ArticleBMJ open2022
Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study.
Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- The role of artificial intelligence (AI) in the elimination of malaria: a systematic literature review.Malaria journal · 2025Pooled it
- Predicting the risk of malaria re-introduction in countries certified malaria-free: a systematic review.Malaria journal · 2023Pooled it
- Integrating machine learning and spatial clustering for malaria case prediction in Brazil's Legal Amazon.BMC infectious diseases · 2025Article
- Hybrid models combining trend and seasonality components with machine learning algorithms provide accurate forecasting of malaria incidence.PLOS global public health · 2025Article
- Advancing Early Warning Systems for Malaria: Progress, challenges, and future directions - A scoping review.PLOS global public health · 2025Article
- Influence of environmental, geographic, socio-demographic, and epidemiological factors on presence of malaria at the community level in two continents.Scientific reports · 2024Article
- Data-driven nexus between malaria incidence and World Bank indicators in the Mekong River during 2000-2022.PLOS global public health · 2024Article
- Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model.Scientific reports · 2023Article
- Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data.Scientific reports · 2023Article
- Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China.PLoS neglected tropical diseases · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
objectivesMalaria is a vector-borne disease that remains a serious public health problem due to its climatic sensitivity. Accurate prediction of malaria re-emergence is very important in taking corresponding effective measures. This study aims to investigate the impact of climatic factors on the re-emergence of malaria in mainland China.
designA modelling study. SETTING AND
participantsMonthly malaria cases for four PRIMARY AND SECONDARY OUTCOME MEASURES: The long short-term memory sequence-to-sequence (LSTMSeq2Seq) deep neural network model was used to predict the re-emergence of malaria cases from 2004 to 2016, based on the influence of climatic factors. We trained and tested the extreme gradient boosting (XGBoost), gated recurrent unit, LSTM, LSTMSeq2Seq models using monthly malaria cases and corresponding meteorological data in 31 provinces of China. Then we compared the predictive performance of models using root mean squared error (RMSE) and mean absolute error evaluation measures.
resultsThe proposed LSTMSeq2Seq model reduced the mean RMSE of the predictions by 19.05% to 33.93%, 18.4% to 33.59%, 17.6% to 26.67% and 13.28% to 21.34%, for
conclusionsThe LSTMSeq2Seq model significantly improved the prediction of malaria re-emergence based on the influence of climatic factors. Therefore, the LSTMSeq2Seq model can be effectively applied in the malaria re-emergence prediction.
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