## What is/are Real Covid 19?

Real Covid 19 - In this study, we evaluated the Ensemble Kalman filter’s performance (EnKF) in the estimation of time-varying model parameters with synthetic data and the real COVID-19 data of Hubei province, China.^{[1]}We illustrate these results via simulation and with real COVID-19 data from the Northeast United States, integrating transportation data into the results.

^{[2]}Computational results on a set of problem instances constructed based on real COVID-19 data demonstrate the effectiveness of the proposed method.

^{[3]}We illustrate these results via simulation and with real COVID-19 data from the Northeast United States, integrating transportation data into the results.

^{[4]}The applicability of the given approach is explored using simulated and a real COVID-19 dataset.

^{[5]}Extensive experiments were performed on a real COVID-19 CT image dataset consisting of 666 chest CT images, with results suggesting the effectiveness of our proposed method compared to several state-of-the-art methods.

^{[6]}Extensive experiments are performed on various partial transfer benchmarks and a real COVID-19 detection task.

^{[7]}Tests on real COVID-19 data confirm the usefulness of our approach.

^{[8]}54% on the smaller dataset, which was enriched by augmented images, out of which all real COVID-19 cases were detected.

^{[9]}Experimental analysis on two synthetic and one real COVID-19 data set demonstrated that our proposed framework outperformed six state-of-the-art methods in terms of binary classification and regression performance.

^{[10]}We also demonstrate the applicability of our approach with real COVID-19 case data.

^{[11]}COVID-19 has caused severe health complications and produced a substantial adverse economic impact around the world Forecasting the trend of COVID-19 infections could help in executing policies to effectively reduce the number of new cases In this study, we apply the decomposition and ensemble model to forecast COVID-19 confirmed cases, deaths, and recoveries in Pakistan for the upcoming month until the end of July For the decomposition of data, the Ensemble Empirical Mode Decomposition (EEMD) technique is applied EEMD decomposes the data into small components, called Intrinsic Mode Functions (IMFs) For individual IMFs modelling, we use the Autoregressive Integrated Moving Average (ARIMA) model The data used in this study is obtained from the official website of Pakistan that is publicly available and designated for COVID-19 outbreak with daily updates Our analyses reveal that the number of recoveries, new cases, and deaths are increasing in Pakistan exponentially Based on the selected EEMD-ARIMA model, the new confirmed cases are expected to rise from 213,470 to 311,454 by 31 July 2020, which is an increase of almost 1 46 times with a 95% prediction interval of 246,529 to 376,379 The 95% prediction interval for recovery is 162,414 to 224,579, with an increase of almost two times in total from 100802 to 193495 by 31 July 2020 On the other hand, the deaths are expected to increase from 4395 to 6751, which is almost 1 54 times, with a 95% prediction interval of 5617 to 7885 Thus, the COVID-19 forecasting results of Pakistan are alarming for the next month until 31 July 2020 They also confirm that the EEMD-ARIMA model is useful for the short-term forecasting of COVID-19, and that it is capable of keeping track of the real COVID-19 data in nearly all scenarios The decomposition and ensemble strategy can be useful to help decision-makers in developing short-term strategies about the current number of disease occurrences until an appropriate vaccine is developed.

^{[12]}The real COVID-19 incidence data entries from 01 July, 2020 to 14 August, 2020 in the country of Pakistan are used for parameter estimation thereby getting fitted values for the biological parameters.

^{[13]}In particular, we propose to utilize the synthetic data and limited unlabeled real COVID-19 CT images to jointly train the segmentation network.

^{[14]}Furthermore, we estimate the values of parameters with the help of least square curve fitting tool for the COVID-19 data recorded in Pakistan since March 1 till June 30, 2020 and show that our considered model give an accurate prediction to the real COVID-19 statistical cases.

^{[15]}To see how well the SEIQR proposed model went, it was compared to real COVID-19 spread data in Saudi Arabia.

^{[16]}In comparison, all models achieve excellent results (over than 90%) in accuracy, precision, recall and F1 score for both synthesized and real COVID-19 CT images, demonstrating the reliable of the synthesized dataset.

^{[17]}As a case study, the COVID-19 transmission dynamics are investigated using daily confirmed cases in Malaysia, where some of the epidemiological parameters of this system are estimated based on the fitting of the model to real COVID-19 data released by the Ministry of Health Malaysia (MOH).

^{[18]}The MLHC-COVID-19 was evaluated in real COVID-19 cases.

^{[19]}With the real COVID-19 test data from March to July collected in Lahore, a significance analysis of the possible features was conducted.

^{[20]}Based on the results obtained, the KF method is capable of keeping track of the real COVID-19 data in nearly all scenarios.

^{[21]}We compare different approaches by solving instances generated using real COVID-19 infection data for distributing vaccines and test kits over the United States and the State of Michigan, respectively.

^{[22]}Results highlight a high correlation between tweets and real COVID-19 data, proving that Twitter can be considered a reliable indicator of the epidemic spreading and that data generated by user activity on social media is becoming an invaluable source for capturing and understanding epidemics outbreaks.

^{[23]}

## 95 % prediction

COVID-19 has caused severe health complications and produced a substantial adverse economic impact around the world Forecasting the trend of COVID-19 infections could help in executing policies to effectively reduce the number of new cases In this study, we apply the decomposition and ensemble model to forecast COVID-19 confirmed cases, deaths, and recoveries in Pakistan for the upcoming month until the end of July For the decomposition of data, the Ensemble Empirical Mode Decomposition (EEMD) technique is applied EEMD decomposes the data into small components, called Intrinsic Mode Functions (IMFs) For individual IMFs modelling, we use the Autoregressive Integrated Moving Average (ARIMA) model The data used in this study is obtained from the official website of Pakistan that is publicly available and designated for COVID-19 outbreak with daily updates Our analyses reveal that the number of recoveries, new cases, and deaths are increasing in Pakistan exponentially Based on the selected EEMD-ARIMA model, the new confirmed cases are expected to rise from 213,470 to 311,454 by 31 July 2020, which is an increase of almost 1 46 times with a 95% prediction interval of 246,529 to 376,379 The 95% prediction interval for recovery is 162,414 to 224,579, with an increase of almost two times in total from 100802 to 193495 by 31 July 2020 On the other hand, the deaths are expected to increase from 4395 to 6751, which is almost 1 54 times, with a 95% prediction interval of 5617 to 7885 Thus, the COVID-19 forecasting results of Pakistan are alarming for the next month until 31 July 2020 They also confirm that the EEMD-ARIMA model is useful for the short-term forecasting of COVID-19, and that it is capable of keeping track of the real COVID-19 data in nearly all scenarios The decomposition and ensemble strategy can be useful to help decision-makers in developing short-term strategies about the current number of disease occurrences until an appropriate vaccine is developed.^{[1]}