Novel Coronavirus is a highly infectious disease, with over one million confirmed cases and thousands of deaths recorded. The disease has become pandemic, affecting almost all nations of the world, and has caused enormous economic, social and psychological burden on countries. Hygiene and educational campaign programmes have been identified to be potent public health interventions that can curtail the spread of the highly infectious disease. In order to verify this claim quantitatively, we propose and analyze a non-linear mathematical model to investigate the effect of healthy sanitation and awareness on the transmission dynamics of Coronavirus disease (COVID-19) prevalence. Rigorous stability analysis of the model equilibrium points was performed to ascertain the basic reproduction number R0, a threshold that determines whether or not a disease dies out of the population. Our model assumes that education on the disease transmission and prevention induce behavioral changes in individuals to imbibe good hygiene, thereby reducing the basic reproduction number and disease burden. Numerical simulations are carried out using real life data to support the analytic results.The novel coronavirus (COVID-19) outbreak emerged in December 2019. The disease has caused loss of many lives and has become an unprecedented threat to public health worldwide. We develop simple COVID-19 epidemic models to study treatment strategies to control the pandemic. The results show that eradication of the disease is possible if the efficacy of treatment is perfect. We also investigate the existence of a dual-rate effect. Conditions under which the effect occurs are derived. When the effect is present, a tactic to control the infection might be to initially treat infected individuals aggressively at a relatively high rate to drive the prevalence to a lower region that can be maintained in the long run at relatively moderate rate and cost. The short term forecasts regarding different parameters of the COVID-19 are very important to make informed decisions. However, majority of the earlier contributions have used classical time series models, such as auto regressive integrated moving average (ARIMA) models, to obtain the said forecasts for Iran and its neighbors. In addition, the impacts of lifting the lockdowns in the said countries have not been studied. The aim of this paper is to propose more flexible Bayesian structural time series (BSTS) models for forecasting the future trends of the COVID-19 in Iran and its neighbors, and to compare the predictive power of the BSTS models with frequently used ARIMA models. The paper also aims to investigate the casual impacts of lifting the lockdown in the targeted countries using proposed models. We have proposed BSTS models to forecast the patterns of this pandemic in Iran and its neighbors. The predictive power of the proposed models has been compared with ARIMA models using different forecast accighbors need to improve their extensive healthcare infrastructure to cut down the higher expected death toll. Finally, these countries should develop and implement the strict SOPs for the commercial activities in order to prevent the expected second wave of the pandemic.The serious efforts would be needed to make sure that these expected figures regarding active number of cases come true. Iran and its neighbors need to improve their extensive healthcare infrastructure to cut down the higher expected death toll. Finally, these countries should develop and implement the strict SOPs for the commercial activities in order to prevent the expected second wave of the pandemic.One of the major difficulties with modelling an ongoing epidemic is that often data is limited or incomplete, making it hard to estimate key epidemic parameters and outcomes (e.g. attack rate, peak time, reporting rate, reproduction number). In the current study, we present a model for data-fitting limited infection case data which provides estimates for important epidemiological parameters and outcomes. The model can also provide reasonable short-term (one month) projections. We apply the model to the current and ongoing COVID-19 outbreak in Canada both at the national and provincial/territorial level.With the spread of COVID-19 across the world, a large amount of data on reported cases has become available. We are studying here a potential bias induced by the daily number of tests which may be insufficient or vary over time. Indeed, tests are hard to produce at the early stage of the epidemic and can therefore be a limiting factor in the detection of cases. Such a limitation may have a strong impact on the reported cases data. Indeed, some cases may be missing from the official count because the number of tests was not sufficient on a given day. In this work, we propose a new differential equation epidemic model which uses the daily number of tests as an input. We obtain a good agreement between the model simulations and the reported cases data coming from the state of New York. We also explore the relationship between the dynamic of the number of tests and the dynamics of the cases. We obtain a good match between the data and the outcome of the model. Finally, by multiplying the number of tests by 2, 5, 10, and 100 we explore the consequences for the number of reported cases.In this paper we forecast the spread of the coronavirus disease 2019 outbreak in Italy in the time window from May 19 to June 2, 2020. H-Cys(Trt)-OH In particular, we consider the forecast of the number of new daily confirmed cases. A forecast procedure combining a log-polynomial model together with a first-order integer-valued autoregressive model is proposed. An out-of-sample comparison with forecasts from an autoregressive integrated moving average (ARIMA) model is considered. This comparison indicates that our procedure outperforms the ARIMA model. The Root Mean Square Error (RMSE) of the ARIMA is always greater than that of the our procedure and generally more than twice as high as the our procedure RMSE. We have also conducted Diebold and Mariano (1995) tests of equal mean square error (MSE). The tests results confirm that forecasts from our procedure are significantly more accurate at all horizons. We think that the advantage of our approach comes from the fact that it explicitly takes into account the number of swabs.