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1.
Machine learning models for government to predict COVID-19 outbreak
Poonam Chaudhary, Saibal K. Pal, Rajan Gupta, Gaurav Pandey, 2020, original scientific article

Abstract: The COVID-19 pandemic has become a major threat to the whole world. Analysis of this disease requires major attention by the government in all countries to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed and trained for Indian region till 10th May, 2020, and testing has been done for the number of cases for the next three weeks. Machine learning models such as SEIR model and Regression model have been used for predictions based on the data collected from the official portal of the Government of India in the time period of 30th January, 2020, to 10th May, 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0, which is the spread of the disease, was calculated to be 2.84. Expected cases are predicted around 175K--200K in the three-week time period of test data, which is very close to the actual numbers. This study will help the government and doctors in preparing their plans for the future.
Found in: osebi
Keywords: COVID-19, India, spread exposed infected recovered model, regression model, machine learning, predictions, forecasting
Published: 01.04.2021; Views: 399; Downloads: 9
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2.
Technological and analytical review of contact tracing apps for COVID-19 management
Poonam Chaudhary, Saibal K. Pal, Rajan Gupta, Gaurav Pandey, 2021, original scientific article

Abstract: Role of technology is improving for COVID-19 management all around the world. Usage of mobile applications, web applications, cloud computing, and related technologies have helped many public administrators worldwide manage the current pandemic. Contact tracing applications are such mobile app solutions that are used by more than 100 countries today. This study presents a structured research review-based framework related to multiple contact tracing applications. The various components of the framework are related to technological working, design architecture, and feature analysis of the applications, along with the analysis of the acceptance of such applications worldwide. Also, components focusing on the security features and analysis of these applications based on Data Privacy, Security Vetting, and different attacks have been included in the research framework. Many applications are yet to explore the analytical capabilities of the data generated through contact tracing. The various use-cases identified for these applications are detecting positive case probability, identifying a containment zone in the country, finding regional hotspots, monitoring public events & gatherings, identifying sensitive routes, and allocating resources in various regions during the pandemic. This study will act as a guide for the users researching contact tracings applications using the proposed four-layered framework for their app assessment.
Found in: osebi
Keywords: novel corona virus, location technology, contact tracing applications, Aarogya Setu App, data science, data analysis
Published: 02.04.2021; Views: 389; Downloads: 0
.pdf Fulltext (3,82 MB)

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