Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania.

ABSTRACT: As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017-2019 to predict risks at the ward level, the lowest decision-making unit in mainland

SUBMITTER: Thawer SG 

PROVIDER: S-EPMC10313820 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature