Influenza Forecasting

Shaghaghi, Navid; Calle, Andres; Kouretas, George

doi:10.1145/3378393.3402286

Cited by 8 publications

(1 citation statement)

References 2 publications

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“…The analysis used Google Trends to reduce noise in fashion data. [24] built a model using Long Short-Term Memory (LSTM) to anticipate the number influenza cases using the data of flu season from Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO), and Google Trends to help the decision maker increasing or decreasing vaccines and medicines in advance. [25] stated that Google Trends were very correlated with the Lyme disease incidence report in Germany.…”

Section: Related Workmentioning

confidence: 99%

Google Trends Analysis of COVID-19 Pandemic

Pan

Nguyễn

Abu-gellban

et al. 2020

2020 IEEE International Conference on Big Data (Big Data)

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The World Health Organization (WHO) announced that COVID-19 was a pandemic disease on the 11th of March as there were 118K cases in several countries and territories. Numerous researchers worked on forecasting the number of confirmed cases since anticipating the growth of the cases helps governments adopting knotty decisions to ease the lockdowns orders for their countries. These orders help several people who have lost their jobs and support gravely impacted businesses. Our research aims to investigate the relation between Google search trends and the spreading of the novel coronavirus (COVID-19) over countries worldwide, to predict the number of cases. We perform a correlation analysis on the keywords of the related Google search trends according to the number of confirmed cases reported by the WHO. After that, we applied several machine learning techniques (Multiple Linear Regression, Nonnegative Integer Regression, Deep Neural Network), to forecast the number of confirmed cases globally based on historical data as well as the hybrid data (Google search trends). Our results show that Google search trends are highly associated with the number of reported confirmed cases, where the Deep Learning approach outperforms other forecasting techniques. We believe that it is not only a promising approach for forecasting the confirmed cases of COVID-19, but also for similar forecasting problems that are associated with the related Google trends.

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Section: Related Workmentioning

confidence: 99%