This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Dong-xue Liang
Tsinghua University, The Future Laboratory
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: With the current worldwide spreading of the coronary virus (COVID-19) pandemic, accurately predicting the rate of spread of the virus has become an urgent need.
Methods: In this article we propose a universal COVID-19 prediction model that is independent of country-specific factors in this paper. By analyzing the pandemic data in China, we combined the advantages of Gaussian function with that of chi-square distribution function, to render an innovative mathematical model named the H-Gaussian with five parameters to be learned, and solved the parameters by a gradient descent algorithm.
Results: We trained the model with partial historical pandemic data to predict subsequent pandemic trends in several regions, and validated the predictions with real data. The H-Gaussian model was experimentally shown to correctly predict the pandemic trends, and the parameters had good interpretability.
Conclusions: On this basis, the global trends of the pandemic are given based on the data currently available, as well as suggestions for subsequent prevention strategies.
Keywords
COVID-19, pandemic trend prediction, machine learning
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Dong-xue Liang
Tsinghua University, The Future Laboratory
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 26 May, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Infectious Diseases
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: With the current worldwide spreading of the coronary virus (COVID-19) pandemic, accurately predicting the rate of spread of the virus has become an urgent need.
Methods: In this article we propose a universal COVID-19 prediction model that is independent of country-specific factors in this paper. By analyzing the pandemic data in China, we combined the advantages of Gaussian function with that of chi-square distribution function, to render an innovative mathematical model named the H-Gaussian with five parameters to be learned, and solved the parameters by a gradient descent algorithm.
Results: We trained the model with partial historical pandemic data to predict subsequent pandemic trends in several regions, and validated the predictions with real data. The H-Gaussian model was experimentally shown to correctly predict the pandemic trends, and the parameters had good interpretability.
Conclusions: On this basis, the global trends of the pandemic are given based on the data currently available, as well as suggestions for subsequent prevention strategies.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
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