Abstract
Infectious diarrhea has high morbidity and mortality around the world. For this reason, diarrhea prediction has emerged as an important problem to prevent and control outbreaks. Numerous studies have built disease prediction models using large-scale data. However, these methods perform poorly on diarrhea data. To address this issue, this paper proposes a parsimonious model (PM), which takes historical outpatient visit counts, meteorological factors (MFs) and Baidu search indices (BSIs) as inputs to perform prediction. An experimental evaluation was done to compare the short-term prediction performance of ten algorithms for four groups of inputs, using data collected in Xiamen, China. Results show that the proposed method is effective in improving the prediction accuracy.
Highlights
To keep up with the pace of income growth, urbanization, and globalization, risk management of infectious diseases in public has become a critical task [1]
Numerous studies have used autoregression (AR), autoregressive integrated moving average model (ARIMA), and machine learning methods to predict upcoming values based on past observations. e widely used machine learning methods are multiple linear regression (MLR), support vector regression (SVR), and random forest regression (RFR) [6,7,8]
We propose a parsimonious model (PM). e proposed model first assigns a vector to each input dimension, and the vectors are connected to the target
Summary
To keep up with the pace of income growth, urbanization, and globalization, risk management of infectious diseases in public has become a critical task [1]. Infectious diarrhea (ID) [2] is one of the most common infectious diseases in the world, which infects more than 1 billion persons. A famous autoregression method is that of Box and Jenkins [9], which has been applied in many fields [10], such as for electricity load forecasting and stock price prediction. Another famous statistical method is spline interpolation [11], which learns and uses a cubic spline interpolation to predict future values. The performance of these methods degrades when dealing with nonstationary and chaotic time series, such as those of diarrhea outpatients
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