ArticlePloS one2020
Bayesian spatio-temporal modeling of malaria risk in Rwanda.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions.Journal of the Royal Society, Interface · 2021Pooled it
- Dual Epidemics in Toronto: Shared and Divergent Neighborhood Risks of COVID-19 and Opioid Mortality from a Joint Spatial and Temporal Analysis.Journal of urban health : bulletin of the New York Academy of Medicine · 2026Article
- Modeling policy decisions to mitigate the risk of emerging arboviral diseases under ecological changes in Uganda: Proposing a one Health in all policies approach.One health (Amsterdam, Netherlands) · 2026Article
- Drivers of autochthonous malaria cases over time: could the Central European present the African future?Malaria journal · 2024Article
- Modelling spatiotemporal variation in under-five malaria risk in Ghana in 2016-2021.Malaria journal · 2024Article
- Identifying Malaria Hotspots Regions in Ghana Using Bayesian Spatial and Spatiotemporal Models.Infectious diseases & immunity · 2024Article
- Bayesian spatio-temporal analysis of malaria prevalence in children between 2 and 10 years of age in Gabon.Malaria journal · 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
- Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania.Scientific reports · 2023Article
- Projecting malaria elimination in Thailand using Bayesian hierarchical spatiotemporal models.Scientific reports · 2023Article
- Spatio-temporal analysis of malaria incidence and its risk factors in North Namibia.Malaria journal · 2023Article
- Spatio-Temporal Bayesian Models for Malaria Risk Using Survey and Health Facility Routine Data in Rwanda.International journal of environmental research and public health · 2023Article
- Climate variability, socio-economic conditions and vulnerability to malaria infections in Mozambique 2016-2018: a spatial temporal analysis.Frontiers in public health · 2023Article
- The use of routine health facility data for micro-stratification of malaria risk in mainland Tanzania.Malaria journal · 2022Article
- Stratification of malaria incidence in Papua New Guinea (2011-2019): Contribution towards a sub-national control policy.PLOS global public health · 2022Article
- Article
- The secondary transmission pattern of COVID-19 based on contact tracing in Rwanda.BMJ global health · 2021Article
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3 authors.
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Abstract
Every year, 435,000 people worldwide die from Malaria, mainly in Africa and Asia. However, malaria is a curable and preventable disease. Most countries are developing malaria elimination plans to meet sustainable development goal three, target 3.3, which includes ending the epidemic of malaria by 2030. Rwanda, through the malaria strategic plan 2012-2018 set a target to reduce malaria incidence by 42% from 2012 to 2018. Assessing the health policy and taking a decision using the incidence rate approach is becoming more challenging. We are proposing suitable statistical methods that handle spatial structure and uncertainty on the relative risk that is relevant to National Malaria Control Program. We used a spatio-temporal model to estimate the excess probability for decision making at a small area on evaluating reduction of incidence. SIR and BYM models were developed using routine data from Health facilities for the period from 2012 to 2018 in Rwanda. The fitted model was used to generate relative risk (RR) estimates comparing the risk with the malaria risk in 2012, and to assess the probability of attaining the set target goal per area. The results showed an overall increase in malaria in 2013 to 2018 as compared to 2012. Ofall sectors in Rwanda, 47.36% failed to meet targeted reduction in incidence from 2012 to 2018. Our approach of using excess probability method to evaluate attainment of target or identifying threshold is a relevant statistical method, which will enable the Rwandan Government to sustain malaria control and monitor the effectiveness of targeted interventions.
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