Mathematical modeling and a month ahead forecast of the coronavirus disease 2019 (COVID-19) pandemic: an Indian scenario

Forecasting adversities of COVID-19 waves in India using intelligent computing

The second wave of the COVID-19 pandemic outburst triggered enormously all over India. This ill-fated and fatal brawl affected millions of Indian citizens, with many active and infected Indians struggling to recover from this deadly disease to date, leading to a grief situation. The present situation warrants developing a robust and sound forecasting model to evaluate the adversities of the epidemic with reasonable accuracy to assist officials in curbing this hazard. Consequently, we employed Auto-ARIMA, Auto-ETS, Auto-MLP, Auto-ELM, AM, MLP and proposed ELM methods for assessing accumulative infected COVID-19 individuals by the end of July 2021. We made 90 days of advanced forecasting, i.e., up to 24 July 2021, for the number of cumulative infected COVID-19 cases of India using all seven methods in 15 days' intervals. We fine-tuned the hyper-parameters to enhance the prediction performance of these models and observed that the proposed ELM model offers satisfactory accuracy with MAPE of 5.01, and it rendered better accuracy than the other six models. To comprehend the dataset's nature, five features are extracted. The resulting feature values encouraged further investigation of the models for an updated dataset, where the proposed model provides encouraging results.

Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy

The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, in December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 million deaths. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic's second wave in Italy (the period after October 13, 2020). The autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), the neural network autoregression (NNAR) model, the trigonometric exponential smoothing state space model with Box-Cox transformation, ARMA errors, and trend and seasonal components (TBATS), and all of their feasible hybrid combinations were employed to forecast the number of patients hospitalized with mild symptoms and the number of patients hospitalized in the intensive care units (ICU). The data for the period February 21, 2020-October 13, 2020 were extracted from the website of the Italian Ministry of Health ( www.salute.gov.it ). The results showed that (i) hybrid models were better at capturing the linear, nonlinear, and seasonal pandemic patterns, significantly outperforming the respective single models for both time series, and (ii) the numbers of COVID-19-related hospitalizations of patients with mild symptoms and in the ICU were projected to increase rapidly from October 2020 to mid-November 2020. According to the estimations, the necessary ordinary and intensive care beds were expected to double in 10 days and to triple in approximately 20 days. These predictions were consistent with the observed trend, demonstrating that hybrid models may facilitate public health authorities' decision-making, especially in the short-term.

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the rate of COVID-19 spread and modeling of its course have critical impact on both health system and policy makers. Indeed, policy making depends on judgments formed by the prediction models to propose new strategies and to measure the efficiency of the imposed policies. Based on the nonlinear and complex nature of this disorder and difficulties in estimation of virus transmission features using traditional epidemic models, artificial intelligence methods have been applied for prediction of its spread. Based on the importance of machine and deep learning approaches in the estimation of COVID-19 spreading trend, in the present study, we review studies which used these strategies to predict the number of new cases of COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network and multilayer perceptron are among the mostly used strategies in this regard. We compared the performance of several machine learning methods in prediction of COVID-19 spread. Root means squared error (RMSE), mean absolute error (MAE), R2 coefficient of determination (R2), and mean absolute percentage error (MAPE) parameters were selected as performance measures for comparison of the accuracy of models. R2 values have ranged from 0.64 to 1 for artificial neural network (ANN) and Bidirectional long short-term memory (LSTM), respectively. Adaptive neuro-fuzzy inference system (ANFIS), Autoregressive Integrated Moving Average (ARIMA) and Multilayer perceptron (MLP) have also have R2 values near 1. ARIMA and LSTM had the highest MAPE values. Collectively, these models are capable of identification of learning parameters that affect dissimilarities in COVID-19 spread across various regions or populations, combining numerous intervention methods and implementing what-if scenarios by integrating data from diseases having analogous trends with COVID-19. Therefore, application of these methods would help in precise policy making to design the most appropriate interventions and avoid non-efficient restrictions.

A Spreadsheet-Based Short Time Forecasting Method for the COVID-19 Pandemic

As we are writing this paper, the number of daily affected COVID patients is around 0.38 million and with active cases over 3 million in India. This large number of active cases is putting the medical facilities under severe strain. Many researchers have proposed many ways of forecasting the COVID-19 patients but they mainly worked on the cumulative cases and moreover, all those methods required considerable skill and computational cost. In this work, a simple spreadsheet-based forecasting model has been developed which will help to predict the number of active cases in the immediate future i.e., the next few days. This information can be useful for emergency management. The difficulty which is generally faced in predicting the active cases is that the dynamics of active cases has a complex dependence on a number of Non-Pharmaceutical Interventions (NPI) and social factors and can undergo sharp changes. Quadratic, cubic and quartic polynomial functions have been applied to capture these peaks and observed that the quadratic function helps in better prediction of the peak. The accuracy of the prediction methods is measured as well as it is tried to observe how the methods predict data for the next 1 day, 3 days and 6 days. A prediction method analogous to weather forecasting method is recommended in this work where the prediction for each day gets updated depending on the most recent data available. This method has also been found to perform well even in the period there were sharp changes in the trend due to imposition of strict NPI measures.

Impact of COVID-19 outbreak on tropospheric NO2 pollution assessed using Satellite-ground perspectives observations in India

The global outbreak of Novel Corona Virus 2019 (SARS-CoV-2) has made worldwide lockdown including India since March 24, 2020. The current research aims at the improvements of nitrogen dioxide (NO₂) during the COVID-19 lockdown in India. This research has been done using both the open source data sets taken from satellite and ground based for better analysis. For the satellite-based analysis, the Sentinel 5 Precauser's Tropospheric NO₂ from the European Space Agency and for the ground-based numeric data sets from Central Pollution Control Board (CPCB) has been used. During the COVID-19 disease, outbreak the world has set in quarantine and as an overcome air quality improved in Asian countries after national lockdown, the average NO₂ rates plummeted calculated by 40-50%. Similarly, it dramatically decreased in Asia during the COVID-19 pandemic quarantine period. The basic statistical patterns of the NO₂ concentration spectrum of historical data sets (2018-2020) bi-weekly showed during October to March were seen higher in each year. Related with National Ambient Air Quality Standards of mean of NO₂ in India our result shown in the NO₂ levels fall in 21 μg/m³ during the national lockdown, from the Central Pollution Control Board's air quality standards it almost decreased 50% of the hourly mean in India. This caused by the sudden restriction to the development of manufacturing and the transportations which ultimately minimized the fossil fuel burning which cause the most of the NO₂ releases to the atmosphere. Nowadays, people are aware about comparatively prosperous future with clear blue skies and uses of renewable energy sources from the nature.