Predicting diarrhoea outbreaks with climate change

Sensitivity analysis Our parameter sensitivity analysis (Experiments setup section) demonstrated that the predic- tion of diarrhoea outbreak by the given ML methods is influenced by specific climate factors. The most prominent (influential) factors are precipitation, humidity, evaporation and tempera- ture, although their levels of influence differ across South African provinces. Our findings are in agreement with studies such as [7, 8] that have shown that diarrhoea cases increase for every 1˚C increase in temperature. In addition, related work by [42] reported that evaporation rate is strongly linked to high temperature. Since increase in diarrhoea cases have been associated with high temperature, perhaps diarrhoea can also be linked to evaporation rate. Other studies [9, 15] have also demonstrated that precipitation rate and humidity are strongly related to reported increases in diarrhoea-related hospitalizations. Study contributions A key contribution of this research is the first comprehensive study and application of perti- nent ML methods to real-world health-care data sourced from various South African medical institutions in order to formalise an effective predictive machine learning methodology for Sub-Saharan Africa (currently, one of the most adversely affected areas, globally, by diarrhoea outbreaks [1, 3]). A second key contribution of this research is the use of evolutionary optimi- sation for automating parameter tuning for a given ML method and associated training data-

set, as well as demonstration of data augmentation techniques, such as use of generative mod- els to generate artificial data [24, 25] to complement training data deficiencies. While our study has demonstrated that ML can be used for diarrhoea outbreak prediction with climate factors. The results can be improved in some ways. For example, taking other human and environmental factors that cause the spread of infectious diseases into consider- ation may improve the accuracy of future diarrhoea prediction models. Given the different strength of each ML algorithm, developing a hybrid method that combines the advantage and benefits of at least two ML algorithms may result in a methodology that yields consistently high predictive task performance regardless of the conditions set in an experiment. Conclusion The global burden of diarrhoea is a major public health problem that causes both personal and widespread harm. This study ascertained the applicability of various Machine Learning (ML) methods in the development of automated early warning system for predicting the outbreak of diarrhoea in South Africa given specific climate variables. We compared the predictive task performance of various ML methods, including Support Vector Machines, Long-Short Term Memory Neural Networks (LSTM) and Convolutional Neural Networks (CNNs), for predicting daily diarrhoea cases over nine South African provinces. Prediction comparisons were with respect to a specific set of climate variables and varying proportional combinations of real-

world and synthetic (data augmentation) training and testing data. Results indicated that over- all (for all real-world data-sets), our CNN yielded the highest accuracy predictions supporting the well established predictive capacity and efficacy of deep-learning systems. However, given synthetic training and testing data-augmentation, our LSTM yielded the most accuracy predic- tions overall. This also study elucidated that the climate variables: precipitation, humidity, evaporation, and temperature, yielded the greatest impact on daily diarrhoea cases across South Africa, and were thus the data-set variables integral to the predictive success of our tested methods. Thus, a key contribution of this study is the guidance it provides researchers in selecting a suitable ML method for disease outbreak prediction (diarrhoea case prediction in this study), given real-world and augmented training and testing data-sets containing PLOS ONE Predicting diarrhoea outbreaks with climate change PLOS ONE | https://doi.org/10.1371/journal.pone.0262008 April 19, 2022 15 / 18