CARD Predictive Modeling and SEI Formulation: COVID-19 Statistics in India

Abstract

The current scenario of the pandemic COVID-19 has been a source of anchorage for researchers, healthcare professionals, and statisticians. Based on the immense data, it has been observed that the role of statistics has been crucial in researching and at the same for predicting the COVID-19 scenario of the entire globe. This paper deals with extensive data collection and predictive modeling to derive a CARD model using statistical tools like regression curve fitting. The exponential growth model has been prevalent in live updates via COVID-19 dashboards maintained by different organizations like WHO, Johns Hopkins University, Indian Council of Medical Research. In a similar tone, the paper discusses a time-varying exponential growth model specific to the Indian condition. However, a generic model has been derived by different researchers of other countries. The model accuracy has been considered satisfactory. Moreover, a State-wise Evaluation Indexing has been performed considering parameters like sanitation, population below the poverty line, literacy rate, and population density. Results have been shown for better data visualization through cartograms. The conclusions are noteworthy, and the CARD model can be trained and developed with better accuracy using the concept of machine and deep learning, keeping in context the huge amount of instantaneous data being generated every day all over the world.