ABSTRACT
COVID-19 is a pandemic that has affected millions of lives over the past year. During previous global epidemics such as H1N1 Swine Flu (2009–2010), the Spanish Flu (1918), Cholera (1905), and HIV (1981), empirical and analytical models have been used to make predictions, tackling the changes and governance easier. This chapter presents a systematic review of relevant models related to COVID-19 which helped in predicting the pandemic cases, eventually making the delivery of healthcare services convenient all over the world. Various models along with their assumptions and conclusions will be studied from published literature, which has really helped the scientific community. This chapter also deals with the introduction of a predictive CARD modeling that fits well with the prelockdown COVID-19 statistics in India.
This deals with extensive data collection and predictive modeling to derive a CARD model using statistical tools like regression and 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, and ICMR. In a similar vein, the paper discusses a time-varying exponential growth model specific to the Indian condition. However, a generic model has been derived for further researchers of other countries. The model accuracy has been considered satisfactory. Moreover, a State-wise Evaluation Indexing (SEI) has been performed considering parameters like sanitation, population below the poverty line, literacy rate, and population density. State-wise index value 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.
