A team of researchers led by Professor Jianping Huang from Lanzhou University, China, developed the Global Prediction System of the Covid-19 Pandemic (GPCP).

Jianping Huang, who has dedicated a long time studying long-term climate prediction, dust-cloud interaction, and semi-arid climate change, has developed a climate system model -- GPCP -- that can fight the pandemic.

The model is based on the traditional Susceptible-Infected-Recovered (SIR) infectious disease model. The improved methods and results were published in Atmospheric and Ocean Science Letters.

"From the simulation results of four selected countries with relatively high numbers of confirmed cases, the Statistical-Susceptible-Infected-Recovered model using the LM algorithm was found to be more consistent with the actual curve of the epidemic, being better able to reflect its trend of development," explains Prof. Huang.

Notably, the ensemble empirical mode decomposition (EEMD) model and the autoregressive moving average (ARMA) model were also used in combination to improve the prediction. The EEMD method has been widely used in the fields of engineering, meteorology, ecology, among others.

It can decompose the signal according to its own scale and is suitable for non-stationary and nonlinear signal processing. The ARMA method can better predict time series, the authors of the study wrote.

"We found that the EEMD-ARMA method can be directly used to predict the number of daily new cases in countries with a smaller number of confirmed cases whose development trend cannot be predicted by the infectious disease model," he added.

Based on the results, this method is more effective for improving prediction results and making direct predictions, Prof. Huang concluded.

The GPCP model developed by Jianping Huang's team can carry out targeted predictions for different countries and regions, and the researchers claimed that it has achieved good prediction results.

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