The latest version of human coronavirus said to be COVID-19 came out as a sudden pandemic disease within human population and in the absence of vaccination and proper treatment till date, it daunting threats heavily to human lives, infecting more than 12, 11, 214 people and death more than 67, 666 people in 208 countries across the globe as on April 06, 2020, which is highly alarming. When no treatment or vaccine is available till date and to avoid COVID-19 to be transmitted in the community, social distancing is the only way to prevent the disease, which is well taken into account in our novel epidemic models as a special compartment, that is, home isolation. Based on the transmitting behavior of COVID-19 in the human population, we develop three quarantine models of this pandemic taking into account the compartments susceptible population, immigrant population, home isolation population, infectious population, hospital quarantine population, and recovered population. Local and global asymptotic stability is proved for all the three models. Extensive numerical simulations are performed to establish the analytical results with suitable examples. Our research reveals that home isolation and quarantine to hospitals are the two pivot force-control policies under the present situation when no treatment is available for this pandemic.In this paper, we have conducted analysis based on data obtained from National Institute of Health (NIH) - Islamabad and produced a forecast of COVID-19 confirmed cases as well as the number of deaths and recoveries in Pakistan using the Auto-Regressive Integrated Moving Average Model (ARIMA). The fitted forecasting models revealed high exponential growth in the number of confirmed cases, deaths and recoveries in Pakistan. Based on our model prediction the number of confirmed cases will be increased by 2.7 times, 95% prediction interval for the number of cases at the end of May 2020 = (5681 to 33079). There could be up to 500 deaths, 95% prediction interval = (168 to 885) and there could be eightfold increase in the number of recoveries, 95% prediction interval = (2391 to 16126). The forecasting results of COVID-19 are alarming for May in Pakistan. The health officials and government should adopt new strategies to control the pandemic from further spread until a proper treatment or vaccine is developed.We describe in this paper an analysis of the spatial evolution of coronavirus pandemic around the world by using a particular type of unsupervised neural network, which is called self-organizing maps. Based on the clustering abilities of self-organizing maps we are able to spatially group together countries that are similar according to their coronavirus cases, in this way being able to analyze which countries are behaving similarly and thus can benefit by using similar strategies in dealing with the spread of the virus. Publicly available datasets of coronavirus cases around the globe from the last months have been used in the analysis. Interesting conclusions have been obtained, that could be helpful in deciding the best strategies in dealing with this virus. Most of the previous papers dealing with data of the Coronavirus have viewed the problem on temporal aspect, which is also important, but this is mainly concerned with the forecast of the numeric information. However, we believe that the spatial aspect is also important, so in this view the main contribution of this paper is the use of unsupervised self-organizing maps for grouping together similar countries in their fight against the Coronavirus pandemic, and thus proposing that strategies for similar countries could be established accordingly.We present results of different approaches to model the evolution of the COVID-19 epidemic in Argentina, with a special focus on the megacity conformed by the city of Buenos Aires and its metropolitan area, including a total of 41 districts with over 13 million inhabitants. We first highlight the relevance of interpreting the early stage of the epidemic in light of incoming infectious travelers from abroad. Next, we critically evaluate certain proposed solutions to contain the epidemic based on instantaneous modifications of the reproductive number. Finally, we build increasingly complex and realistic models, ranging from simple homogeneous models used to estimate local reproduction numbers, to fully coupled inhomogeneous (deterministic or stochastic) models incorporating mobility estimates from cell phone location data. The models are capable of producing forecasts highly consistent with the official number of cases with minimal parameter fitting and fine-tuning. We discuss the strengths and limitations of the proposed models, focusing on the validity of different necessary first approximations, and caution future modeling efforts to exercise great care in the interpretation of long-term forecasts, and in the adoption of non-pharmaceutical interventions backed by numerical simulations.The recent Coronavirus has been spreading through all the world fastly. In this work we focus on the evolution of the COVID-19 in one of the most populous Brazilian states, namely the Rio de Janeiro state. The first case was reported in March 5, 2020, thus we have a considerable amount of available data to make a good analysis. We study the dynamics of COVID-19 through a Susceptible-Infectious-Quarantined-Recovered (SIQR) model with an additional mechanism that represents the implementation of public policies of social isolation. Based on the data collected from the Rio de Janeiro state Department of Health from March 5, 2020 through April 26, 2020, we observed that the implementation of social distancing policies changed the initial exponential growth to a sub-exponential one. The SIQR model with the above-mentioned mechanism is capable of reproducing the data of confirmed cases in Rio de Janeiro, and it explains how that change occurred.  https://www.selleckchem.com/products/OSI-906.html  The model also predicts an important mitigation effect, namely the flattening effect, i.e., the considerably decrease of the maximum number of confirmed cases. Through the results of the model, this effect can be directly related to the social isolation policies. Finally, we consider the relaxation of such policies, and discuss about the ideal period of time to release people to return to their activities.