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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15257

Title: Exploring predictive frameworks for malaria in Burundi
Authors: M simana, Lionel Divin
Nibayisabe, Emile
Niyukuria, David
Keywords: malaria
generalized linear model
neural network
Issue Date: Jul-2017
Publisher: Malaria in Burundi
Citation: Malaria in Burundi
Abstract: Malaria is a major public health concern in Burundi. The infection has been increasing in the last decade despite e orts to increase access to health services, and the deployment of several intervention programs. The use of di erent data sources can help to build predictive models of malaria cases in di erent sub-populations. We built predictive frameworks using generalized linear model (GLM), and arti cial neural network to predict malaria cases in four sub-populations (pregnant women and children under 5 years, pregnant women, children between 0 and 11 months, children between 12 and 59 months), and the overall general population. The results showed that almost half malaria infections are observed in pregnant women and children under 5 years, but children between 12 and 59 months carry the highest burden. Neural network model performed better in predicting total cases compared to GLM. But the latter provided information on the e ect of predictors, which is an important source of information to mainstream target interventions. Early prediction of cases can provide timely information needed to be proactive for intervention strategies, and it can help to mitigate the epidemics and reduce its impact on populations and the economy.
Description: This article is published by Malaria in Burundi and is also available at https://doi.org/10.1101/2021.07.20.21260697
URI: 10.1101/2021.07.20.21260697
Appears in Collections:College of Science

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