In this paper, Transfer Learning is used in LSTM networks to forecast new COVID cases and deaths. Models trained in data from early COVID infected countries like Italy and the United States are used to forecast the spread in other countries. Single and multistep forecasting is performed from these models. The results from these models are tested with data from Germany, France, Brazil, India, and Nepal to check the validity of the method. The obtained forecasts are promising and can be helpful for policymakers coping with the threats of COVID-19.

中文翻译：

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使用 LSTM 网络进行 COVID-19 病例和死亡预测的迁移学习

在本文中，LSTM 网络中使用迁移学习来预测新的 COVID 病例和死亡人数。根据意大利和美国等早期新冠病毒感染国家的数据训练的模型被用来预测其他国家的传播情况。根据这些模型执行单步和多步预测。这些模型的结果用来自德国、法国、巴西、印度和尼泊尔的数据进行了测试，以检查该方法的有效性。获得的预测很有希望，可以帮助政策制定者应对 COVID-19 的威胁。