Turn to the Internet First? Using Online Medical Behavioral Data to Forecast COVID-19 Epidemic Trend

Cao, Bolin; Yang, Guang; Luo, Ningzheng; Chao, Naipeng

doi:10.1016/j.ipm.2020.102486

Cited by 37 publications

(22 citation statements)

References 61 publications

(64 reference statements)

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“…Indeed, using social media, public opinions on personal face mask use [ 19 ] and COVID-19 vaccine uptake [ 20 , 21 ] have been investigated. Existing research has also studied the predictive power of online medical consultation, online medical appointment, and online medical search in forecasting regional outbreaks and found online medical consultation to be the most predicative [ 22 ]. Furthermore, a recent longitudinal study on the mental health of the Chinese population during the pandemic has found that dissemination of health information via radio was associated with higher levels of anxiety and depression, and suggested television and the internet as alternatives [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Depression Trends on Twitter During the COVID-19 Pandemic: Observational Study

Zhang¹,

Lyu²,

Liu³

et al. 2021

JMIR Infodemiology

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Background The COVID-19 pandemic has affected people’s daily lives and has caused economic loss worldwide. Anecdotal evidence suggests that the pandemic has increased depression levels among the population. However, systematic studies of depression detection and monitoring during the pandemic are lacking. Objective This study aims to develop a method to create a large-scale depression user data set in an automatic fashion so that the method is scalable and can be adapted to future events; verify the effectiveness of transformer-based deep learning language models in identifying depression users from their everyday language; examine psychological text features’ importance when used in depression classification; and, finally, use the model for monitoring the fluctuation of depression levels of different groups as the disease propagates. Methods To study this subject, we designed an effective regular expression-based search method and created the largest English Twitter depression data set containing 2575 distinct identified users with depression and their past tweets. To examine the effect of depression on people’s Twitter language, we trained three transformer-based depression classification models on the data set, evaluated their performance with progressively increased training sizes, and compared the model’s tweet chunk-level and user-level performances. Furthermore, inspired by psychological studies, we created a fusion classifier that combines deep learning model scores with psychological text features and users’ demographic information, and investigated these features’ relations to depression signals. Finally, we demonstrated our model’s capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic. Results Our fusion model demonstrated an accuracy of 78.9% on a test set containing 446 people, half of which were identified as having depression. Conscientiousness, neuroticism, appearance of first person pronouns, talking about biological processes such as eat and sleep, talking about power, and exhibiting sadness were shown to be important features in depression classification. Further, when used for monitoring the depression trend, our model showed that depressive users, in general, responded to the pandemic later than the control group based on their tweets (n=500). It was also shown that three US states—New York, California, and Florida—shared a similar depression trend as the whole US population (n=9050). When compared to New York and California, people in Florida demonstrated a substantially lower level of depression. Conclusions This study proposes an efficient method that can be used to analyze the depression level of different groups of people on Twitter. We hope this study can raise awareness among researchers and the public of COVID-19’s impact on people’s mental health. The noninvasi...

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Section: Introductionmentioning

confidence: 99%

Monitoring Depression Trends on Twitter During the COVID-19 Pandemic: Observational Study

Zhang¹,

Lyu²,

Liu³

et al. 2021

JMIR Infodemiology

View full text Add to dashboard Cite

show abstract

“…In the future, we will further consider whether the proposed integrated model can integrate more information modalities. For example, Wensen Huang et al evaluated predictive value of regional outbreaks of online medical behavior data, including online medical consultation (OMC), online medical appointment (OMA) and online medical search (OMS) for the 2019 coronavirus disease in Shenzhen, China from January 1, 2020 to March 5, 2020 [ 81 ]. If this type of information model can be integrated with data-driven predictive models, and natural language processing (NLP) and other algorithms can be used to extract more information and merge into the model, it will have a certain significance for further improving the predictive effect of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning

Jin¹,

Dong²,

Yu³

et al. 2022

Computers in Biology and Medicine

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“…Compared to benchmark models (e.g., ARIMA, exponential smoothing or seasonal naïve), the authors concluded that with 95% of forecasting intervals for every horizon, their model can generate a more accurate prediction of COVID-19 cases in the UK. By employing online medical data in China (Shenzhen city), Huang et al ( 2021 ) tried to forecast COVID-19 cases. They have used data regarding consultation, appointment and online outbreak search, and then conducted a multivariate vector autoregression analysis.…”

Section: Literature Surveymentioning

confidence: 99%

Investigation of robustness of hybrid artificial neural network with artificial bee colony and firefly algorithm in predicting COVID-19 new cases: case study of Iran

Shaibani

Emamgholipour

Moazeni

2021

Stoch Environ Res Risk Assess

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As an ongoing public health menace, the novel coronavirus pandemic has challenged the world. With several mutations and a high transmission rate, the virus is able to infect individuals in an exponential manner. At the same time, Iran is confronted with multiple wave peaks and the health care system is facing a major challenge. In consequence, developing a robust forecasting methodology can assist health authorities for effective planning. In that regard, with the help of Artificial Neural Network-Artificial Bee Colony (ANN-ABC) and Artificial Neural Network- Firefly Algorithm (ANN-FA) as two robust hybrid artificial intelligence-based models, the current study intends to select the optimal model with the maximum accuracy rate. To do so, first a sample of COVID-19 confirmed cases in Iran ranging from 19 February 2020 to 25 July 2021 is compiled. 75% (25%) of total observation is randomly allocated as training (testing) data. Afterwards, an ANN model is trained with Levenberg–Marquardt algorithm. Accordingly, based on R-squared and root-mean-square error criteria, the optimal number of hidden neurons is computed as 17. The proposed ANN model is employed to develop ANN-ABC and ANN-FA models for achieving the maximum accuracy rate. According to ANN-ABC, the R- squared values of the optimal model are 0.9884 and 0.9885 at train and test stages. In respect to ANN-FA, the R-squared ranged from 0.9954 to 0.9940 at the train and test phases, which indicates the outperformance of ANN-FA for predicting COVID-19 new cases in Iran. Finally, the proposed ANN-ABC and ANN-FA are applied for simulating the COVID-19 new cases data in different countries. The results revealed that both models can be used as a robust predictor of COVID-19 data and in a majority of cases ANN-FA outperforms the ANN-ABC.

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Turn to the Internet First? Using Online Medical Behavioral Data to Forecast COVID-19 Epidemic Trend

Cited by 37 publications

References 61 publications

Monitoring Depression Trends on Twitter During the COVID-19 Pandemic: Observational Study

Monitoring Depression Trends on Twitter During the COVID-19 Pandemic: Observational Study

A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning

Investigation of robustness of hybrid artificial neural network with artificial bee colony and firefly algorithm in predicting COVID-19 new cases: case study of Iran

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