ABSTRACT
The recent trends of the COVID-19 research are being devoted to disease transmission modeling in presence of vaccinated individuals, while the emerging needs are being focused on developing effective strategies for the optimal distribution of vaccine between population. In this context, we propose a novel non-linear time-varying model that effectively supports policy-makers in predicting and analyzing the dynamics of COVID-19 when partially and fully immune individuals are included in the population. Differently from the related literature, where the common strategies typically rely on the prioritization of the different classes of individuals, we propose a novel Model Predictive Control approach to optimally control the multi-dose vaccine administration in the case the available number of doses is not sufficient to cover the whole population. Focusing on the minimization of the expected number of deaths, the approach discriminates between the number of first and second doses. We calibrate the model on the Israeli scenario using real data and we estimate the impact of the vaccine administration on the virus dynamics. Lastly, we assess the impact of the first dose of the Pfizer's vaccine confirming the results of clinical tests. © 2021 IEEE.
ABSTRACT
This article proposes a stochastic nonlinear model predictive controller to support policymakers in determining robust optimal nonpharmaceutical strategies to tackle the COVID-19 pandemic waves. First, a time-varying SIRCQTHE epidemiological model is defined to get predictions on the pandemic dynamics. A stochastic model predictive control problem is then formulated to select the necessary control actions (i.e., restrictions on the mobility for different socioeconomic categories) to minimize the socioeconomic costs. In particular, considering the uncertainty characterizing this decision-making process, we ensure that the capacity of the healthcare system is not violated in accordance with a chance constraint approach. The effectiveness of the presented method in properly supporting the definition of diversified nonpharmaceutical strategies for tackling the COVID-19 spread is tested on the network of Italian regions using real data. The proposed approach can be easily extended to cope with other countries' characteristics and different levels of the spatial scale. IEEE
ABSTRACT
The recent trends of the COVID-19 research have been devoted to disease transmission modeling, with the aim of investigating the effects of different mitigation strategies mainly through scenario-based simulations. In this context we propose a novel non-linear time-varying model that effectively supports policy-makers in predicting and analyzing the dynamics of COVID-19 secondary waves. Specifically, this paper proposes an accurate SIRUCQTHE epidemiological model to get reliable predictions on the pandemic dynamics. Differently from the related literature, in the fitting phase, we make use of the google mobility reports to identify and predict the evolution of the infection rate. The effectiveness of the presented method is tested on the network of Italian regions. First, we describe the Italian epidemiological scenario in the COVID-19 second wave of contagions, showing the raw data available for the Italian scenario and discussing the main assumptions on the system parameters. Then, we present the different steps of the procedure used for the dynamical fitting of the SIRUCQTHE model. Finally, we compare the estimation results with the real data on the COVID-19 secondary waves in Italy. Provided the availability of reliable data to calibrate the model in heterogeneous scenarios, the proposed approach can be easily extended to cope with other scenarios. © 2021 IEEE.
ABSTRACT
The COVID-19 pandemic has caused, and continues to cause, unprecedented disruption in England. The impact of the pandemic on the English education system has been significant, especially for children and young people with special educational needs and disabilities (SEND). While it was encouraging that the educational rights of children and young people with SEND were highlighted during the COVID-19 pandemic, Government decision-making appeared to be centered around the needs of pupils in mainstream schools. In this article, co-authored by an academic researcher and senior leaders from the Pan London Autism Schools Network (PLASN;a collective of special schools in London and the South East of England, catering for pupils on the autistic spectrum), we reflect on the impact of the COVID-19 pandemic on special schools in England. We document and discuss a range of challenges experienced by PLASN schools, including the educational inequalities that were exposed and perpetuated by the COVID-19 pandemic, as well as the manner in which the needs and realities of special schools were overlooked by the Government. We also detail the creative and innovative solutions implemented by PLASN schools to overcome barriers that they encountered. These solutions centered on facilitating holistic approaches to support, ensuring clear and regular communication with families, providing effective support for home learning, and promoting collaborative ways of working;all of which align with good practice principles in autism education more generally, and are essential elements of practice to maintain post-pandemic. We additionally reflect on how the COVID-19 pandemic could be a catalyst for much-needed change to the SEND system: leading to better educational provision, and therefore better outcomes, for pupils with SEND. © Copyright © 2021 Crane, Adu, Arocas, Carli, Eccles, Harris, Jardine, Phillips, Piper, Santi, Sartin, Shepherd, Sternstein, Taylor and Wright.
ABSTRACT
The COVID-19 crisis is profoundly influencing the global economic framework due to restrictive measures adopted by governments worldwide. Finding real-time data to correctly quantify this impact is very significant but not as straightforward. Nevertheless, an analysis of the power demand profiles provides insight into the overall economic trends. To accurately assess the change in energy consumption patterns, in this work we employ a multi-layer feed-forward neural network that calculates an estimation of the aggregated power demand in the north of Italy, (i.e, in one of the European areas that were most affected by the pandemics) in the absence of the COVID-19 emergency. After assessing the forecasting model reliability, we compare the estimation with the ground truth data to quantify the variation in power consumption. Moreover, we correlate this variation with the change in mobility behaviors during the lockdown period by employing the Google mobility report data. From this unexpected and unprecedented situation, we obtain some intuition regarding the power system macro-structure and its relation with the overall people's mobility. © 2020 AEIT.