Modeling COVID-19 considering asymptomatic cases and avoid contacts

World Health Organization (WHO) defined coronaviruses (CoV) as a large family of viruses that cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS CoV).The novel coronavirus (Covid-19) is a new strain that has not been previously identified in humans. Coronaviruses are zoonotic, meaning they are transmitted between animals and people. In this work is presented a mathematical model that describes the transmission of Covid-19. The model considers both symptomatic and asymptomatic cases. Several studies showed the importance of asymptomatic people in the disease transmission. This is a predictive model, we look at different scenarios, first of all assuming any prevention to avoid the diffusion of the virus is taken and secondly different scenarios where precautionary measures to avoid contact between people are taken, such as quarantine and social distancing. We consider a measures to contain the disease, already studied for a predator-prey systems with the disease in the prey population assuming that the infection rate can be decreased avoiding contacts between preys (people in our case). From the numerical results we get that avoiding contacts helps to delay the peak of the maximum number of infected people, that is important in those cases where the hospital system does not have enough seats in the intensive care unit. Furthermore we studied how the reproduction number depends on the parameters values of the model. Some of the parameters are fixed, as found in literature for Italy, while other are used as control parameters.