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OBJECTIVE The problem of overcrowding in emergency departments (ED) is common in hospitals worldwide. In Greece, especially, this problem is exacerbated by the presence of a large proportion of patients who attend for minor medical problems. In order to manage these patients more efficiently in the ED of the "Venizeleion" General Hospital in Crete a "fast track" clinic was established, the "Clinic for Rapid Management of Low Emergency Cases". The aim of this study was to investigate the potential benefits from the "fast track" clinic in the overall operation of the ED. METHOD Comparison was made between the total number of visits to all ED clinics in the period January-May 2012 (before operation of the "fast track" clinic) and the period January-May 2013 (during which the "fast track" clinic was in operation). An hourly record was kept of the number of visits and the waiting time during five consecutive 24 hour on-call periods in order to determine a possible 24-hour fluctuation pattern in the presentation of patients. RESULTS Despite a 15.3% increase in the total number of ED visits during the first five months of 2013 over the corresponding period of 2012, a decrease was recorded in visits to the individual specialty clinics of the ED (i.e., medical, surgical, etc.) of 5.5% to 33.1%. The largest decrease (33.1%) was observed in the ED medical clinic, which was attributed to examination of the majority of low-risk cases in the "fast track" clinic. The total number of laboratory examinations ordered by the ED did not increase in the second study period. Analysis of the hourly variation showed a drop in demand during the noon time zone. The time available per visit in the "fast track" clinic (from 12.8 to 20.4 min) was considered satisfactory in terms of patient safety and overall waiting time. CONCLUSIONS The establishment of a "fast track" low emergency clinic in the ED had beneficial effects on the management of both patients with minor health problems and those with more serious problems. The "fast track" clinic also helped to keep the cost of emergency services at a low level. The extension of such an establishment to other hospitals would contribute to improving the quality of care provided to patients presenting with acute health problems. [ABSTRACT FROM AUTHOR]

The temporal development of the wave function within a double-barrier quantum well is studied by numerically solving the time-dependent Schrödinger equation. One-dimensional AlGaAs-GaAs-AlGaAs and AlAs-GaAs-AlAs structures are considered in this study. The build-up time and the decay constant of the resonant probability amplitude have been computed as functions of the system parameters. In general, these times are considerably different in magnitude. The amplitude of the wave function trapped inside the well is found to depend crucially on the initial energy spread. The results are in good agreement with those of a recent photoluminescence experiment. [ABSTRACT FROM AUTHOR]

Using the recently proposed Susceptible-Asymptomatic-Infected-Vaccinated-Removed (SAIVR) model, we study the impact of key factors affecting COVID-19 vaccine rollout effectiveness and the susceptibility to resurgent epidemics. The SAIVR model expands the widely used Susceptible-Infectious-Removed (SIR) model for describing epidemics by adding compartments to include the asymptomatic infected (A) and the vaccinated (V) populations. We solve the model numerically to make predictions on the susceptibility to resurgent COVID-19 epidemics depending on initial vaccination coverage, importation loads, continuing vaccination, and more contagious SARS-CoV-2 variants, under persistent immunity and immunity waning conditions. The parameters of the model represent reported epidemiological characteristics of the SARS-CoV-2 virus such as the disease spread in countries with high levels of vaccination coverage. Our findings help explain how the combined effects of different vaccination coverage levels and waning immunity lead to distinct patterns of resurgent COVID-19 epidemics (either surges or endemic), which are observed in countries that implemented different COVID-19 health policies and achieved different vaccinated population plateaus after the vaccine rollouts in the first half of 2021.

Population-wide vaccination is critical for containing the SARS-CoV-2 (COVID-19) pandemic when combined with restrictive and prevention measures. In this study we introduce SAIVR, a mathematical model able to forecast the COVID-19 epidemic evolution during the vaccination campaign. SAIVR extends the widely used Susceptible-Infectious-Removed (SIR) model by considering the Asymptomatic (A) and Vaccinated (V) compartments. The model contains several parameters and initial conditions that are estimated by employing a semi-supervised machine learning procedure. After training an unsupervised neural network to solve the SAIVR differential equations, a supervised framework then estimates the optimal conditions and parameters that best fit recent infectious curves of 27 countries. Instructed by these results, we performed an extensive study on the temporal evolution of the pandemic under varying values of roll-out daily rates, vaccine efficacy, and a broad range of societal vaccine hesitancy/denial levels. The concept of herd immunity is questioned by studying future scenarios which involve different vaccination efforts and more infectious COVID-19 variants., Competing Interests: Authors declare that they have no conflict of interest., (© 2021 Elsevier Ltd. All rights reserved.)

A simple analytical model for modeling the evolution of the 2020 COVID-19 pandemic is presented. The model is based on the numerical solution of the widely used Susceptible-Infectious-Removed (SIR) populations model for describing epidemics. We consider an expanded version of the original Kermack-McKendrick model, which includes a decaying value of the parameter β (the effective contact rate), interpreted as an effect of externally imposed conditions, to which we refer as the forced-SIR (FSIR) model. We introduce an approximate analytical solution to the differential equations that represent the FSIR model which gives very reasonable fits to real data for a number of countries over a period of 100 days (from the first onset of exponential increase, in China). The proposed model contains 3 adjustable parameters which are obtained by fitting actual data (up to April 28, 2020). We analyze these results to infer the physical meaning of the parameters involved. We use the model to make predictions about the total expected number of infections in each country as well as the date when the number of infections will have reached 99% of this total. We also compare key findings of the model with recently reported results on the high contagiousness and rapid spread of the disease., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 Published by Elsevier Ltd.)

Background: Intervention measures have been implemented around the world to mitigate the spread of the coronavirus disease (COVID-19) pandemic. Understanding the dynamics of the disease spread and the effectiveness of the interventions is essential in predicting its future evolution., Objective: The aim of this study is to simulate the effect of different social distancing interventions and investigate whether their timing and stringency can lead to multiple waves (subepidemics), which can provide a better fit to the wavy behavior observed in the infected population curve in the majority of countries., Methods: We have designed and run agent-based simulations and a multiple wave model to fit the infected population data for many countries. We have also developed a novel Pandemic Response Index to provide a quantitative and objective way of ranking countries according to their COVID-19 response performance., Results: We have analyzed data from 18 countries based on the multiple wave (subepidemics) hypothesis and present the relevant parameters. Multiple waves have been identified and were found to describe the data better. The effectiveness of intervention measures can be inferred by the peak intensities of the waves. Countries imposing fast and stringent interventions exhibit multiple waves with declining peak intensities. This result strongly corroborated with agent-based simulations outcomes. We also provided an estimate of how much lower the number of infections could have been if early and strict intervention measures had been taken to stop the spread at the first wave, as actually happened for a handful of countries. A novel index, the Pandemic Response Index, was constructed, and based on the model's results, an index value was assigned to each country, quantifying in an objective manner the country's response to the pandemic., Conclusions: Our results support the hypothesis that the COVID-19 pandemic can be successfully modeled as a series of epidemic waves (subepidemics) and that it is possible to infer to what extent the imposition of early intervention measures can slow the spread of the disease., (©Efthimios Kaxiras, Georgios Neofotistos. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 30.07.2020.)