Coronavirus disease 2019 (COVID-19), which causes severe respiratory illness, has become a pandemic. The World Health Organization has declared it a public health crisis of international concern. We developed a susceptible, exposed, infected, recovered (SEIR) model for COVID-19 to show the importance of estimating the reproduction number (R
0
). This work is focused on predicting the COVID-19 outbreak in its early stage in India based on an estimation of R
0
. The developed model will help policymakers to take active measures prior to the further spread of COVID-19. Data on daily newly infected cases in India from March 2, 2020 to April 2, 2020 were to estimate R
0
using the earlyR package. The maximum-likelihood approach was used to analyze the distribution of R
0
values, and the bootstrap strategy was applied for resampling to identify the most likely R
0
value. We estimated the median value of R
0
to be 1.471 (95% confidence interval [CI], 1.351 to 1.592) and predicted that the new case count may reach 39,382 (95% CI, 34,300 to 47,351) in 30 days.
The COVID-19 is an epidemic that causes respiratory infection. The forecasted data will help the policy makers to take precautionary measures and to control the epidemic spread. The two models were adopted for forecasting the daily newly registered cases of COVID-19 namely ‘earlyR’ epidemic model and ARIMA model. In earlyR epidemic model, the reported values of serial interval of COVID-19 with gamma distribution have been used to estimate the value of R
0
and ‘projections’ package is used to obtain epidemic trajectories by fitting the existing COVID-19 India data, serial interval distribution, and obtained R0 value of respective states. The ARIMA model is developed by using the ‘auto.arima’ function to evaluate the values of (p, d, q) and ‘forecast’ package is used to predict the new infected cases. The methodology evaluation shows that ARIMA model gives the better accuracy compared to earlyR epidemic model.
<span lang="EN-US">A parallel active power filter is employed to enrich the quality of power in the power grid with non-linear loads. The induction motor drive requires better performance in many applications. The overall performance enhancement is performed by mitigating the delivery of current harmonics and related warmness losses in an induction motor. In this paper, comprehensive performance evaluation of a 3-phase induction motor is mentioned with fuzzy logic controller-based shunt active filter (SAF), and the outcomes are compared with proportional integral (PI) and proportional derivative controller (PID) controller. In this work, a new scheme of shunt active filters is connected at the supply side of the vertical speed indicator VSI fed induction motor. The simulation was performed by a fuzzy logic controller (FLC) based 3-</span><span lang="EN-US">f</span><span lang="EN-US"> induction motor drive (IMD) using parallel SAF. Simulation result from MATLAB/Simulink software has been presented to understand the reduction in harmonics by introducing an active filter near the supply side. A fuzzy suitable judgment controller is brought on this work to develop the induction motor dynamic response. Analysis of the simulation outcomes, the usage of MATLAB/Simulink software program for the proposed FLC managed induction motor had been demonstrated and overall performance enhancement of induction motor was discussed.</span>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.