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This paper aims at demonstrating how and that model predictive control (MPC) strategies can be used to determine optimal intervention policies against the COVID-19 pandemic. Especially for the time after a first wave of infection and before a vaccine can be safely distributed to a sufficient extent, the intervention experience from the first outbreak can be utilized to guide the policy decision in this period. The MPC problem in this paper takes the pandemic in different regions of a country and its neighboring countries into account, while policies such as wearing masks or social distancing are selected as inputs to be optimized. This optimized policy balances the risk of a second outbreak and socio-economic costs, while considering that the measure should not be too severe to be rejected by the population. Effectiveness of this policy compared to standard intervention policies is compared through numerical simulations.

The paper presents the analysis of data collected with a survey on COVID-19 impact on STEM including engineering and Information and Communication Technologoes (ICT) professionals from different regions that have different cultures and focuses on the effect on women or other marginalized groups. Four major geographic areas are considered - Europe, Asia, North and South America. Particularities in Western, Eastern and Balkan regions in Europe are identified and compared with other geographic regions. Specific models for balancing professional and personal COVID-19 challenges adopted by women and other groups are pointed out and discussed with a focus on past, present, and future. The study will help employers, Higher Education Institutions (HEIs), and authorities to define better policies for supporting women and other marginalized groups in pandemic and other situations that bring office at home and require intensive use of digital technologies. Hints for game changes in the post-COVID 19 period are proposed.

In this paper we explore the effect of the number of daily tests on an epidemics control policy purely based on testing and selective quarantine, and the impact of these actions depending on the time their application starts. We introduce a general model incorporating a stochastic disease evolution, a particular weighted graph representing the population, and an optimal contact tracing strategy to allocate available tests. Simulations on a community of 50’000 individuals show that the evolution of the epidemic produces a clear non-linear response to the variation of the number of tests used and to the starting time of their application. These results suggest that not only a minimum number of tests is necessary to obtain a positive outcome from the tracing strategy but also that there exists a saturation on the contribution of additional tests. The results also show that the timing in the application of the measures is as important as the measures themselves and that an excessive delay can be hardly overcome.

Group Design Project (GDP) is a common education strategy in engineering. However, due to the COVID-19 pandemic, GDP cannot be fulfilled in a typical lab condition. The paper describes an example of delivering intensive hands-on, group project-based engineering course Autonomous Vehicle Dynamics and Control at Cranfield University. The project was designed to be implemented using modern simulation tools. As a result, students have not only obtained a better understanding of the engineering areas but also learned the usage of essential engineering and IT tools. The students obtained skillsets useful in modern engineering applications, where a simulation environment could improve the quality of the system before deployment and reduce a development cost.

The aim of this article is to give a better understanding of the dynamics of the SARS-CoV-2 pandemic in the Bergamo province (Italy), one of the most hit areas of the world, between February and April 2020. A new compartmental model, called SIAT(3)HE, was designed and fitted on accurate data about the pandemic provided by ATS Bergamo, the health protection agency of the Bergamo province. Our results show that SARS-CoV-2 reached Bergamo in January and infected 318,000 people, the 28.8% of the province population. The 43.1% of the infected individuals stayed asymptomatic. As 6,028 people died due to COVID-19 till April 30th, the infection fatality ratio of SARS-CoV-2 in the Bergamo province was 1.9%. These results are in very good agreement with available information: the number of infections is consistent with the results of recent serological surveys and the number of deaths due to COVID-19 is close to the excess mortality of the considered period. Copyright (C) 2021 The Authors.

Surges of COVID-19 infections could lead to insufficient supply of mechanical ventilators, and rationing of needed care. Multiplexing mechanical ventilators (co-MV) to serve multiple patients is a potential temporary solution. However, if patients are ventilated in parallel ventilation, there is currently no means to match ventilation requirements or patients, with no guidelines to date for co-MV. This research uses patient-specific clinically validated respiratory mechanics models to propose a method for patient matching and mechanical ventilator settings for two-patient co-MV under pressure control mode. The proposed method can simulate and estimate the resultant tidal volume of different combinations of co-ventilated patients. With both patients fulfilling the specified constraint under similar ventilation settings, the actual mechanical ventilator settings for co-MV are determined. This method allows clinicians to analyze in silico co-MV before clinical implementation.

In order to analyze the effect of vaccination in a population with the presence of viruses, a variation of the SIR (Susceptible-Infected-Removed) model is proposed taking into account social distancing and the effect of the vaccine. The equilibrium points of the proposed model are calculated and the stability analysis of the system is carried out. For the proposed model, disease-free equilibrium point and endemic equilibrium point are found and the conditions of existence are discussed. For the disease-free equilibrium point the bifurcation conditions are derived and simulations show that reducing the vaccination effort can lead the disease-free equilibrium to the endemic equilibrium. From the theoretical analysis, a minimum value of effort is obtained to guarantee a disease-free equilibrium point. Simulations were carried out from the value obtained from Rv to validate the theoretical results.

The task of managing epidemic security during COVID-19 is considered. The spread of the SARS-CoV-2 virus without and with vaccination is described by mathematical and computer models built on the basis of the epidemic control protocol adopted by the Georgian authorities. The mathematical model of the spread of the SARS-CoV-2 virus is described using the Cauchy problem for a system of ordinary differential equations. For the management of epidemiological safety, a objective function has been built, which takes into account: the financial consequences of introducing a lockdown in the country and the cost of treating the infected. Among the parameters of the model, the governing ones are highlighted. The control parameters are used to minimize the objective function. In the work, mainly theoretical research is given. However, computer simulation and a computational experiment on the proposed computer model with constant parameters allows us to answer the question: what is the number of infected citizens in the country, in which the economy does not need a lockdown, and the recovery prognosis of those infected with the SARS-CoV-2 virus is favorable.

The article examines the psychological antecedents and opportunities for correcting attitudes associated with the person’s rejection of COVID-19 prevention measures. The psychological factors that favor the appearance of such attitudes in a person are discussed. Three groups of these antecedents are distinguished, differing in terms of their place in the structure of the psyche and the degree of their stability during a person’s life: stable, relatively stable, dynamic. The group of stable psychological antecedents includes personality traits, cognitive/thinking style, personal values; into the group of relatively stable – worldview beliefs, science-related knowledge, peculiarities of the social self and group identity; into the group of dynamic ones – the psychological state, attitudes, and beliefs regarding the situation of the pandemic. Two main strategies for counteracting negative attitudes and beliefs a re highlighted: preventive (implemented even before the emergence of undesirable attitudes and the appearance of misinformation provoking their occurrence), reactive (carried out after the formation of negative attitudes and beliefs). Various options for implementing a preventive strategy are considered: limiting the spread of misinformation about COVID-19, preventing its refutation, and developing people’s skills to detect misinformation. The factors of the effectiveness of the reactive counteraction strategy are analyzed: the type of tactics used to correct negative attitudes, the characteristics of the source of misinformation about COVID-19, the structure of the refuting message, the frequency of repetition of misinformation, the time elapsed between its appearance and rebutting it, the degree and direction of the recipient’s cognitive activity, his/her views and beliefs.

The COVID-19 pandemic is the defying crisis of our time. Since mass vaccination has not yet been established, countries still have been facing many issues due to the viral spread. Even in cities with high seroprevalence, intense resurgent waves of COVID-19 have been registered, possibly due to viral variants with greater transmission rates. Accordingly, we develop a new Model Predictive Control (MPC) framework that is able to determine social distancing guidelines and altogether provide estimates for the future epidemiological characteristic of the contagion. For such, the viral dynamics are represented through a Linear Parameter Varying (LPV) version of the Susceptible-Infected-Recovered-Deceased (SIRD) model. The solution of the LPV MPC problem is based on a Sequential Quadratic Program (SQP). This SQP provides convergent estimates of the future LPV scheduling parameters. We use real data to illustrate the efficiency of the proposed method to mitigate this contagion while vaccination is ongoing.