In this paper, the latest global COVID-19 pandemic prediction is addressed. Each country worldwide has faced this pandemic differently, reflected in its statistical number of confirmed and death cases. Predicting the number of confirmed and death cases could allow us to know the future number of cases and provide each country with the necessary information to make decisions based on the predictions. Recent works are focused only on confirmed COVID-19 cases or a specific country. In this work, the firefly algorithm designs an ensemble neural network architecture for each one of 26 countries. In this work, we propose the firefly algorithm for ensemble neural network optimization applied to COVID-19 time series prediction with type-2 fuzzy logic in a weighted average integration method. The proposed method finds the number of artificial neural networks needed to form an ensemble neural network and their architecture using a type-2 fuzzy inference system to combine the responses of individual artificial neural networks to perform a final prediction. The advantages of the type-2 fuzzy weighted average integration (FWA) method over the conventional average method and type-1 fuzzy weighted average integration are shown.

本文讨论了最新的全球 COVID-19 大流行预测。世界上每个国家都以不同的方式面对这一流行病，这反映在其确诊病例和死亡病例的统计数字上。预测确诊和死亡病例数可以让我们知道未来的病例数，并为每个国家提供必要的信息，以便根据预测做出决定。最近的工作仅关注已确认的 COVID-19 病例或特定国家/地区。在这项工作中，萤火虫算法为 26 个国家中的每一个国家设计了一个集成神经网络架构。在这项工作中，我们提出了用于集成神经网络优化的萤火虫算法，该算法适用于 COVID-19 时间序列预测，采用加权平均积分方法使用 2 型模糊逻辑。所提出的方法找到形成集成神经网络所需的人工神经网络的数量及其架构，使用类型 2 模糊推理系统结合各个人工神经网络的响应来执行最终预测。显示了 2 型模糊加权平均积分 (FWA) 方法相对于传统平均方法和 1 型模糊加权平均积分的优势。