Background Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease. The novel coronavirus disease, COVID-19, is spreading rapidly all over the world. The countries with dense populations, in particular, such as India, await imminent risk in tackling the epidemic. Different forecasting models are being used to predict future cases of COVID-19. The predicament for most of them is that they are not able to capture both the linear and nonlinear features of the data solely. Methods We propose an ensemble model integrating an autoregressive integrated moving average model (ARIMA) and a nonlinear autoregressive neural network (NAR). ARIMA models are used to extract the linear correlations and the NAR neural network for modeling the residuals of ARIMA containing nonlinear components of the data. Comparison: Single ARIMA model, ARIMA-NAR model and few other existing models which have been applied on the COVID-19 data in different countries are compared based on performance evaluation parameters. Result The hybrid combination displayed significant reduction in RMSE(16.23%), MAE(37.89%) and MAPE (39.53%) values when compared with single ARIMA model for daily observed cases. Similar results with reduced error percentages were found for daily reported deaths and cases of recovery as well. RMSE value of our hybrid model was lesser in comparison to other models used for forecasting COVID-19 in different countries. Conclusion Results suggested the effectiveness of the new hybrid model over a single ARIMA model in capturing the linear as well as nonlinear patterns of the COVID-19 data.

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In the modern web applications, users have assumed a prominent role by effectively contributing to the provision of some online services, such as live streaming of multimedia content. For instance, while users watch a football match or a concert through those applications, their devices may also be sharing the received content with others who are interested in the same event, defining a Peer-to-Peer (P2P) service model. The state of the art solutions for this purpose are highly dependent on the end-points and the available network bandwidth, hindering the use of some devices (e.g. phone, tablets, TVs, etc.) . In this context, the Global Media Transmission Protocol (GMTP) is proposed, a cross-layer network/transport protocol that uses access routers as the end-point of a system-independent P2P network and to cache contents of servers in Content Delivery Networks (CDN). It is discussed how GMTP enables users of a variety of smart consumer electronic devices to use GMTP-based systems, aswell as a technique for encouraging users to share their network resources according to the principle that the more a user's router shares bandwidth, the more incentives a user's router will receive from the network. The evaluation results show that GMTP can reduce the startup delay in mobile devices, while scaling the number of nodes without increasing the bandwidth consumption, independent of the device type 1 .

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