Deep-LSTM ensemble framework to forecast Covid-19: an insight to the global pandemic

Abstract

The pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is spreading all over the world. Medical health care systems are in urgent need to diagnose this pandemic with the support of new emerging technologies like artificial intelligence (AI), internet of things (IoT) and Big Data System. In this dichotomy study, we divide our research in two waysfirstly, the review of literature is carried out on databases of Elsevier, Google Scholar, Scopus, PubMed and Wiley Online using keywords Coronavirus, Covid-19, artificial intelligence on Covid-19, Coronavirus 2019 and collected the latest information about Covid-19. Possible applications are identified from the same to enhance the future research. We have found various databases, websites and dashboards working on real time extraction of Covid-19 data. This will be conducive for future research to easily locate the available information. Secondly, we designed a nested ensemble model using deep learning methods based on long short term memory (LSTM). Proposed Deep-LSTM ensemble model is evaluated on intensive care Covid-19 confirmed and death cases of India with different classification metrics such as accuracy, precision, recall, f-measure and mean absolute percentage error. Medical healthcare facilities are boosted with the intervention of AI as it can mimic human intelligence. Contactless treatment is possible only with the help of AI assisted automated health care systems. Furthermore, remote location self treatment is one of the key benefits provided by AI based systems.

Black fungus immunosuppressive epidemic with Covid-19 associated mucormycosis (zygomycosis): a clinical and diagnostic perspective from India

“…The catastrophic phase of Covid-19 (Coronavirus disease 2019) ravages the world by its disastrous ability of transmission from human to human, and it still continues since December 2019 (Shastri et al 2020). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) yields Covid-19, and now it became more devastating by associating with wide range of fungal and bacterial infections (Shastri et al 2021a). The main pathogens like aspergillus and candida are mainly involved in the co-infection in patients with some Covid-19 history.…”

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

“…LSTM has been applied to predict COVID-19 cases in Canada [ 64 ], India [ 65 ], India, the United States [ 66 ], and Peru, Russia and Iran [ 67 ]. It succeeded to predict the trend of the pandemic despite of the nature of the transmission rate trend (linear, cubic, or exponential growth).…”

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review

“…The Long Short-Term Memory (LSTM) [105] is one of the popular examples. Most of the RNN based techniques are utilized for the prediction 106 , 107 , 108 , 109 and spreading of COVID-19 disease. For instance, Yang et al [110] .…”

“…Many LSTM based techniques targeted the X-ray modality 112 , 113 , 114 . Some methods are data mining and prediction-based [ 109 , 115 , 116 ]. However, limited literature exists that utilizes CT imagery and the RNN models.…”