Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources

Abstract

The influenza problem has always been an important global issue. It not only affects people's health problems but is also an essential topic of governments and health care facilities. Early prediction and response is the most effective control method for flu epidemics. It can effectively predict the influenza-like illness morbidity, and provide reliable information to the relevant facilities. For social facilities, it is possible to strengthen epidemic prevention and care for highly sick groups. It can also be used as a reminder for the public. This study collects information on the influenzalike illness emergency department visits to the Taiwan Centers for Disease Control, and the PM 2.5 open-source data from the Taiwan Environmental Protection Administration's air quality monitoring network. By using deep learning techniques, the relevance of short-term estimates and the outbreak calculation method can be determined. The techniques are published by the WHO to determine whether the influenza-like illness situation is still in a stage of reasonable control. Finally, historical data and future forecasted data are integrated on the web page for visual presentation, to show the actual regional air quality situation and influenza-like illness data and to predict whether there is an outbreak of influenza in the region.

COVID-19 in Iran: A Deeper Look Into The Future

“…14, No. 1 ISSN 2150-8097. doi:XX.XX/XXX.XX this virus have flu-like symptoms, including fever, cough, fatigue and dyspnea [5]. The overall death rate is estimated to be 2.3% but is higher in elderly and those with comorbidities [6].…”

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Towards Using Recurrent Neural Networks for Predicting Influenza-like Illness: Case Study of Covid-19 in Morocco

“…In addition to virology, artificial intelligence is playing a critical role in forecasting the spread of Covid-19 with numerous applications such as computer vision, graph analytics, geographic information systems (SIG), machine/deep learning …etc. The authors of [7] proposes an LSTM based neural network for real-time influenza-like illness rate (ILI) forecasting in Guangzhou, China, a multi-channel mechanism was added to support the heterogeneity of data collected from different sources and with different formats. Another work [8] proposed a deep learning model based on LSTM to predict Influenza-like illness using multiple open data sources in Taiwan centers for disease control, the study gave important results for predicting the disease outbreak in Taiwan.…”