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Trend Prediction and Intervention of the COVID-19 Pandemic: A Modelling Study
13 Pages Posted: 28 Apr 2020
More...Abstract
Background: COVID-19 has become a global public health disaster, and more than one million people have been infected all over the world. Governments across the world could rely on mathematical predictions to guide prevention measures in this pandemic. Here we investigate epidemiological models for the new infectious pathogens to simulate and predict the outbreaks of COVID-19 in China and other countries.
Method: To overcome the limitations of the existing epidemiological models, we establish an ABC-SEIR model as a novel epidemiological dynamics model to predict epidemic trend of COVID-19 in the world. This model includes several key factors such as home isolation and collective isolation. At the same time, the infection rate is modified to be linearly decreased with the number of cured and dead. We employ the ABC-SEIR model to predict the epidemic trends, turning points, and ending dates in the major countries suffering COVID-19 on base of the daily cases from January 24 to March 28, 2020.
Findings: According to projection results of our ABC-SEIR models, we predict that there will be about 3 million infected cases in the United States, and the peak of exiting confirmed cases will appear approximately on April 20. Currently, the Italian epidemic is reaching a turning point and could end by mid of June. The disease spread in South Korea will be expected to finish in the end of May, 2020.
Interpretation: Although the COVID-19 outbreak has been alleviated in China by taking active prevention measures, countries around the world should work together to contain the spread of disease. Based on the prediction results of our model, suggestions are recommended for the prevention and control of the disease transmission, such as strengthening the propaganda of epidemic health, enhancing isolation and social distancing measures, and timely testing and treatment.
Funding Statement: Support by the Research Funds of Science and Technology Innovation Committee of Shenzhen Municipality under Grant JCYJ20180305164357463, the National Natural Science Foundation of China under Grant 61801269.
Declaration of Interests: All authors declare no competing interests.
Keywords: COVID-19; Epidemiological Model; Epidemic Prediction and Intervention
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