Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

Abstract

Background: Cholera as an endemic disease remains a health issue in Iran despite decrease in incidence. Since forecasting epidemic diseases provides appropriate preventive actions in disease spread, different forecasting methods including artificial neural networks have been developed to study parameters involved in incidence and spread of epidemic diseases such as cholera. Objectives: In this study, cholera in rural area of Chabahar, Iran was investigated to achieve a proper forecasting model. Materials and Methods: Data of cholera was gathered from 465 villages, of which 104 reported cholera during ten years period of study. Logistic regression modeling and correlate bivariate were used to determine risk factors and achieve possible predictive model onehidden-layer perception neural network with backpropagation training algorithm and the sigmoid activation function was trained and tested between the two groups of infected and non-infected villages after preprocessing. For determining validity of prediction, the ROC diagram was used. The study variables included climate conditions and geographical parameters. Results: After determining significant variables of cholera incidence, the described artificial neural network model was capable of forecasting cholera event among villages of test group with accuracy up to 80%. The highest accuracy was achieved when model was trained with variables that were significant in statistical analysis describing that the two methods confirm the result of each other. Conclusions: Application of artificial neural networking assists forecasting cholera for adopting protective measures. For a more accurate prediction, comprehensive information is required including data on hygienic, social and demographic parameters.

The effect of climate change on cholera disease: The road ahead using artificial neural network

“…In the case of precipitation, other studies found that heavy rainfall as an important parameter on cholera incidence can lead to flooding and affect water quality and sanitation systems [41, 42]. Although, the effect of low precipitation in increasing the risk of cholera disease described in this study, was supported by recent findings in Bangladesh and Iran [12, 21].…”

“…The optimized NN has one hidden layer and Tan Axon and Momentum were set as layer’s transfer function and learning rule, respectively. Different studies applied ANNs to predict the diarrheal and cholera outbreaks in distinct areas of the world [21, 22, 50, 51]. Pezeshki et al used a multilayer perception ANNs to create a model and predict cholera disease in Chabahar, Iran.…”

“…Pezeshki et al used a multilayer perception ANNs to create a model and predict cholera disease in Chabahar, Iran. They applied monthly average of temperature, humidity and rainfall as climatic variables and their results illustrated that cholera cases were significantly related to humidity and there was a significant relationship between the cholera incidence and lack of rainfall [21]. Considering the importance of controlling the infectious diseases, many studies were done to estimate the effect of climate changes on infectious diseases in human societies in the future and prepare helpful prediction tools [52].…”

“…Considering extensive changes in global and local climate conditions in last decades and its impact on the spread of different diseases, it is a great necessity to find proper algorithms to predict the vastness of these diseases based on climate conditions. In this regards, ANNs are known as one of the appropriate tools for climate modeling and weather prediction [21]. In ANNs models, the sensitivity analysis was carried out for enhancing network performance and determination of the relative importance and contribution of input parameters on the output [55].…”

“…Artificial neural networks (ANNs), as nonlinear statistical modeling, can be applied for creating models of disease spreading and prediction of epidemic outcomes. Moreover, ANNs can gain elucidation of data and nonlinear forecast analysis of variables for evaluation of biological and environmental data [21]. Several studies applied ANNs to forecast the incidence of cholera over an area and illustrated advantages and disadvantages of this method [21, 22].…”