Forecasting COVID-19 impact in India using pandemic waves Nonlinear Growth Models

Pavan Kumar; Ram Kumar Singh; Chintan Nanda; Himangshu Kalita; Shashikanta Patairiya; Yagya Datt Sharma; Meenu Rani; Akshaya Srikanth Bhagavathula

Mathematical models are widely used to forecast the spreading of the disease and capture the probability of cases from susceptible to infected, and then to a recovery state or death. Many SIR models have been published or proposed online [2] [3] [4] [5] . However, these models assume randomly mixed between all individuals in the given population. Nonlinear models/functions are more advanced methods that provide solution iteratively [6] . The iterative methods such as nonlinear regression include the modified Gauss-Newton method, gradient or steepest-descent method, multivariate secant or false position, and the Marquardt method [6, 7] . With regards to COVID-19, forecasts have played an important role in capturing the probability of infection and the basic reproduction rate. No studies have used a specific nonlinear model to forecast the COVID-19 dynamics in India. Therefore, we generated 30 days forecast the dynamics of cumulative confirmed death and recovery of COVID-19 cases in India.