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The epidemic situation may cause severe social and economic impacts on a country. So, there is a need for a trustworthy prediction model that can offer better prediction results. The forecasting result will help in making the prevention policies and remedial action in time, and thus, we can reduce the overall social and economic impacts on the country. This article introduces a CNN-LSTM hybrid deep learning prediction model, which can correctly forecast the COVID-19 epidemic across India. The proposed model uses convolutional layers, to extract meaningful information and learn from a given time series dataset. It is also enriched with the LSTM layer's capability, which means it can identify long-term and short-term dependencies. The experimental evaluation has been performed to gauge the performance and suitability of our proposed model among the other well-established time series forecasting models. From the empirical analysis, it is also clear that the use of extra convolutional layers with the LSTM layer may increase the forecasting model's performance. Apart from this, the deep insides of the current situation of medical resource availability across India have been discussed. [ABSTRACT FROM AUTHOR]

Infections or virus-based diseases are a significant threat to human societies and could affect the whole world within a very short time-span. Corona Virus Disease-2019 (COVID-19), also known as novel coronavirus or SARSCoV- 2 (Severe Acute Respiratory Syndrome-Coronavirus-2), is a respiratory based touch contiguous disease. The catastrophic situation resulting from the COVID-19 pandemic posed a serious threat to societies globally. The whole world is making tremendous efforts to combat this life-threatening disease. For taking remedial action and planning preventive measures on time, there is an urgent need for efficient prediction models to confront the COVID-19 outbreak. A deep learning-based ARIMA-LSTM hybrid model is proposed in this article for predicting the COVID-19 outbreak by utilizing real-time information from the WHO's daily bulletin report as well as provides information regarding clinical trials across the world. To evaluate the suitability and performance of our proposed model compared to other well-established prediction models, an experimental study has been performed. To estimate the prediction results, the three performance measures, i.e., Root Mean Square Error (RMSE), Coefficient of determination (R2 Score), and Mean Absolute Percentage Error (MAPE) have been employed. The prediction results of fifty countries substantiated the fact that the proposed ARIMA-LSTM hybrid model performs very well as compared to other models. The proposed model archives the lowest RMSE, lowest MAPE, and highest R2 Score throughout the testing, under varied selection criteria (country- wise). This article aims to contribute a deep learning-based solution for the wellbeing of livings and to provide the current status of clinical trials across the globe. [ABSTRACT FROM AUTHOR]

• The Covid-MANet is a single generic multi-task framework for automated lung segmentation, COVID-19 diagnosis, infection region quantification and severity assessment of COVID-19 into more specific levels as mild, moderate, severe and critical. • The Covid-MANet improves generalization and interpretability for COVID-19 classification by introducing segmentation-based cropping and classification by proposed MA-DenseNet201 model outperforming state-of-the-art networks. • Hybrid loss function is used for lung and infection segmentation task whereas weighted loss function for performance improvement in classification problem. • Investigates the class-wise sensitivity analysis at various confidence threshold levels. Based on prior awareness of various class level accuracies, a weighted average ensemble approach (WAE) outperforms state-of-the-art models for all the classes. • Finally, a gradient-weighted class activation mapping (Grad-CAM) is used for explainable diagnosis to generate a localization map for each disease type, investigates model interpretation in addition to COVID-19 infection map. The devastating outbreak of Coronavirus Disease (COVID-19) cases in early 2020 led the world to face health crises. Subsequently, the exponential reproduction rate of COVID-19 disease can only be reduced by early diagnosis of COVID-19 infection cases correctly. The initial research findings reported that radiological examinations using CT and CXR modality have successfully reduced false negatives by RT-PCR test. This research study aims to develop an explainable diagnosis system for the detection and infection region quantification of COVID-19 disease. The existing research studies successfully explored deep learning approaches with higher performance measures but lacked generalization and interpretability for COVID-19 diagnosis. In this study, we address these issues by the Covid-MANet network, an automated end-to-end multi-task attention network that works for 5 classes in three stages for COVID-19 infection screening. The first stage of the Covid-MANet network localizes attention of the model to the relevant lungs region for disease recognition. The second stage of the Covid-MANet network differentiates COVID-19 cases from bacterial pneumonia, viral pneumonia, normal and tuberculosis cases, respectively. To improve the interpretation and explainability, three experiments have been conducted in exploration of the most coherent and appropriate classification approach. Moreover, the multi-scale attention model MA-DenseNet201 proposed for the classification of COVID-19 cases. The final stage of the Covid-MANet network quantifies the proportion of infection and severity of COVID-19 in the lungs. The COVID-19 cases are graded into more specific severity levels such as mild, moderate, severe, and critical as per the score assigned by the RALE scoring system. The MA-DenseNet201 classification model outperforms eight state-of-the-art CNN models, in terms of sensitivity and interpretation with lung localization network. The COVID-19 infection segmentation by UNet with DenseNet121 encoder achieves dice score of 86.15% outperforming UNet, UNet++, AttentionUNet, R2UNet, with VGG16, ResNet50 and DenseNet201 encoder. The proposed network not only classifies images based on the predicted label but also highlights the infection by segmentation/localization of model-focused regions to support explainable decisions. MA-DenseNet201 model with a segmentation-based cropping approach achieves maximum interpretation of 96% with COVID-19 sensitivity of 97.75%. Finally, based on class-varied sensitivity analysis Covid-MANet ensemble network of MA-DenseNet201, ResNet50 and MobileNet achieve 95.05% accuracy and 98.75% COVID-19 sensitivity. The proposed model is externally validated on an unseen dataset, yields 98.17% COVID-19 sensitivity. [ABSTRACT FROM AUTHOR]