Optimal control to limit the spread of COVID-19 in Italy. (Special issue on COVID.)

We apply optimal control theory to a generalized SEIR-type model. The proposed system has three controls, representing social distancing, preventive means, and treatment measures to combat the spread of the COVID-19 pandemic. We analyze such optimal control problem with respect to real data transmission in Italy. Our results show the appropriateness of the model, in particular with respect to the number of quarantined/hospitalized (confirmed and infected) and recovered individuals. Considering the Pontryagin controls, we show how in a perfect world one could drastically diminish the number of susceptible, exposed, infected, quarantined/hospitalized, and death individuals, by increasing the population of insusceptible/protected.

Transport and optimal control of vaccination dynamics for COVID-19

We develop a mathematical model for transferring the vaccine BNT162b2 based on the heat diffusion equation. Then, we apply optimal control theory to the proposed generalized SEIR model. We introduce vaccination for the susceptible population to control the spread of the COVID-19 epidemic. For this, we use the Pontryagin minimum principle to find the necessary optimality conditions for the optimal control. The optimal control problem and the heat diffusion equation are solved numerically. Finally, several simulations are done to study and predict the spread of the COVID-19 epidemic in Italy. In particular, we compare the model in the presence and absence of vaccination. © 2022 Elsevier Inc. All rights reserved.

COVIDX: Computer-aided diagnosis of COVID-19 and its severity prediction with raw digital chest X-ray scans

Background: Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the globe. Objective: In the absence or inadequate provision of therapeutic treatments of COVID-19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest X-ray (CXR) image can effortlessly be used as a substitute modality to diagnose the COVID-19. Method: In this study, we present an automatic COVID-19 diagnostic and severity prediction system (COVIDX) that uses deep feature maps of CXR images along with classical machine learning algorithms to identify COVID-19 and forecast its severity. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COVID-19 vs pneumonia, and COVID-19 severity) using different conventional supervised classification algorithms. Results: We evaluated COVIDX through 10-fold cross-validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. In all the adopted evaluation settings, COVIDX showed strong generalization power and outperforms all the prevailing state-of-the-art methods designed for this purpose. Conclusions: Our proposed method (COVIDX), with vivid performance in COVID-19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow-up studies of COVID-19 infected patients. © The Author(s) 2022. Published by Higher Education Press.

Optimal control to limit the spread of COVID-19 in Italy

We apply optimal control theory to a generalized SEIR-type model. The proposed system has three controls, representing social distancing, preventive means, and treatment measures to combat the spread of the COVID-19 pandemic. We analyze such optimal control problem with respect to real data transmission in Italy. Our results show the appropriateness of the model, in particular with respect to the number of quarantined/hospitalized (confirmed and infected) and recovered individuals. Considering the Pontryagin controls, we show how in a perfect world one could have drastically diminish the number of susceptible, exposed, infected, quarantined/hospitalized, and death individuals, by increasing the population of insusceptible/protected.

THE ECONOMIC AND SOCIAL IMPACT OF TELEWORKING IN ROMANIA: PRESENT PRACTICES AND POST PANDEMIC DEVELOPMENTS

The emergence of COVID-19 has drastically transformed the way people are working along with that the productivity of firms is also affected due to this catastrophic event that happened at the end of the year 2019. The teleworking aspect has gained momentum during COVID-19 and it has been used extensively by the firms to maintain their operations. This study aims to examine the impact of COVID-19 policies, adoption of advanced technology, and teleworking on firm productivity in Romania with a sample of 328 including the Vice President, Operation Manager, General manager, Logistics Manager, Procurement Manager, and Information system manager while the collected data was analyzed using the CB SEM technique. After analyzing the collected data, we have found that the COVID-19 pandemic plays an imperative role in increasing teleworking practices in Romania, while the COVID-19 pandemic is not viewed as a significant factor in the adoption of advanced technology in Romania. On the other side, this pandemic negatively affects Romania's firm productivity, whereas teleworking and the adoption of advanced technology positively affect the Romanian firms' productivity. Lastly, the adoption of advanced technology helps in increasing the pattern of teleworking in Romania. The increased use of teleworking practices has been regarded as an important practice in Romania to follow the COVID-19 policies imposed by the government on one side, yet to keep the business operation in the smooth run by working from home through employing the technology to maintain the firm productivity.

Analysing secondary infections of coronavirus disease 2019 across the geopolitical zones of Nigeria using estimated time dependent reproduction number

Time dependent reproduction number (TD - R0) is a measure of secondary infections or transmissibility of a disease useful in monitoring changes in the rate of infection and assessing policies put in place to control the spread of a.2, disease. In this study we used daily infections situation report of COVID - 19 published by the Nigeria Centre for Disease Control (NCDC) to estimate Nigeria's TD - R0 and then repeated the same for the six geo - political zones in the country. Estimates of TD - R0 values for the country from the 23^rd of March - 27^th of May 2020 fluctuated with a maximum of 2.3 (95% CrI) on the 19^th of April and a minimum of 0.83 (95% CrI) on the 16^th May 2020. Despite the decline in TD - R0 since the early stages of the outbreak of COVID - 19 in Nigeria suggesting a fall in the expected rate of secondary infection apart from the northwest and the northeast geo - political zones values remain above 1.0 for other zones and the country, generally. The Kolmogorov - Smirnov (KS) test was used to test the null hypothesis stating that the means of TD - R0 across the geo-political zones does not follow the same distribution pattern. After making adjustments for Type 1 - error we accepted the null hypothesis (p < 0.05) for six pairs of geo-political zones. We conclude that our findings are significant in studying the COVID - 19 epidemic in Nigeria and important in evaluating the strategies deployed by governments at the national and regional levels, thus, the same method can be replicated across Africa.