The COVID-19 outbreak in late December 2019 is still spreading rapidly in many countries and regions around the world. It is thus urgent to predict the development and spread of the epidemic. In this paper, we have developed a forecasting model of COVID-19 by using a deep learning method with rolling update mechanism based on the epidemical data provided by Johns Hopkins University. First, as traditional epidemical models use the accumulative confirmed cases for training, it can only predict a rising trend of the epidemic and cannot predict when the epidemic will decline or end, an improved model is built based on long short-term memory (LSTM) with daily confirmed cases training set. Second, considering the existing forecasting model based on LSTM can only predict the epidemic trend within the next 30 days accurately, the rolling update mechanism is embedded with LSTM for long-term projections. Third, by introducing Diffusion Index (DI), the effectiveness of preventive measures like social isolation and lockdown on the spread of COVID-19 is analyzed in our novel research. The trends of the epidemic in 150 days ahead are modeled for Russia, Peru and Iran, three countries on different continents. Under our estimation, the current epidemic in Peru is predicted to continue until November 2020. The number of positive cases per day in Iran is expected to fall below 1000 by mid-November, with a gradual downward trend expected after several smaller peaks from July to September, while there will still be more than 2000 increase by early December in Russia.  https://www.selleckchem.com/products/Gefitinib.html  Moreover, our study highlights the importance of preventive measures which have been taken by the government, which shows that the strict controlment can significantly reduce the spread of COVID-19.COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.Co-infection of Middle Eastern respiratory syndrome, coronavirus and tuberculosis, TB has a complex clinical entities that has estimated worldwide; mostly, in the Middle East. Clinical studies have shown that the propagation of disease is faster in (MERS-CoV) and TB co-infection compared to those of mono-infection. Clinical reports have shown that treatment of tuberculosis (TB) increase the risk of (MERS-CoV) reactivation. In this article, we propose an epidemic model to represents the Middle East respiratory syndrome coronavirus and tuberculosis (TB) co-infection. To do this, we first find the basic reproductive number and analyze the stability of the model. The stability conditions are obtained in term of the basic reproductive number. We also study the bifurcation analysis of the model, using the central manifold theory. Sensitivity of the basic reproductive number is performed to understand the most sensitive parameters. Finally, we show the feasibility of the analytical work, by numerical simulation.This paper studies the social and economic responses to the COVID-19 pandemic in a large sample of countries. I stress, in particular, the importance of countries' interconnections to understand the spread of the virus. I estimate a global VAR model and exploit a dataset on existing social connections across country borders. I show that social networks help explain not only the spread of the disease but also cross-country spillovers in perceptions about coronavirus risk and in social distancing behavior. In the early phases of the pandemic, perceptions of coronavirus risk in most countries are affected by pandemic shocks originating in Italy. Later, the USA, Spain, and the UK play sizable roles. Social distancing responses to domestic and global health shocks are heterogeneous; however, they almost always exhibit delays and sluggish adjustments. Unemployment responses vary widely across countries. Unemployment is particularly responsive to health shocks in the USA and Spain, while unemployment fluctuFations are attenuated almost everywhere else.Motivated by the many diverse responses of different countries to the COVID-19 emergency, here we develop a toy model of the dependence of the epidemics spreading on the availability of tests for disease. Our model, that we call SUDR+K, grounds on the usual SIR model, with the difference of splitting the total fraction of infected individuals in two components patients that are still undetected and patients that have been already detected through tests. Moreover, we assume that available tests increase at a constant rate from the beginning of epidemics but are consumed to detect infected individuals. Strikingly, we find a bi-stable behavior between a phase with a giant fraction of infected and a phase with a very small fraction. We show that the separation between these two regimes is governed by a match between the rate of testing and a rate of infection spread at given time. We also show that the existence of two phases does not depend on the mathematical choice of the form of the term describing the rate at which undetected individuals are tested and detected.