ArticleInternational journal of intelligent systems2022
An intelligent forecast for COVID-19 based on single and multiple features.
Article in International journal of intelligent systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Analysis and prediction of infectious diseases based on spatial visualization and machine learning.Scientific reports · 2024Article
- The coming Omicron waves and factors affecting its spread after China reopening borders.BMC medical informatics and decision making · 2023Article
- Transfer learning based cascaded deep learning network and mask recognition for COVID-19.World wide web · 2023Article
- An intelligent forecast for COVID-19 based on single and multiple features.International journal of intelligent systems · 2022Article
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Authors and funding
6 authors.
Funding
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Abstract
It is urgent to identify the development of the Corona Virus Disease 2019 (COVID-19) in countries around the world. Therefore, visualization is particularly important for monitoring the COVID-19. In this paper, we visually analyze the real-time data of COVID-19, to monitor the trend of COVID-19 in the form of charts. At present, the COVID-19 is still spreading. However, in the existing works, the visualization of COVID-19 data has not established a certain connection between the forecast of the epidemic data and the forecast of the epidemic. To better predict the development trend of the COVID-19, we establish a logistic growth model to predict the development of the epidemic by using the same data source in the visualization. However, the logistic growth model only has a single feature. To predict the epidemic situation in an all-round way, we also predict the development trend of the COVID-19 based on the Susceptible Exposed Infected Removed epidemic model with multiple features. We fit the data predicted by the model to the real COVID-19 epidemic data. The simulation results show that the predicted epidemic development trend is consistent with the actual epidemic development trend, and our model performs well in predicting the trend of COVID-19.
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