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The conventional methods used for the diagnostics of viral infection are either expensive and time-consuming or not accurate enough and dependent on consumable reagents. In the presence of pandemics, a fast and reagent-free solution is needed for mass screening. Recently, the diagnosis of viral infections using infrared spectroscopy has been reported as a fast and low-cost method. In this work a fast and low-cost solution for corona viral detection using infrared spectroscopy based on a compact micro-electro-mechanical systems (MEMS) device and artificial intelligence (AI) suitable for mass deployment is presented. Among the different variants of the corona virus that can infect people, 229E is used in this study due to its low pathogeny. The MEMS ATR-FTIR device employs a 6 reflections ZnSe crystal interface working in the spectral range of 2200-7000 cm-1. The virus was propagated and maintained in a medium for long enough time then cell supernatant was collected and centrifuged. The supernatant was then transferred and titrated using plaque titration assay. Positive virus samples were prepared with a concentration of 105 PFU/mL. Positive and negative control samples were applied on the crystal surface, dried using a heating lamp and the spectrum was captured. Principal component analysis and logistic regression were used as simple AI techniques. A sensitivity of about 90 % and a specificity of about 80 % were obtained demonstrating the potential detection of the virus based on the MEMS FTIR device. © 2023 SPIE.
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The recent pandemic COVID-19 has forced almost all governments in the glob to take stringent actions to counteract the spread of this new deadly various. In Jordan, the government introduced the Marshal Law and enforced lockdown of all activities in both of private and public sectors in the country including schools, universities and social gatherings. As a result, demand on the Internet increased sharply: working from home, on-line teaching and learning and on-line orders for different supplies. Such sudden and unexpected dramatic changes increased the load on the fragile existing network and available E-learning systems. In this study, online teaching process, in-house solutions for virtual coaching, in the faculties of engineering, was evaluated and analyzed, with focus on students' points of view. To achi eve such goal, a st ructured questionnaire was distributed to engineering students from different levels and streams. Statistical analysis was conducted to determine the level of satisfaction of students and testify how far the E-learning process was successful as compared with conventional classroom learning. It was concluded that students were not comfortable with this first experience related to online teaching, especially when it comes to specialized technical and practical courses as well as labs. Moreover, they faced serious problems in following up online lectures. This could be attributed to the fact that this is the 1st experience on distance learning and weakness in existing facilities and lack of awareness among students as well as staff members. © 2022, Rajarambapu Institute Of Technology. All rights reserved.
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This work predicts the dynamics of the COVID-19 under widespread vaccination to anticipate the virus's current and future waves. We focused on establishing two population-based models for predictions: the fractional-order model and the fractional-order stochastic model. Based on dose efficacy, which is one of the main imposed assumptions in our study, some vaccinated people will probably be exposed to infection by the same viral wave. We validated the generated models by applying them to the current viral wave in Egypt. We assumed that the Egyptian current wave began on 10th September 2021. Using current actual data and varying our models’ fractional orders, we generate different predicted wave scenarios. The numerical solution of our models is obtained using the fractional Euler method and the fractional Euler Maruyama method. At the end, we compared the current predicted wave under a high vaccination rate with the previous viral wave. Through this comparison, the vaccination control effect is quantified.
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In this paper, we investigate the stochastic nature of the COVID-19 temporal dynamics by generating a fractional-order dynamic model and a fractional-order-stochastic model. Initially, we considered the first and second vaccination doses as multiple vaccinations were initiated worldwide. The concerned models are then tested for the Saudi Arabia second virus wave, which is assumed to start on 1st March 2021. Four daily vaccination scenarios for the first and second dose are assumed for 100 days from the wave beginning. One of these scenarios is based on function optimization using the invasive weed optimization algorithm (IWO). After that, we numerically solve the established models using the fractional Euler method and the Euler-Murayama method. Finally, the obtained virus dynamics using the assumed scenarios and the real one started by the government are compared. The optimized scenario using the IWO effectively minimizes the predicted cumulative wave infections with a 4.4 % lower number of used vaccination doses.
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In this paper, COVID-19 dynamics are modelled with three mathematical dynamic models, fractional order modified SEIRF model, stochastic modified SEIRF model, and fractional stochastic modified SEIRF model, to characterize and predict virus behavior. By using Euler method and Euler-Murayama method, the numerical solutions for the considered models are obtained. The considered models are applied to the case study of Egypt to forecast COVID-19 behavior for the second virus wave which is assumed to be started on 15 November 2020. Finally, comparisons between actual and predicted daily infections are presented.
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In this paper daily confirmed cases of COVID -19 in different countries are modelled using different mathematical regression models. The mathematical curve fitting is used as a prediction tool to model both pervious and next upcoming Coronavirus waves. The used models cover virus spreading from 1/3/2020 to 10/4/2021. According to virus spreading, countries under study in this paper are categorized into three main categories. First category in which Coronavirus first wave takes about two year seasons (about 180 days) to make a complete virus cycle. Second category is countries with higher transmission rates with one year season (about 90 days) to make the first complete virus cycle. These countries take offline periods with low spreading rates. The third category is countries with the highest transmission rates and make complete virus cycles without offline periods. All categories are modelled with different mathematical fitting models. Finally, predictions of different upcoming scenarios for these countries are made.Funding Statement: The research is partially supported by P.R.I.N. 2019 and the RUDN University Program 5-100".Declaration of Interests: The authors have no competing interests.