Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Coronavirus Pandemic (COVID-19): A Survey of Analysis, Modeling and Recommendations

Version 1 : Received: 21 August 2020 / Approved: 24 August 2020 / Online: 24 August 2020 (02:54:47 CEST)

How to cite: Amjad, T.; Daud, A.; Hayat, M.K.; Afzal, M.T.; Dawood, H. Coronavirus Pandemic (COVID-19): A Survey of Analysis, Modeling and Recommendations. Preprints 2020, 2020080495. https://doi.org/10.20944/preprints202008.0495.v1 Amjad, T.; Daud, A.; Hayat, M.K.; Afzal, M.T.; Dawood, H. Coronavirus Pandemic (COVID-19): A Survey of Analysis, Modeling and Recommendations. Preprints 2020, 2020080495. https://doi.org/10.20944/preprints202008.0495.v1

Abstract

COVID-19 has created anxiety not only in individuals but also in health organizations, and countries worldwide. Not a single industry is left un-influenced and loss is being estimated in billions of dollars. The widespread of this pandemic disease has challenged researchers all over the world. Some of the researchers are working to invent its cure while, others are applying computing technologies to stop its spread, by analyzing and identifying patterns for prediction and forecasting. This is by no doubt the hottest area of research for the last 100 years. This survey has targeted the research published in computing sub-domains to combat the pandemic. The survey has clustered the scientific efforts into logical groups: surveillance, metrological effects, social media analytics, image processing and business and economy, analysis and modeling. It will serve as a leading source for the followings: researchers who want to identify what has been achieved in different computing sub-domains, those who need fresh authenticated datasets openly accessible for different research contexts and what are future directions in this area of research. The findings of analysis and modeling can be also useful for government agencies who want to set priorities and formulate policies.

Keywords

coronavirus pandemic (COVID-19); analysis; modeling; recommendations; surveillance; social media analytics; meteorological effects; image processing; business and economy

Subject

Computer Science and Mathematics, Analysis

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