论文标题
使用时间序列预测模型的COVID-19大流行预测
COVID-19 Pandemic Prediction using Time Series Forecasting Models
论文作者
论文摘要
数以百万计的人被感染了，由于全球持续正在进行的冠状病毒（Covid-19）大流行，成千上万的人丧生。确定未来感染病例和病毒的传播率至关重要，以避免死亡。准确地预测Covid-19的传播是对研究界的分析和挑战性的现实问题。因此，我们将Covid-19的日间信息用于全世界和10个受影响国家的累积案例的传播；美国，西班牙，意大利，法国，德国，俄罗斯，伊朗，英国，土耳其和印度。我们利用从2020年1月22日至2020年5月20日的冠状病毒的时间数据。我们对Covid-19爆发的演变进行了建模，并使用Arima和Prophet时间序列预测模型进行预测。根据平均绝对误差，均方根误差，根相对平方误差和平均绝对百分比误差评估模型的有效性。我们的分析可以帮助理解疾病爆发的趋势，并提供所采用国家的流行病学阶段信息。我们的调查表明，Arima模型对于预测Covid-19的患病率更有效。预测结果有可能协助政府计划政策以遏制病毒的传播。
Millions of people have been infected and lakhs of people have lost their lives due to the worldwide ongoing novel Coronavirus (COVID-19) pandemic. It is of utmost importance to identify the future infected cases and the virus spread rate for advance preparation in the healthcare services to avoid deaths. Accurately forecasting the spread of COVID-19 is an analytical and challenging real-world problem to the research community. Therefore, we use day level information of COVID-19 spread for cumulative cases from whole world and 10 mostly affected countries; US, Spain, Italy, France, Germany, Russia, Iran, United Kingdom, Turkey, and India. We utilize the temporal data of coronavirus spread from January 22, 2020 to May 20, 2020. We model the evolution of the COVID-19 outbreak, and perform prediction using ARIMA and Prophet time series forecasting models. Effectiveness of the models are evaluated based on the mean absolute error, root mean square error, root relative squared error, and mean absolute percentage error. Our analysis can help in understanding the trends of the disease outbreak, and provide epidemiological stage information of adopted countries. Our investigations show that ARIMA model is more effective for forecasting COVID-19 prevalence. The forecasting results have potential to assist governments to plan policies to contain the spread of the virus.
