Regional level influenza study based on Twitter and machine learning method

Xue, Hongxin; Bai, Yanping; Hu, Hongping; Liang, Haijian

doi:10.1371/journal.pone.0215600

Cited by 20 publications

(7 citation statements)

References 35 publications

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“…First, we need to check whether our approach works well for novel data sets other than news articles. Recently, influenza prediction has been studied using various data [38,[50][51][52][53]. Therefore, it is necessary to study whether our approach can improve performance when applied to different data sets used in the recent state-of-the-art studies.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study

Jang¹,

Kim²,

Kim³

2021

JMIR Med Inform

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Background Each year, influenza affects 3 to 5 million people and causes 290,000 to 650,000 fatalities worldwide. To reduce the fatalities caused by influenza, several countries have established influenza surveillance systems to collect early warning data. However, proper and timely warnings are hindered by a 1- to 2-week delay between the actual disease outbreaks and the publication of surveillance data. To address the issue, novel methods for influenza surveillance and prediction using real-time internet data (such as search queries, microblogging, and news) have been proposed. Some of the currently popular approaches extract online data and use machine learning to predict influenza occurrences in a classification mode. However, many of these methods extract training data subjectively, and it is difficult to capture the latent characteristics of the data correctly. There is a critical need to devise new approaches that focus on extracting training data by reflecting the latent characteristics of the data. Objective In this paper, we propose an effective method to extract training data in a manner that reflects the hidden features and improves the performance by filtering and selecting only the keywords related to influenza before the prediction. Methods Although word embedding provides a distributed representation of words by encoding the hidden relationships between various tokens, we enhanced the word embeddings by selecting keywords related to the influenza outbreak and sorting the extracted keywords using the Pearson correlation coefficient in order to solely keep the tokens with high correlation with the actual influenza outbreak. The keyword extraction process was followed by a predictive model based on long short-term memory that predicts the influenza outbreak. To assess the performance of the proposed predictive model, we used and compared a variety of word embedding techniques. Results Word embedding without our proposed sorting process showed 0.8705 prediction accuracy when 50.2 keywords were selected on average. Conversely, word embedding using our proposed sorting process showed 0.8868 prediction accuracy and an improvement in prediction accuracy of 12.6%, although smaller amounts of training data were selected, with only 20.6 keywords on average. Conclusions The sorting stage empowers the embedding process, which improves the feature extraction process because it acts as a knowledge base for the prediction component. The model outperformed other current approaches that use flat extraction before prediction.

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Section: Limitations and Future Workmentioning

confidence: 99%

Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study

Jang¹,

Kim²,

Kim³

2021

JMIR Med Inform

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“…Hongxin Xue et al [11] proposed three flu prediction models, to check and verify the cause of spread of flue. The models were based on twitter and US Centers for Disease Control's (CDC's) Influenza-Like Illness (ILI) data.…”

Section: Literature Surveymentioning

confidence: 99%

Influenza Prediction: Analyzing Machine Learning Algorithms

Yadav

Agarwal

2020

AJCST

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Analyzing online or digital data for detecting epidemics is one of the hot areas of research and now becomes more relevant during the present outbreak of Covid-19. There are several different types of the influenza virus and moreover they keep evolving constantly in the same manner the COVID-19 virus has done. As a result, they pose a greater challenge when it comes to analyzing them, predicting when, where and at what degree of severity it will outbreak during the flu season across the world. There is need for greater surveillance to both seasonal and pandemic influenza to ensure the health and safety of the mankind. The objective of work is to apply machine learning algorithms for building predictive models that can predict where the occurrence, peak and severity of influenza in each season. For this work we have considered a freely available dataset of Ireland which is recorded for the duration of 2005 to 2016. Specifically, we have tested three ML Algorithms namely Linear Regression, Support Vector Regression and Random Forests. We found Random Forests is giving better predictive results. We also conducted experiment through weka tool and tested Zero R, Linear Regression, Lazy Kstar, Random Forest, REP Tree, Multilayer Perceptron models. We again found the Random Forest is performing better in comparison to all other models. We also evaluated other regression models including Ridge Regression, modified Ridge regression, Lasso Regression, K Neighbor Regression and evaluated the mean absolute errors. We found that modified Ridge regression is producing minimum error. The proposed work is inclined towards finding the suitability & appropriate ML algorithm for solving this problem on Flu.

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“…On the one hand, researchers are actively exploring effective treatment against 2019-nCov; on the other hand, they are discussing how to predict the development trend of the novel pneumonia in the future and the turning point. Existing predictions of epidemic transmitted diseases can be roughly divided into: Prediction by search engine [2] [3], SNS (social networking services) [4] [5] and the data of disease prevention and control center [6] and sentinel hospitals. Because of its abundant data and its characteristics of strong periodicity and seasonal outbreaks, most of the existing studies are aimed at the predication of influenza outbreaks, which are divided into two categories: (1) nowcasting.…”

Section: Introductionmentioning

confidence: 99%

Can Search Query Forecast successfully in China’s novel coronavirus (2019-nCov) pneumonia?

2020

Preprint

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Recently the novel coronavirus (2019-nCov) pneumonia outbreak in China then the world, and the Number of infections and death continues to increases. Search Query performs well in forecasting the epidemics. It is still a question whether search engine data can forecast the drift and the inflexion in 2019-nCov pneumonia. Based on the Baidu Search Index, we propose three prediction models: composite Index, composite Index with filtering and suspected NCP(Novel Coronavirus Pneumonia). The result demonstrates that the predictive model of composite index with filtering performs the best while the model of suspected NCP has the highest forecast error. We further predict the out-of-the-set NCP confirmed cases and monitor that the next peak of new diagnoses will occur on February 16 th and 17 th .

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Regional level influenza study based on Twitter and machine learning method

Cited by 20 publications

References 35 publications

Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study

Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study

Influenza Prediction: Analyzing Machine Learning Algorithms

Can Search Query Forecast successfully in China’s novel coronavirus (2019-nCov) pneumonia?

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