Predicting diarrhoea outbreaks with climate change

RESEARCH ARTICLE Predicting diarrhoea outbreaks with climate change Tassallah AbdullahiID1*, Geoff Nitschke1, Neville Sweijd2 1 Department of Computer Science, University of Cape Town, Cape Town, Western Cape, South Africa, 2 Applied Centre for Climate and Earth Systems Science, Council for Scientific and Industrial Research, Cape Town, South Africa * abdtas008@myuct.ac.za Abstract Background Climate change is expected to exacerbate diarrhoea outbreaks across the developing world, most notably in Sub-Saharan countries such as South Africa. In South Africa, dis- eases related to diarrhoea outbreak is a leading cause of morbidity and mortality. In this study, we modelled the impacts of climate change on diarrhoea with various machine learn- ing (ML) methods to predict daily outbreak of diarrhoea cases in nine South African provinces. Methods We applied two deep Learning DL techniques, Convolutional Neural Networks (CNNs) and Long-Short term Memory Networks (LSTMs); and a Support Vector Machine (SVM) to pre- dict daily diarrhoea cases over the different South African provinces by incorporating climate information. Generative Adversarial Networks (GANs) was used to generate synthetic data which was used to augment the available data-set. Furthermore, Relevance Estimation and Value Calibration (REVAC) was used to tune the parameters of the ML methods to optimize the accuracy of their predictions. Sensitivity analysis was also performed to investigate the contribution of the different climate factors to the diarrhoea prediction method. Results

Our results showed that all three ML methods were appropriate for predicting daily diarrhoea cases with respect to the selected climate variables in each South African province. How- ever, the level of accuracy for each method varied across different experiments, with the deep learning methods outperforming the SVM method. Among the deep learning tech- niques, the CNN method performed best when only real-world data-set was used, while the LSTM method outperformed the other methods when the real-world data-set was aug- mented with synthetic data. Across the provinces, the accuracy of all three ML methods improved by at least 30 percent when data augmentation was implemented. In addition, REVAC improved the accuracy of the CNN method by about 2.5% in each province. Our parameter sensitivity analysis revealed that the most influential climate variables to be PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.0262008 April 19, 2022 1 / 18 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Abdullahi T, Nitschke G, Sweijd N (2022) Predicting diarrhoea outbreaks with climate change. PLoS ONE 17(4): e0262008. https://doi. org/10.1371/journal.pone.0262008 Editor: Jie Zhang, Newcastle University, UNITED KINGDOM Received: March 8, 2021 Accepted: December 15, 2021 Published: April 19, 2022 Copyright: © 2022 Abdullahi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original

author and source are credited. Data Availability Statement: The real-world diarrhoea case data used in this study contain protected health information and cannot be published for reasons of data protection. The real- world diarrhoea data used in this study are available from Clicks Group Limited, South Africa but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. The data are however available from Dr. Neville Sweijd (nsweijd@access.ac.za) for Council for Scientific Research (CSIR) or jacqui.duminy@clicksgroup. co.za for Clickgroup.co.za., and after receipt of permission with the Clicks Group Limited, South