{'fraction_non_alphanumeric': 0.021900664841611264, 'fraction_numerical': 0.005084082909659757, 'mean_word_length': 5.047281323877068, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Temporal Convolutional Recurrent Neural Network Model for Epidemic Forecasting Using Multi-Dimensional Data: A new method (Preprint)


 BACKGROUND
 Infectious diseases such as COVID-19, influenza, Malaria, and Dengue have caused a significant threat throughout the world. For example, the expected yearly cost of pandemic influenza at roughly $500 billion, while COVID-19 has diminished the economic activity and could potentially lead to structural shifts in the global economy. One of the underlying major problems regarding these traditional surveillance epidemic methods is that they are not always effective and also the results produced by these methods usually have a delay of several weeks.
 
 
 OBJECTIVE
 The purpose of this study is to develop an epidemic forecasting model utilizing the deep learning technology that can be adapted to epidemic datasets and can predict the incidence or number of infectious diseases more accurately than traditional epidemic prediction methods.
 
 
 METHODS
 To predict the incidence of the epidemic, in this study, we collect real-world infectious disease data and transformed the dataset into time series. Our method uses the following information as inputs : (1) environmental and climatic information (2) epidemic–related internet search activity, (3) Google Trends, and (4) CDC ILI, Dengue Fever, Measles Incidence Data and related historical data. The proposed deep learning method utilizes a temporal convolutional technique that enables the exploitation of complex temporal patterns of epidemic activity across historical observations series. In the proposed deep learning model, we use the long and short-term memory units in a recurrent neural network to learn the temporal pattern of historical data.
 
 
 RESULTS
 We compare our model with three state-of-the-art deep learning models to evaluate the performance, accuracy, and relevance of the model predictions. We input the epidemic incidence data and observation data of the past 12 weeks to predict the number of incidence of the next one, two, and three weeks, respectively. We evaluate these models on the four real-world data sets we collected. The experiments demonstrate that our proposed model is better than several other models.
 
 
 CONCLUSIONS
 Previous studies often use autoregressive models or traditional machine learning methods to predict future epidemics. Compared to these, the performance evaluation of our method shows that our proposed method is superior to traditional non-machine methods and basic neural network models.
