World is facing stress due to unpredicted pandemic of novel COVID-19. Daily growing magnitude of confirmed cases of COVID-19 put the whole world humanity at high risk and it has made a pressure on health professionals to get rid of it as soon as possible. So, it becomes necessary to predict the number of upcoming cases in future for the preparation of future plan-of-action and medical set-ups. The present manuscript proposed a hybrid fuzzy time series model for the prediction of upcoming COVID-19 infected cases and deaths in India by using modified fuzzy C-means clustering technique. Proposed model has two phases. In phase-I, modified fuzzy C-means clustering technique is used to form basic intervals with the help of clusters centroid while in phase-II, these intervals are upgraded to form sub-intervals. The proposed model is tested against available COVID-19 data for the measurement of its performance based on mean square error, root mean square error and average forecasting error rate. The novelty of the proposed model lies in the prediction of COVID-19 infected cases and deaths for next coming 31 days. Beside of this, estimation for the approximate number of isolation beds and ICU required has been carried out. The projection of the present model is to provide a base for the decision makers for making protection plan during COVID-19 pandemic.

由于新型 COVID-19 的意外大流行，世界正面临压力。COVID-19 确诊病例的数量每天都在增加，使整个世界人类处于高风险之中，并迫使卫生专业人员尽快摆脱它。因此，有必要预测未来即将发生的病例数量，以准备未来的行动计划和医疗机构。本手稿提出了一种混合模糊时间序列模型，通过使用改进的模糊 C 均值聚类技术来预测印度即将发生的 COVID-19 感染病例和死亡。拟议的模型有两个阶段。在第一阶段，改进的模糊C均值聚类技术被用于在聚类质心的帮助下形成基本区间，而在第二阶段，这些区间被升级以形成子区间。所提出的模型针对可用的 COVID-19 数据进行了测试，以根据均方误差、均方根误差和平均预测误差率来衡量其性能。所提出模型的新颖之处在于预测未来 31 天的 COVID-19 感染病例和死亡人数。除此之外，还对所需隔离床位和加护病房的大致数量进行了估算。本模型的预测旨在为决策者在 COVID-19 大流行期间制定保护计划提供依据。对所需隔离床位和重症监护病房的大致数量进行了估算。本模型的预测旨在为决策者在 COVID-19 大流行期间制定保护计划提供依据。对所需隔离床位和重症监护病房的大致数量进行了估算。本模型的预测旨在为决策者在 COVID-19 大流行期间制定保护计划提供依据。