Vaccine rate forecast for COVID-19 in Africa using hybrid forecasting models

Background: The public health sectors can use the forecasting applications to determine vaccine stock requirements to avoid excess or shortage stock. This prediction will ensure that immunization protection for COVID-19 is well-distributed among African citizens. Objective(s): The aim of this study is to forecast vaccination rate for COVID-19 in Africa Methods: The method used to estimate predictions is the hybrid forecasting models which predicts the COVID-19 vaccination rate (CVR). HARIMA is a hybrid of ARIMA and the Linear Regression model and HGRNN is a hybrid of Generalized Regression Neural Network (GRNN) and the Gaussian Process Regression (GPR) model which are used to improve predictive accuracy. Result(s): In this study, standard and hybrid forecasting models are used to evaluate new COVID-19 vaccine cases daily in May and June 2021. To evaluate the effectiveness of the models, the COVID-19 vaccine dataset for Africa was used, which included new vaccine cases daily from 13 January 2021 to 16 May 2021. Root Mean Squared Error (RMSE) and Error Percentage (EP) are used as evaluation measures in this process. The results obtained showed that the hybrid GRNN model performed better than the hybrid ARIMA model. Conclusion(s): HGRNN model provides accurate daily vaccinated case forecast, which helps to maintain optimal vaccine stock to avoid vaccine wastage and save many lives.Copyright © 2023 Dhamodharavadhani S et al.

Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases.

The recent COVID-19 outbreak has severely affected people around the world. There is a need of an efficient decision making tool to improve awareness about the spread of COVID-19 infections among the common public. An accurate and reliable neural network based tool for predicting confirmed, recovered and death cases of COVID-19 can be very helpful to the health consultants for taking appropriate actions to control the outbreak. This paper proposes a novel Nonlinear Autoregressive (NAR) Neural Network Time Series (NAR-NNTS) model for forecasting COVID-19 cases. This NAR-NNTS model is trained with Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR) training algorithms. The performance of the proposed model has been compared by using Root Mean Square Error (RMSE), Mean Square Error (MSE) and correlation co-efficient i.e. R-value. The results show that NAR-NNTS model trained with LM training algorithm performs better than other models for COVID-19 epidemiological data prediction.

Pattern Analysis of COVID-19 Death and Recovery Cases Data of Countries Using Greedy Biclustering Algorithm

COVID-19 data were analyzed using the biclustering approach to gain insights such as which groups of countries have similar epidemic trajectory patterns over the subset of COVID-19 pandemic outburst days (called bicluster). Countries within these groups (biclusters) are all in the same phase but with a slightly different trajectory. An approach based on the Greedy Two Way KMeans biclustering algorithm (also called Greedy Biclustering) is proposed to analyze COVID-19 epidemiological data, which identifies subsets of countries that represent a similar epidemic trajectory pattern over a specific period of time. To the best of authors’ knowledge, this is a new application of biclustering approach to analyze COVID-19 data. Results confirm that the proposed approach can alert and help the government authorities and healthcare professionals to know what to anticipate and which measures to implement to decelerate the COVID-19 spread. © 2021 River Publishers.

Competitive Capsule Network Based Sentiment Analysis on Twitter COVID'19 Vaccines

COVID-19 is an extremely contagious virus that has rapidly spread around the world. This disease has infected people of all ages in India, from children to the elderly. Vaccination, on the other hand, is the only way to preserve human lives. In the midst of a pandemic, it's critical to know what people think of COVID-19 immunizations. The primary goal of this article is to examine corona vaccination tweets from India's Twitter social media. This study introduces CompCapNets, a unique deep learning approach for Twitter sentiment classification. The results suggest that the proposed method outperforms other strategies when compared to existing traditional methods. © 2022 River Publishers.

Biclustering Analysis of Countries Using COVID-19 Epidemiological Data

In this work, COVID-19 data were analyzed using the biclustering approach to gain insights such as which group of countries have similar epidemic trajectory patterns over the subset of COVID-19 pandemic outburst days (called bicluster). Countries within these groups (biclusters) are all in the same phase but with a slightly different trajectory. An approach based on the Greedy Two-Way KMeans biclustering algorithm is proposed to analyze COVID-19 epidemiological data, which identifies subgroups of countries that show a similar epidemic trajectory patterns over a specific period of time. To the best of authors’ knowledge, this is the first time that the biclustering approach has been applied to analyze COVID-19 data. In fact, these COVID-19 epidemiological data is not a real count because not all data can be tracked properly and other practical difficulties in collecting the data. Even in developed countries, it has huge practical problems. Therefore, if we can use the IoT-based COVID-19 monitoring system to detect the origin of the COVID-19 outbreak, we can identify the real situation in each country. Results confirm that the proposed approach can alert and helps the government authorities and healthcare professionals to know what to anticipate and which measures to implement to decelerate the COVID-19 spread. © 2021, Springer Nature Switzerland AG.

Computational Intelligence Based Hybrid Hyperparameter Tuned Prediction Techniques for COVID-19 Epidemiological Data

The aim of this work is to examine the appropriate predictive model for COVID-19 epidemiological data in the Indian population using Computational Intelligence based Hyper parameter Tuned Regression Techniques (CI-HTR). The development of the COVID-19 Epidemiological Data Prediction (EDP) model for India is proposed by using CI-HTR such as Gaussian process regression (GPR) model and shallow neural network models. For that purpose, this analysis uses a time series data set that includes daily cumulative COVID-19 epidemiological data. In order to increase forecast accuracy, hybrid models are constructed by merging Hyper parameter tuned regression models with Non-Linear Auto Regressive (NAR) neural network. Experiential research in this work reveals significant differences between the different types of CI-HTR models for the COVID-19 fatality prediction. The performance metrics like Root Mean Squared Error (RMSE), and the correlation factor called ‘R-Value’, are used to estimate the proposed methodology for COVID-19 dataset. It has been found that the hybrid GPR prediction model is the superior to the other models for the COVID-19 EDP. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 mortality rate prediction for India using statistical neural networks and gaussian process regression model.

The primary purpose of this research is to identify the best COVID-19 mortality model for India using regression models and is to estimate the future COVID-19 mortality rate for India. Specifically, Statistical Neural Networks (Radial Basis Function Neural Network (RBFNN), Generalized Regression Neural Network (GRNN)), and Gaussian Process Regression (GPR) are applied to develop the COVID-19 Mortality Rate Prediction (MRP) model for India. For that purpose, there are two types of dataset used in this study: One is COVID-19 Death cases, a Time Series Data and the other is COVID-19 Confirmed Case and Death Cases where Death case is dependent variable and the Confirmed case is an independent variable. Hyperparameter optimization or tuning is used in these regression models, which is the process of identifying a set of optimal hyperparameters for any learning process with minimal error. Here, sigma (σ) is a hyperparameter whose value is used to constrain the learning process of the above models with minimum Root Mean Squared Error (RMSE). The performance of the models is evaluated using the RMSE and 'R2 values, which shows that the GRP model performs better than the GRNN and RBFNN.

Novel COVID-19 Mortality Rate Prediction (MRP) Model for India Using Regression Model With Optimized Hyperparameter

The main objective of this study is to estimate the future COVID-19 mortality rate for India using COVID-19 mortality rate models from different countries. Here, the regression method with the optimal hyperparameter is used to build these models. In the literature, numerous mortality models for infectious diseases have been proposed, most of which predict future mortality by extending one or more disease-related attributes or parameters. But most of these models predict mortality rates from historical data. In this paper, the Gaussian process regression model with the optimal hyperparameter is used to develop the COVID-19 mortality rate prediction (MRP) model. Five different MRP models have been built for the U.S., Italy, Germany, Japan, and India. The results show that Germany has the lowest death rate in 2000 plus COVID-19 confirmed cases. Therefore, if India follows the strategy pursued by Germany, India will control the COVID-19 mortality rate even in the increase of confirmed cases.

Sentimental analysis using capsule network with gravitational search algorithm

Day by day the recent development of communication and the data on the web is increasing tremendously. Moreover, the use of social media among people to express their opinion has greatly increased. Therefore, analyzing this textual data using sentimental analysis techniques can be very helpful in capturing and categorizing people’s opinions. This work aims to propose an algorithm which is combination of Capsule Network (CN) with Gravitational Search Algorithm (GSA) to analyze people’s sentiments from twitter data. In text data mining, CN works to an excessive extent for sentiment analysis compared with other models. The performance of the proposed approach is studied using existing benchmark datasets and COVID-19 twitter posts. The results showed that the proposed approach could automatically classify the sentiments with high performance. It works better compared to other algorithms and results also encourage further research. © 2020 River Publishers.

COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models.

The primary aim of this study is to investigate suitable Statistical Neural Network (SNN) models and their hybrid version for COVID-19 mortality prediction in Indian populations and is to estimate the future COVID-19 death cases for India. SNN models such as Probabilistic Neural Network (PNN), Radial Basis Function Neural Network (RBFNN), and Generalized Regression Neural Network (GRNN) are applied to develop the COVID-19 Mortality Rate Prediction (MRP) model for India. For this purpose, we have used two datasets as D1 and D2. The performances of these models are evaluated using Root Mean Square Error (RMSE) and "R," a correlation value between actual and predicted value. To improve prediction accuracy, the new hybrid models have been constructed by combining SNN models and the Non-linear Autoregressive Neural Network (NAR-NN). This is to predict the future error of the SNN models, which adds to the predicted value of these models for getting better MRP value. The results showed that the PNN and RBFNN-based MRP model performed better than the other models for COVID-19 datasets D2 and D1, respectively.