ArticleMalaria journal2022
Bayesian spatio-temporal modelling and mapping of malaria and anaemia among children between 0 and 59 months in Nigeria.
Article in Malaria journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Where and why malaria persists in heterogeneous transmission settings: insights from routine health facility data.BMJ global health · 2026Article
- Co-occurrence of anaemia and malaria among children under five in Uganda: A joint modeling analysis.PLOS global public health · 2026Article
- Integrating vulnerability and hazard in malaria risk mapping: the elimination context of Senegal.BMC infectious diseases · 2025Article
- Malaria hotspots and risk factors among children under-five years of age across eight West African countries: A geospatial analysis of DHS data.MalariaWorld journal · 2025Article
- Interactive and lag effects of environmental factors on the density of schistosome-transmitting Oncomelania hupensis: A twelve-year monthly repeated survey.Parasitology research · 2024Article
- Optimizing spatio-temporal correlation structures for modeling food security in Africa: a simulation-based investigation.BMC bioinformatics · 2024Article
- Identifying Malaria Hotspots Regions in Ghana Using Bayesian Spatial and Spatiotemporal Models.Infectious diseases & immunity · 2024Article
- Modelling spatiotemporal patterns of visceral leishmaniasis incidence in two endemic states in India using environment, bioclimatic and demographic data, 2013-2022.PLoS neglected tropical diseases · 2024Article
- Three Gorges Dam: Potential differential drivers and trend in the spatio-temporal evolution of the change in snail density based on a Bayesian spatial-temporal model and 5-year longitudinal study.Parasites & vectors · 2023Article
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3 authors.
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Abstract
BACKGROUND/M&M: A vital aspect of disease management and policy making lies in the understanding of the universal distribution of diseases. Nevertheless, due to differences all-over host groups and space-time outbreak activities, data are subject to intricacies. Herein, Bayesian spatio-temporal models were proposed to model and map malaria and anaemia risk ratio in space and time as well as to ascertain risk factors related to these diseases and the most endemic states in Nigeria. Parameter estimation was performed by employing the R-integrated nested Laplace approximation (INLA) package and Deviance Information Criteria were applied to select the best model.
resultsIn malaria, model 7 which basically suggests that previous trend of an event cannot account for future trend i.e., Interaction with one random time effect (random walk) has the least deviance. On the other hand, model 6 assumes that previous event can be used to predict future event i.e., (Interaction with one random time effect (ar1)) gave the least deviance in anaemia. DISCUSSION: For malaria and anaemia, models 7 and 6 were selected to model and map these diseases in Nigeria, because these models have the capacity to receive strength from adjacent states, in a manner that neighbouring states have the same risk. Changes in risk and clustering with a high record of these diseases among states in Nigeria was observed. However, despite these changes, the total risk of malaria and anaemia for 2010 and 2015 was unaffected.
conclusionNotwithstanding the methods applied, this study will be valuable to the advancement of a spatio-temporal approach for analyzing malaria and anaemia risk in Nigeria.
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