Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model.

ABSTRACT: Climatic factors influence malaria transmission via the effect on the Anopheles vector and Plasmodium parasite. Modelling and understanding the complex effects that climate has on malaria incidence can enable important early warning capabilities. Deep learning applications across fields are proving valuable, however the field of epidemiological forecasting is still in its infancy with a lack of applied deep learning studies for malaria in southern Africa which leverage quality datasets. Using a novel high resolution malaria incidence dataset containing 23 years of daily data from 1998 to 2021, a statistical model and XGBOOST machine learning model were compared to a deep learning Transformer model by assessing the accuracy of their numerical predictions. A novel loss function, used to acco

SUBMITTER: Pillay MT 

PROVIDER: S-EPMC10754862 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature