Predicting diarrhoea outbreaks with climate change

considered when predicting outbreak of diarrhoea in South Africa were precipitation, humid- ity, evaporation and temperature conditions. Conclusions Overall, experiments indicated that the prediction capacity of our DL methods (Convolu- tional Neural Networks) was found to be superior (with statistical significance) in terms of prediction accuracy across most provinces. This study’s results have important implications for the development of automated early warning systems for diarrhoea (and related disease) outbreaks across the globe. Introduction Diarrhoea is a major health concern and has remained among the top leading cause of global morbidity and mortality amongst all ages [1, 2]. Annually, over 2.5 million deaths attributed to diarrhoea is recorded worldwide [3]. The World Health Organization reported that the Sub- Saharan Africa (SSA) and South Asia regions account for more than 80 percent of total world records [1, 3]. Over the SSA region, South Africa is one of the most affected countries. In 2010 and 2015, diarrhoea was reported to be among the top ten leading causes of years of life lost among South African residents [4]. Diarrhoea also accounts for three percent of the total death records in individual of all ages in the country [5]. Some studies such as [6, 7] have shown that diarrhoea infections in South Africa are attributed to nosocomial infections or community acquired resulting from contaminated food and water caused by a range of pathogens. How- ever, studies by [8, 9] reported that climate factors and weather variability influence the level of

abundance and seasonality of the pathogens present in the environment, thus the prevalence of diarrhoea can be linked to extremities from weather events. South Africa is a region that experiences significant temperature and precipitation anomaly, which are factors that play a vital role in the long-term trends of diarrhoea [10, 11]. For exam- ple, in Western Cape province of South Africa, the rate of diarrhoea hospitalizations was strongly linked to increase in minimum and maximum temperature [7]. A study in Limpopo province showed that seasons when precipitation rate was below normal coincides with a high number of diarrhoea cases [9]. Thus, the development of a model with the ability to capture complex relationships and long-term dependencies between climate factors and diarrhoea may be effective for diarrhoea predictive analysis. A diarrhoea predictive model could be used for public health surveillance as it will offer timely detection and prompt notification for the control of diarrhoea outbreak. Several studies have developed models for investigating diarrhoea outbreak in various com- munities. A vast majority were developed with statistical models such as Auto-regressive Inte- grated Moving Average Model (ARIMA) [12], Poisson Regression [7], Auto-regressive Analysis of Covariance Model (ANCOVA) [13] and Time-series Log Linear Regression [8]. For instance, a study by [12] used the influence of climate variables to develop an ARIMA model that predicts the daily incidence of diarrhoea in Beijing. The Poisson Regression model was

also used by [7] to assess the relationship between diarrhoea cases and temperature variability in South Africa. Although these studies have proven useful, other studies such as [14, 15] have shown that traditional statistical models and frameworks are often limited for the analysis of high dimensional, imbalanced, and non-linear data. In addition, these studies [14, 15] reported that the limitations of statistical models can be addressed using Machine Learning (ML) PLOS ONE Predicting diarrhoea outbreaks with climate change PLOS ONE | https://doi.org/10.1371/journal.pone.0262008 April 19, 2022 2 / 18 Africa (https://www.clicksgroup.co.za/) or the Council for Scientific and Industrial Research (CSIR) (https://www.csir.co.za/). Furthermore, I confirm that others would be able to access these data by request and permission from the Clicks Group Limited, South Africa. I also confirm that no special privileges were involved when the data was secured. All other datasets (real-world climate data and synthetic datasets for all variables including diarrhoea) used in this study can be accessed from our GitHub repository https://github.com/ aminalawal/Predicting-Diarrhoea-Outbreak-with- Climate-Change. Funding: We would like to note that this study was supported by the South African National Research Foundation (NRF): 403 Human and Social Dynamics in Development Grant (Grant No. 118557). Competing interests: The authors have declared that no competing interests exist.