Novel COVID-19 Prediction Model in Python Using FB Prophet

Ganesana Charishma, C. Krishna, Tummala Sai Lasya, Sangana Venkata Mounika
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Abstract

Since end of 2019, the coronavirus disease has spread quickly to nations all over the world. This has had a huge impact on many nations' economies and also on the global health system. As there is no effective treatment for cure, it's indeed vital to foresee COVID situations ahead to make the appropriate plans. Despite the fact that there are many models for predicting COVID-19, none of them have predicted for a specific number of days i.e., 30 days or 90 days or 1 week. To address this issue, the proposed study employs the Facebook prophet model for the job of COVID-19 case predictions using Python over the following thirty days. The prophet is a data-driven time series projecting technique using an incremental approach that matches non-linear tendencies with annual, monthly, and everyday periodicity and vacation impacts.
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在Python中使用FB Prophet的新型COVID-19预测模型
自2019年底以来,冠状病毒疾病已迅速蔓延到世界各国。这对许多国家的经济和全球卫生系统产生了巨大影响。由于没有有效的治疗方法,因此提前预测COVID情况以制定适当的计划确实至关重要。尽管有许多预测新冠病毒的模型,但没有一个模型能预测特定的天数,即30天、90天或1周。为了解决这个问题,拟议的研究使用Facebook先知模型在接下来的30天内使用Python进行COVID-19病例预测。先知是一种数据驱动的时间序列预测技术,使用增量方法匹配非线性趋势与年、月、日周期和假期的影响。
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