Predictive Model of Coronavirus Disease COVID-19 Cutting Edge Research

Abstract

COVID19 is a global pandemic spread to over 170 nations and regions. In virtually all nations impacted, the number of illnesses and fatalities has skyrocketed. Predictive approaches may be implemented to aid in developing improved plans and the making of sound judgments. These technologies analyze previous events to better anticipate what will occur in the future. These forecasts can aid in the planning of anticipated hazards and repercussions. In order to provide reliable projections, forecasting technology is critical. The prediction technology in this study is divided into two categories: random theoretical mathematical models and data science/machine learning technology. Forecasting relies heavily on data obtained from multiple platforms. Big data acquired from the World Health Organization/national database and social media communications were the two types of data sets examined in this study. The influence of environmental variables, the incubation time, the impact of isolation, age, gender, and other characteristics may all be used to forecast the pandemic. In this paper, the methodologies and factors utilized for prediction have been thoroughly examined. Forecasting technology, on the other hand, comes with its own set of difficulties (technical and general). This study examines these issues and offers a series of advice for those who are presently fighting the global COVID-19 epidemic. Aim: Predictive Model of Coronavirus Disease COVID-19 Cutting Edge Research. Conclusion: To get more accurate global forecasts, the models suggested in the literature must be evaluated on a worldwide scale. Multiple peaks should be included in the model for similar reasons, not just for short-term forecasting but also for anticipating outbreaks later this year. The study also highlighted the limitations of various prediction models and provided practical advice for dealing with the outbreak.