Cost-effectiveness of a mathematical modeling with optimal control approach of spread of COVID-19 pandemic: A case study in Peru

COVID-19 pandemic affects 213 countries and regions around the world. Which the number of people infected with the virus exceeded 26 millions infected and more than 870 thousand deaths until september 04, 2020, in the world, and Peru among the countries most affected by this pandemic. So we proposed a mathematical model describes the dynamics of spread of the COVID-19 pandemic in Peru. The optimal control strategy based on the model is proposed, and several reasonable and suitable control strategies are suggested to the prevention and reduce the spread COVID-19 virus, by conducting awareness campaigns and quarantine with treatment. coronavirus 2019 (COVID-19). Pontryagin's maximum principle is used to characterize the optimal controls and the optimality system is solved by an iterative method. Finally, some numerical simulations are performed to verify the theoretical analysis using Matlab. © 2022

The Media Briefings of the MOHAP and its Communication Content to Raise Awareness of the Coronavirus

At the outbreak of disease and epidemics, governments and health-care organizations prepare emergency crisis management plans, developed based on information and guidance from the World Health Organization. These include preparedness, response and recovery strategies, media and public communication are an important component of their implementation. The concept of crisis communication implies that honest, candid, quick, accurate and full response to the crisis is one of the basic requirements of crisis management. The study aims to reveal the type of communication content presented by the media briefings of the MOHAP awareness of the Coronavirus and to curb its spread. The study adopted a curriculum for content analysis(quantitative and qualitative)of the periodic briefings organized by the Ministry over four months (March, April, May, June), starting from the first briefing held on Saturday, March 7, 2020 ended with the last regular briefing on Tuesday, June 27, 2020. The study found several results, the most important of which were: The MOHAP has been quick, organized and effective in its communication response;Periodic briefings were organized on the latest developments and figures regarding Coronavirus cases in the State, government actions and initiatives announced to curb the spread of COVID19. The study recommends that further studies be undertaken to implement the CERC Crisis and Risk Management Communications Model, in order to reveal the effectiveness of crisis communication applied by the bodies and organizations in the Arab region to manage crises and risks, both in communications and in the media. © 2022 DSR Publishers/ The University of Jordan.

Identifying Anti-Vaccination Tweets in Arabic Language Utilizing NLP, ML, and DL Approaches

Twitter has had a prominent role during the Covid-19 pandemic in discussing public healthcare-related events, including vaccine efficacy. In this context, the sentiments of individuals about the vaccine have varied between supporters of vaccination programs, and opponents who advocate others not to vaccinate. Being able to identify anti-vaxxers through their tweets could enable public health organizations to make wise decisions to enhance vaccine acceptance, by allocating customized awareness campaigns to individuals to reduce anti-vaccination sentiments among different groups. This study aims to provide a smart classification model that can identify the sentiments of individuals in the Middle East region toward Covid-19 vaccinations through the Arabic tweets they publish on their own Twitter accounts, exploiting Natural Language Processing, Machine Learning, and Deep Learning-based models. Nine well-known Machine Learning algorithms and four Deep Learning algorithms were investigated with various feature extraction techniques (TF, TF-IDF, and Glove). The results show that the performance of the TF model outperforms other models in the Multinomial Naïve Bayes with an accuracy of 82.14%. On the other hand, the performance of the TF-IDF model outperforms other models in the Support Vector Machine with an accuracy of 81.81%. Finally, the Glove word embedding model outperforms other models in the LSTM with an accuracy of 81.67%. © 2021 IEEE.

It is not as simple as that: Playing out password security trainings in order to nudge password changes

The COVID-19 pandemic forced a number of companies to place their staff into home office. In terms of security awareness measures, this means that content or training can only be played out remotely. Within this work, we report about a security awareness campaign focusing on password security that was carried out at a German mid-size company (2000 employees). We compare the effect of remotely played out training content on user behavior, i.e, on getting employees to change their password. The first content was directly embedded into an e-mail, the second was compiled on an intranet web page, and the third content was embedded into a video. Password changes were observed solely within the IT backend on the basis of events and timestamps generated by the company's Active Directory service. For the campaign four representative samples (140 employees per sample) among the staff were selected and assigned to the different training contents. A fourth group served as a control group. During a period of 6 weeks, the content was played out two times. Unexpectedly, the measured password change rate observed was very low. Further, compared to the control group's behavior, none of the different content formats played out led to significant more password changes. Clearly, the campaign failed according to its aim. Based on our observations, we provide several possible explanations for which there is some evidence from the literature. © 2021 ACM.