NIPGM(1,1,t.^\alpha) Grey Model of COVID-19 Population Prediction Based on Slime Mold Algorithm

The global spread of COVID-19 makes it extremely urgent to study the characteristics of viral transmission. In this paper, the prediction of the cumulative number of infections in COVID-19 was taken as the research object. Aiming at the limitation of GM (1,1) model, the known data were given exponential weight by using the idea of grey modeling. A new nonlinear power-rate grey model NIPGM $(1,1,\mathrm{t}^{\wedge}\alpha)$ was established by means of a new method of homogeneous exponential accumulation generation. The analytical solution of the model was obtained by theoretical derivation, and the optimal parameters of the model were calculated by slime mold optimization algorithm. Based on the data of Italy's cumulative population of COVID-19 published by the WHO, we predicted the cumulative number of infections in Italy from December 16 to December 30. Compared with the other three representative grey prediction models, the average relative error of NIPGM model is the smallest, and the prediction accuracy is the highest, which provides an effective model reference for the characteristics research of COVID-19 accumulative population.