Biswas Santanu
Department of Mathematics, Jadavpur University, Raja Subodh Chandra Mallick Road, Kolkata, 700032 India.
Department of Mathematics, Adamas University, Barasat-Barrackpore Road, Jagannathpur, Kolkata, West Bengal 700126 India.
Eur Phys J Spec Top. 2022;231(18-20):3537-3544. doi: 10.1140/epjs/s11734-022-00536-3. Epub 2022 Mar 19.
The devastating waves of covid-19 have wreaked havoc on the world, particularly India and US. The article aims to predict the real-time forecasts of covid-19 confirm cases for India and US. To serve the purpose, ARIMA and NNAR based models have been used to the daily new covid-19 confirm cases. The proposed hybrid models are: (i) ARIMA-NNAR model, (ii) NNAR-ARIMA model, (iii) ARIMA-Wavelet ARIMA model, (iv) ARIMA-Wavelet ANN model, (v) NNAR-Wavelet ANN model, and (vi) NNAR-Wavelet ARIMA model. The models are performed to predict the next 45 days of daily new cases. These forecasts can help Govt. to predict the behavior of covid -19 and aware people about the upcoming third wave of covid-19. Our results suggest that hybrid models perform better than single models. We have also proved that our wavelet-based hybrid models can outdated the performance of previously defined hybrid models in terms of accuracy assessments (MAE and RMSE). We have also estimated the time-dependent reproduction number for India and US to observe the present situation.
新冠疫情的毁灭性浪潮给世界带来了巨大破坏，尤其是印度和美国。本文旨在预测印度和美国新冠确诊病例的实时情况。为此，基于自回归积分滑动平均模型（ARIMA）和非线性自回归神经网络（NNAR）的模型被用于预测每日新增新冠确诊病例。所提出的混合模型包括：（i）ARIMA-NNAR模型，（ii）NNAR-ARIMA模型，（iii）ARIMA-小波ARIMA模型，（iv）ARIMA-小波人工神经网络模型，（v）NNAR-小波人工神经网络模型，以及（vi）NNAR-小波ARIMA模型。这些模型用于预测未来45天的每日新增病例。这些预测有助于政府预测新冠疫情的发展态势，并让人们了解即将到来的第三波疫情。我们的结果表明，混合模型比单一模型表现更好。我们还证明，在准确性评估（平均绝对误差MAE和均方根误差RMSE）方面，我们基于小波的混合模型能够超越先前定义的混合模型。我们还估算了印度和美国的时间依赖再生数，以观察当前形势。
