The spread of the COVID-19 epidemic worldwide has led to investigations in various aspects, including the estimation of expected cases. As it helps in identifying the need to deal with cases caused by the pandemic. In this study, we have used artificial neural networks (ANNs) to predict the number of cases of COVID-19 in Brazil and Mexico in the upcoming days. Prey predator algorithm (PPA), as a type of metaheuristic algorithm, is used to train the models. The proposed ANN models’ performance has been analyzed by the root mean squared error (RMSE) function and correlation coefficient (R). It is demonstrated that the ANN models have the highest performance in predicting the number of infections (active cases), recoveries, and deaths in Brazil and Mexico. The simulation results of the ANN models show very well predicted values. Percentages of the ANN’s prediction errors with metaheuristic algorithms are significantly lower than traditional monolithic neural networks. The study shows the expected numbers of infections, recoveries, and deaths that Brazil and Mexico will reach daily at the beginning of 2021.

中文翻译：

---

利用人工神经网络和捕食者算法对COVID-19进行预测：以巴西和墨西哥为例

COVID-19流行病在世界范围内的扩散导致人们在各个方面进行了调查，包括对预期病例的估计。因为它有助于确定处理由大流行引起的案件的需要。在这项研究中，我们使用人工神经网络（ANN）预测了未来几天巴西和墨西哥的COVID-19病例数。捕食者捕食者算法（PPA）作为一种元启发式算法，用于训练模型。提出的人工神经网络模型的性能已通过均方根误差（RMSE）函数和相关系数（R）进行了分析。结果表明，ANN模型在预测巴西和墨西哥的感染（活动病例），康复和死亡人数方面具有最高的性能。ANN模型的仿真结果显示出很好的预测值。采用元启发式算法的人工神经网络预测误差百分比显着低于传统的整体神经网络。该研究显示，巴西和墨西哥在2021年初每天将达到预期的感染，康复和死亡人数。