No abstract

Global effort s around the world are focused on to discuss several health care strategies for minimizing the impact of the new coronavirus (COVID-19) on the community. As it is clear that this virus becomes a public health threat and spreading easily among individuals. Mathematical models with computational simulations are effective tools that help global effort s to estimate key transmission parameters and further improvements for controlling this disease. This is an infectious disease and can be modeled as a system of non-linear differential equations with reaction rates.This work reviews and develops some suggested models for the COVID-19 that can address important questions about global health care and suggest important notes. Then, we suggest an updated model that includes a system of differential equations with transmission parameters. Some key computational simulations and sensitivity analysis are investigated. Also, the local sensitivities for each model state concerning the model parameters are computed using three different techniques: non-normalizations, half normalizations, and full normalizations.Results based on the computational simulations show that the model dynamics are significantly changed for different key model parameters. Interestingly, we identify that transition rates between asymptomatic infected with both reported and unreported symptomatic infected individuals are very sensitive parameters concerning model variables in spreading this disease. This helps international effort s to reduce the number of infected individuals from the disease and to prevent the propagation of new coronavirus more widely on the community. Another novelty of this paper is the identification of the critical model parameters, which makes it easy to be used by biologists with less knowledge of mathematical modeling and also facilitates the improvement of the model for future development theoretically and practically.

WHO has published key planning recommendations for mass gatherings in the context of the current COVID-19 outbreak. The document provides guidance on containing risks of COVID-19 transmission associated with mass gathering events. The UN and WHO have urged governments around the world to take the mental health consequences of the pandemic seriously, and ensure widespread availability of mental health support. WHO discusses this in a recently released article and has published guidelines for communities and a children's book. An article released by WHO Regional Office for Europe introduces recently published technical guidance on preventing and managing the COVID-19 pandemic across longterm care services. WHO has released a framework for decision-making that proposes a step-wise approach to the assessment of the relative risks and benefits of conductin g mass vaccination campaigns in the context of COVID-19 (see 'Subject in Focus' below). Situation in numbers (by WHO Region) Total (new cases in last 24 hours) Globally 5 817 385 cases (116 048) 362 705 deaths (5 017) Africa 96 902 cases (3 973) 2 482 deaths (55) Americas 2 677 500 cases (64 408) 154 608 deaths (3 396) Eastern Mediterranean 489 921 cases (14 502) 12 078 deaths (245) Europe 2 122 350 cases (19 776) 179 353 deaths (975) SouthEast Asia 249 525 cases (11 445) 7 157 deaths (313) Western Pacific 180 446 cases (1 944) 7 014 deaths (33)

Nowadays, there are a variety of descriptive studies of available clinical data for coronavirus disease (COVID-19). Mathematical modelling and computational simulations are effective tools that help global efforts to estimate key transmission parameters. The model equations often require computational tools and dynamical analysis that play an important role in controlling the disease. This work reviews some models for coronavirus first, that can address important questions about the global health care and suggest important notes. Then, we model the disease as a system of differential equations. We develop previous models for the coronavirus, some key computational simulations and sensitivity analysis are added. Accordingly, the local sensitivities for each model state with respect to the model parameters are computed using three different techniques: non-normalizations, half normalizations and full normalizations. Results based on sensitivity analysis show that almost all model parameters may have role on spreading this virus among susceptible, exposed and quarantined susceptible people. More specifically, communicate rate person–to–person, quarantined exposed rate and transition rate of exposed individuals have an effective role in spreading this disease. One possible solution suggests that healthcare programs should pay more attention to intervention strategies, and people need to self-quarantine that can effectively reduce the disease.