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Dynamic weighted ensemble for diarrhoea incidence predictions

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Abstract

Diarrhoea (DH) disease pose significant threats to national morbidity and mortality in Vietnam, especially on children. Being a climate sensitive disease, it has strong links to various meteorological factors like rainfalls or temperatures. Hence, together with global climate changes, the risk of diarrhoea has been increasing gradually while Vietnam is already a hotspot of diarrhoea worldwide. Thus, having an effective early warning system is becoming an urgent need. However, it has not been paid enough attention with very few research works, mainly focusing on quantilizing the relationships among various climate factors and diarrhoea incidences. Exploring more sophisticated machine learning techniques is therefore an interesting work towards more efficient and effective warning systems. This paper consists of two main contributions. First, many different state-of-the-art prediction models from traditional to most recent advantaged methods, e.g., SARIMA, SARIMAX, LSTM, CNN, Xgboost, SVM, LightGBM, Catboost, LightGBM, N-HiST, BlockRNN, TCN, TFT, or Transformer, are studied for predicting DH rates for a large number of locations (55 provinces) with different climates, geographics and socio-economy factors. It provides a useful view on the overall performances of different ML models on the prediction task, which is extremely useful for other researchers when developing early-warning systems for DH in other places. Second, we introduce a novel ensemble prediction model, called dynamic weighted ensemble (DWE), for further improving the DH prediction performance. DWE is a two layer ensemble approach. The first generates different meta models based on four base component models. The second layer employs a novel approach to predict the performances of all selected meta models and uses these predicted results to dynamically combine these models in a weighted scheme to produce final results. This is totally different to traditional ensemble approaches which only rely on fixed combinations of their components. To the best of our knowledge, DWE is also the first ensemble approach for diarrhoea prediction. Extensive experiments are conducted over all 55 provinces of Vietnam to demonstrate the performance of DWE and to reveal its important characteristics.

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Data availability

Available upon request.

Code availability

Available upon request.

Notes

  1. https://www.who.int/news-room/fact-sheets/detail/diarrhoeal-disease.

  2. The World Bank, Country Climate and Development Report for Vietnam. https://www.worldbank.org/en/country/vietnam/brief/key-highlights-country-climate-and-development-report-for-vietnam.

  3. Germanwatch, Global Climate Risk Index 2020. https://www.germanwatch.org/en/17307.

  4. United States Agency for International Development (USAID), Climate risk profile: Vietnam. https://www.climatelinks.org/countries/vietnam.

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Acknowledgements

This research is funded by Vietnam National University HoChiMinh City (VNU-HCM) under Grant Number DS2022-26-03.

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Authors and Affiliations

  1. Information Technology University, Ho Chi Minh City, Vietnam

    Thanh Duy Do & Thuan Dinh Nguyen

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Thanh Duy Do & Thuan Dinh Nguyen

  3. HMI Laboratory, Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam

    Viet Cuong Ta

  4. Laboratory of Environmental Sciences and Climate Change, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam

    Duong Tran Anh

  5. Faculty of Environment, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam

    Duong Tran Anh

  6. Environmental Health Department, Hanoi University of Publich Health, Hanoi, Vietnam

    Tuyet-Hanh Tran Thi

  7. Queen’s University Belfast, Belfast, UK

    Diep Phan & Son T. Mai

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Contributions

TDD, TDN, VCT, and STM develop core algorithms and perform experiments. THTT, DP and DTA perform data collection, preprocessing and perform experiments on some traditional models. TDN and STM supervise the project. All the authors participate on paper writing and project discussion.

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Correspondence to Thuan Dinh Nguyen.

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Editors: Dino Ienco, Robert Interdonato, Pascal Poncelet.

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Appendix

Appendix

Figure 17 shows monthly averaged DH rate and climate factors (from Jan to Dec) for all provinces. Over the whole country, the peak DH rates fall into Mar to September, when rainfall and temperature are both higher.

Fig. 17
figure 17

Geographical representation of monthly average Diarrhea rate and Climate factors from Jan (1) to Dec (12)

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Do, T.D., Nguyen, T.D., Ta, V.C. et al. Dynamic weighted ensemble for diarrhoea incidence predictions. Mach Learn 113, 2129–2152 (2024). https://doi.org/10.1007/s10994-023-06465-z

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