基于隐马尔可夫模型的孟加拉国COVID-19确诊病例和死亡预测

Dibyo Fabian Dofadar, Riyo Hayat Khan, Md. Golam Rabiul Alam
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引用次数: 7

摘要

在孟加拉国,受冠状病毒感染的人数相当令人担忧。预测未来的病例已成为一种必要,因为它涉及确保有足够的资源来帮助人们,并实施严格的指导方针来应对这一流行病。本研究是利用隐马尔可夫模型从时间序列数据集中预测即将到来的COVID-19确诊病例和死亡人数。利用AIC和BIC确定了最优隐藏状态数。实施拟议的模型是为了预测未来90天孟加拉国每天的确诊病例和每天的死亡人数。
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COVID-19 Confirmed Cases and Deaths Prediction in Bangladesh Using Hidden Markov Model
The number of people affected by Coronavirus is quite concerning in Bangladesh. It has become a necessity to forecast the future cases since it involves ensuring adequate resources to help people and imposing strict guidelines to deal with this epidemic. This research is about predicting upcoming COVID-19 confirmed cases and deaths from a time series dataset using Hidden Markov Model. The optimal number of hidden states were determined using AIC and BIC. The proposed models are implemented to forecast the daily confirmed cases and daily deaths of Bangladesh for next 90 days.
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