Deepluenza: Deep learning for influenza detection from Twitter

Alkouz, Balsam; Aghbari, Zaher Al; Al-Garadi, Mohammed Ali; Sarker, Abeed

doi:10.1016/j.eswa.2022.116845

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…For the influenza monitoring problem, moreover, in achieving better data access efficiency and processing, not only the more powerful algorithms or more effective systems will be applied, but more comprehensive and diversified external data will also be innovated to serve our public health services. There are also some works focusing on retrieving data from different regions and different search engines [ 39 ], News data [ 40 ], human gene database [ 41 ], spatiotemporal feature data [ 42 ] or data from social networking platform such as Weibo [ 43 ], Instagram [ 44 ] or Twitter [ 45 ], which further expand the scope of support availability in the field of influenza surveillance. Therefore, in the future, a greater degree of big data enabling disease detection and prevention will also be the trend that researchers need to focus on, where the work of diversified external data collection and application will also be considered as an important contribution.…”

Section: Discussionmentioning

confidence: 99%

Influenza surveillance with Baidu index and attention-based long short-term memory model

Dai

Li-tao

2023

PLoS ONE

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Background The prediction and prevention of influenza is a public health issue of great concern, and the study of timely acquisition of influenza transmission trend has become an important research topic. For achieving more quicker and accurate detection and prediction, the data recorded on the Internet, especially on the search engine from Google or Baidu are widely introduced into this field. Moreover, with the development of intelligent technology and machine learning algorithm, many updated and advanced trend tracking and forecasting methods are also being used in this research problem. Methods In this paper, a new recurrent neural network architecture, attention-based long short-term memory model is proposed for influenza surveillance. This is a kind of deep learning model which is trained by processing from Baidu Index series so as to fit the real influenza survey time series. Previous studies on influenza surveillance by Baidu Index mostly used traditional autoregressive moving average model or classical machine learning models such as logarithmic linear regression, support vector regression or multi-layer perception model to fit influenza like illness data, which less considered the deep learning structure. Meanwhile, some new model that considered the deep learning structure did not take into account the application of Baidu index data. This study considers introducing the recurrent neural network with long short-term memory combined with attention mechanism into the influenza surveillance research model, which not only fits the research problems well in model structure, but also provides research methods based on Baidu index. Results The actual survey data and Baidu Index data are used to train and test the proposed attention-based long short-term memory model and the other comparison models, so as to iterate the value of the model parameters, and to describe and predict the influenza epidemic situation. The experimental results show that our proposed model has better performance in the mean absolute error, mean absolute percentage error, index of agreement and other indicators than the other comparison models. Conclusion Our proposed attention-based long short-term memory model vividly verifies the ability of this attention-based long short-term memory structure for better surveillance and prediction the trend of influenza. In comparison with some of the latest models and methods in this research field, the model we proposed is also excellent in effect, even more lightweight and robust. Future research direction can consider fusing multimodal data based on this model and developing more application scenarios.

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Section: Discussionmentioning

confidence: 99%

Influenza surveillance with Baidu index and attention-based long short-term memory model

Dai

Li-tao

2023

PLoS ONE

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“…Typically, Tsan et al 12 adopted the Long Short-Term Memory (LSTM) neural networks to predict influenza-like illness and respiratory disease, and LSTM is superior to ARIMA. Alkouz et al 13 proposed a Bidirectional Encoder Representation from Transformers (BERT) based influenza detection model, outperforming traditional methods. Yang et al 14 also used the LSTM method to predict epidemics through multiple open data sources.…”

Section: Related Workmentioning

confidence: 99%

Interpretable spatial identity neural network-based epidemic prediction

Luo,

Li,

Wang

et al. 2023

Sci Rep

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Epidemic spatial–temporal risk analysis, e.g., infectious number forecasting, is a mainstream task in the multivariate time series research field, which plays a crucial role in the public health management process. With the rise of deep learning methods, many studies have focused on the epidemic prediction problem. However, recent primary prediction techniques face two challenges: the overcomplicated model and unsatisfactory interpretability. Therefore, this paper proposes an Interpretable Spatial IDentity (ISID) neural network to predict infectious numbers at the regional weekly level, which employs a light model structure and provides post-hoc explanations. First, this paper streamlines the classical spatio-temporal identity model (STID) and retains the optional spatial identity matrix for learning the contagion relationship between regions. Second, the well-known SHapley Additive explanations (SHAP) method was adopted to interpret how the ISID model predicts with multivariate sliding-window time series input data. The prediction accuracy of ISID is compared with several models in the experimental study, and the results show that the proposed ISID model achieves satisfactory epidemic prediction performance. Furthermore, the SHAP result demonstrates that the ISID pays particular attention to the most proximate and remote data in the input sequence (typically 20 steps long) while paying little attention to the intermediate steps. This study contributes to reliable and interpretable epidemic prediction through a more coherent approach for public health experts.

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“…Its vast pool of information not only serves to heighten public awareness but also acts as a beacon, illuminating the locations and contexts of outbreaks. This wealth of real-time data from Twitter proves invaluable in shedding light on the multifaceted aspects of a wide range of topics and matters of interest to the scientific community from different disciplines, such as infectious disease outbreaks [57][58][59][60][61], cryptocurrency and stock markets [62,63], public health concerns [64][65][66][67], societal problems [68][69][70][71][72], emerging technologies [73,74], human behavior analysis [75][76][77][78], and humanitarian issues [79][80][81][82][83], as can be seen from several prior works in these fields, which focused on sentiment analysis and other forms of content analysis of Tweets. Following the COVID-19 epidemic, a growing corpus of studies have used Twitter data to analyze public reactions during this global health emergency [84,85].…”

Section: Relevance Of Mining and Analysis Of Social Media Data During...mentioning

confidence: 99%

Investigating Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis

Thakur

2023

Preprint

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Since the outbreak of COVID-19, social media platforms, such as Twitter, have experienced a tremendous increase in conversations related to Long COVID. The term “Long COVID” describes the persistence of symptoms of COVID-19 for several weeks or even months following the initial infection. Recent works in this field have focused on sentiment analysis of Tweets related to COVID-19 to unveil the multifaceted spectrum of emotions, viewpoints, and perspectives held by the Twitter community. However, most of these works did not focus on Long COVID, and the few works that focused on Long COVID have limitations. Furthermore, no prior work in this field has investigated Tweets where individuals self-reported experiencing Long COVID on Twitter. The work presented in this paper aims to address these research challenges by presenting multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between May 25, 2020, and January 31, 2023. First, the analysis shows that the average number of Tweets per month where individuals self-reported Long COVID on Twitter, has been considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings of sentiment analysis using VADER show that the percentage of Tweets with positive, negative, and neutral sentiment were 43.12%, 42.65%, and 14.22%, respectively. Third, the analysis of sentiments associated with these Tweets also shows that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.

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Deepluenza: Deep learning for influenza detection from Twitter

Cited by 13 publications

References 15 publications

Influenza surveillance with Baidu index and attention-based long short-term memory model

Influenza surveillance with Baidu index and attention-based long short-term memory model

Interpretable spatial identity neural network-based epidemic prediction

Investigating Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis

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