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Machine Learning Prediction of Covid-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan

Bibliographic Data

ID15506237
AuthorsEssam A Rashed (0000-0001-6571-9807, Suez Canal University, corresponding author), Akimasa Hirata (0000-0001-8336-1140, Nagoya Institute of Technology)
Year2021
Volume18
Issue11
Pages5736-5736
Publication date2021-05-27
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph18115736
PMID34071801
OpenAlexW3165151231
LanguageEN
Citations received3
References cited39

With the wide spread of COVID-19 and the corresponding negative impact on different life aspects, it becomes important to understand ways to deal with the pandemic as a part of daily routine. After a year of the COVID-19 pandemic, it has become obvious that different factors, including meteorological factors, influence the speed at which the disease is spread and the potential fatalities. However, the impact of each factor on the speed at which COVID-19 is spreading remains controversial. Accurate forecasting of potential positive cases may lead to better management of healthcare resources and provide guidelines for government policies in terms of the action required within an effective timeframe. Recently, Google Cloud has provided online COVID-19 forecasting data for the United States and Japan, which would help in predicting future situations on a state/prefecture scale and are updated on a day-by-day basis. In this study, we propose a deep learning architecture to predict the spread of COVID-19 considering various factors, such as meteorological data and public mobility estimates, and applied it to data collected in Japan to demonstrate its effectiveness. The proposed model was constructed using a neural network architecture based on a long short-term memory (LSTM) network. The model consists of multi-path LSTM layers that are trained using time-series meteorological data and public mobility data obtained from open-source data. The model was tested using different time frames, and the results were compared to Google Cloud forecasts. Public mobility is a dominant factor in estimating new positive cases, whereas meteorological data improve their accuracy. The average relative error of the proposed model ranged from 16.1% to 22.6% in major regions, which is a significant improvement compared with Google Cloud forecasting. This model can be used to provide public awareness regarding the morbidity risk of the COVID-19 pandemic in a feasible manner

Artificial neural network · Cloud computing · Coronavirus disease 2019 (COVID-19 · Deep learning · Disease · Geography · Government (linguistics · Machine learning · Meteorology · Pandemic · Recurrent neural network · Time series · Wind speed · Computer Science · COVID-19 diagnosis using AI · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Medicine · Artificial Intelligence

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