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We have established a novel mathematical model that considers various aspects of the spreading of the virus, including, the transmission based on being in the latent period, environment to human transmission, governmental decisions, and control measures. To accomplish this, a compartmental model with eight batches (sub-population groups) has been proposed and the simulation of the set of differential equations has been conducted to show the effects of the various involved parameters. Also, to achieve more accurate results and closer to reality, the coefficients of a system of differential equations containing transmission rates, death rates, recovery rates and etc. have been proposed by some new step-functions viewpoint. Results: First of all, the efficiency of the proposed model has been shown for Iran and Italy, which completely denoted the flexibility of our model for predicting the epidemic progress and its moment behavior. The model has shown that the reopening plans and governmental measures directly affect the number of active cases of the disease. Also, it has specified that even releasing a small portion of the population (about 2-3 percent) can lead to a severe increase in active patients and consequently multiple waves in the disease progress. The effects of the healthcare capacities of the country have been obtained (quantitatively), which clearly specify the importance of this context. Control strategies including strict implementation of mitigation (reducing the transmission rates) and re-quarantine of some portion of population have been investigated and their efficiency has been shown.
In this paper we propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a set up under general latency and infectious period distributions. To some extent, queuing systems with infi
Since the onset of the COVID-19 outbreak in Wuhan, China, numerous forecasting models have been proposed to project the trajectory of coronavirus infection cases. We propose a new discrete-time Markov chain transition matrix model that directly incor
In December 2019, COVID-19 were detected in Wuhan City, Hubei Province of China. SARS-CoV-2 rapidly spread to the whole Chinese mainland with the people during the Chinese Spring Festival Travel Rush. As of 19 February 2020, 74576 confirmed cases of
The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and non-pharmaceutical interventions for controlling the spread
We consider a global (location independent) model of pandemic growth which generalizes the SIR model to accommodate important features of the COVID-19 pandemic, notably the implementation of pandemic reduction measures. This SHIR model is applied to