospatialNo
local.has.microdataYes
local.idAPHI-RDMC-023
local.keywordsBayesian approach; Climate variability; Generalized additive models; Malaria surveillance; Predictive model; Spatial risk; Spatio-temporal; Spatio-temporal clustering.
local.objectiveThis study aimed to examine spatio-temporal patterns and trends of malaria epidemic by accounting for climate variabilities.
local.samplingRetrospective study
local.study.populationAll malaria cases reported from 152 districts in a weekly basis from July 2012 to June 2020 G.C.
local.subject.areaHealth and Health related
local.toolsThe national weekly malaria report form

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