Predicting malaria dynamics in Burundi using deep Learning Models

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ID: 283603
2023
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Abstract
Malaria continues to be a major public health problem on the African continent, particularly in Sub-Saharan Africa. Nonetheless, efforts are ongoing, and significant progress has been made. In Burundi, malaria is among the main public health concerns. In the literature, there are limited prediction models for Burundi. We know that such tools are much needed for interventions design. In our study, we built machine-learning based models to estimates malaria cases in Burundi. The forecast of malaria cases was carried out at province level and national scale as well. Long short term memory (LSTM) model, a type of deep learning model has been used to achieve best results using climate-change related factors such as temperature, rainfal, and relative humidity, together with malaria historical data and human population. With this model, the results showed that at country level different tuning of parameters can be used in order to determine the minimum and maximum expected malaria cases. The univariate version of that model (LSTM) which learns from previous dynamics of malaria cases give more precise estimates at province-level, but both models have same trends overall at provnce-level and country-level
Reference Key
niyukuri2023predicting Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Daxelle Sakubu; Kelly Joelle Gatore Sinigirira; David Niyukuri
Journal arXiv
Year 2023
DOI
DOI not found
URL
http://arxiv.org/abs/2306.02685v2
Keywords
cs.lg cs.ai

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