An agent-based model to assess citizens' acceptance of COVID-19 restrictions

Italy was the first European state affected by COVID-19. Despite many uncertainties, citizens chose to trust the authorities and their trust was pivotal. This research aims to investigate the contribution of Italian citizens' trust in Public Institutions and how it influenced the acceptance of the necessary counter measures. Applying linear regression to a dataset of 4260 Italian respondents, we modelled trust from its main cognitive components, with particular reference to competence and willingness. Therefore, exploiting agent-based modelling, we investigated how these components affected trust and how trust evolution influences the acceptance of these restrictive measures. Our analysis confirms the key role of competence and willingness as cognitive components of trust. Results also suggest that a generic attempt to raise the average trust, besides being challenging, may not be the best strategy to increase compliance. Furthermore, reasoning at category level is a fundamental to identify the best components on which to invest. © Operational Research Society 2021.

Fast screening framework for infection control scenario identification.

Due to the emergence of the novel coronavirus disease, many recent studies have investigated prediction methods for infectious disease transmission. This paper proposes a framework to quickly screen infection control scenarios and identify the most effective scheme for reducing the number of infected individuals. Analytical methods, as typified by the SIR model, can conduct trial-and-error verification with low computational costs; however, they must be reformulated to introduce additional constraints, and thus are inappropriate for case studies considering detailed constraint parameters. In contrast, multi-agent system (MAS) simulators introduce detailed parameters but incur high computation costs per simulation, making them unsuitable for extracting effective measures. Therefore, we propose a framework that implements an MAS for constructing a training dataset, and then trains a support vector regression (SVR) model to obtain effective measure results. The proposed framework overcomes the weaknesses of conventional methods to produce effective control measure recommendations. The constructed SVR model was experimentally verified by comparing its performance on datasets with expected and unexpected outputs. Although datasets producing an unexpected output decreased the prediction accuracy, by removing randomness from the training dataset, the accuracy of the proposed method was still high in these cases. High-precision predictions of the MAS-based simulation output were obtained for both test datasets in under one second of the computational time. Furthermore, the experimental results establish that the proposed framework can obtain intuitively correct outputs for unknown inputs, and produces sufficiently high-precision prediction with lower computation costs than an existing method.

CoSiT: An Agent-based Tool for Training and Awareness to Fight the Covid-19 Spread

Since the beginning of 2020 and following the recommendation of the Emergency Committee, the WHO (World Health Organization) Director General declared that the Covid-19 outbreak constitutes a Public Health Emergency of International Concern. Given the urgency of this outbreak, the international community is mobilizing to find ways to significantly accelerate the development of interventions. These interventions include raising awareness of ethical solutions such as wearing a face mask and respecting social distancing. Unfortunately, these solutions have been criticized and the number of infections and deaths by Covid-19 has only increased because of the lack of respect for these gestures on the one hand, and because of the lack of awareness and training tools on the spread of this disease through simulation packages on the other. To give importance to the respect of these measures, the WHO is going to try to propose to his member states, training and sensitization campaigns on coronavirus through simulation packages, so that the right decisions are taken in time to save lives. Thus, a rigorous analysis of this problem has enabled us to identify three directions for reflection. First, how to propose an IT tool based on these constraints in order to generalize training and awareness for all? Secondly, how to model and simulate these prescribed measures in our current reality? Thirdly, how to make it playful, interactive, and participative so that it is flexible according to the user’s needs? To address these questions, this paper proposes an interactive Agent-Based Model (ABM) describing a pedagogical (training and educational) tool that can help understanding the spread of Covid-19 and then show the impact of the barrier measures recommended by the WHO. The tool implemented is quite simple to use and can help make appropriate and timely decisions to limit the spread of Covid-19 in the population. © 2022,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Human Dense Avoidance Based on Coverage Control through Robots - A Patrol Algorithm Taking into Account of Visible Region

In this paper, we propose an efficient method for human dense avoidance based on a coverage control of multi-agent system. Our motivation is to contribute to an avoidance of human density in the current situation of COVID-19. We also aim to avoid crowding in social events and public spaces. Firstly, we consider a situation in which a robot autonomously patrols a region where human density occurs. As a main result, we propose a patrol algorithm in which robots distribute a cluster of humans by a coverage control if they discover them. Finally, we show an efficiency of the method based on a numerical simulation. © 2022 The Society of Instrument and Control Engineers - SICE.

A Fractal Approach to Streamline the Management of Hospital Information Systems

The paper proposes solutions to improve HIS management speculating the advantages of a fractal multi-agent system holonic architecture. A case study aimed at adapting a hospital for hybrid operations (COVID / non-COVID) demonstrated the effectiveness of capitalizing on the properties of self-similarity and self-organization in solving specific problems of tracking information and patients flows.

Simulation and Forecasting the Covid-19 Pandemic in the Rabat Region

There have been 19 pandemics since December 2019 in Wuhan, China. It then spread to all of the world’s major cities and capitals. As a result, the World Health Organization declared it a worldwide pandemic. It also impacts some industries such as industry, trade, the economy, and public transit. In this study, we utilized the SIR (Susceptible, Infectious, Recovered) model to forecast the status of Covid-19 in the Rabat region, and the model obtained was compared with the current epidemic situation in the region. Furthermore, we have simulated the propagation of the Covid-19 virus by the Multi-Agent System Gama-Platform. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Approach Based on Multi-agent and Artificial Immune Algorithm for the Vehicle Routing Problem in Home-Health Care

In this period of the COVID-19 pandemic, it is critical to adjust the resources found in multiple fields regarding logistical challenges to ensure the minimization of transportation costs and maximization of patient’s comforts and preferences in-home health care. In this article, we will address the vehicle routing problem with time windows, priority, synchronization and lunch break constraints, all combined with scheduling and planning. Often, patients who no longer need to stay in the hospital must give way to another urgent case, but need more treatment than they can receive at home. Caregivers are assigned to provide special care to needy patients based on their requests within time constraints such as time windows, patient preferences, precedence, and synchronization. This paper aims to optimize the planning and scheduling of home care efficiently with consideration and respect to the criterion such as qualification of caregivers, availability and preferences of the patients. To resolve this problem, an Artificial Immune Algorithm (AIS) is proposed as a generator of routes, and a multi-agent approach is established to guarantee the upmost coordination and communication among the overall actors. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Studying citizens' trust to monitor measures acceptance during covid-19 pandemic

Over the last year, the COVID-19 pandemic has strongly affected everyone's lives. An unprecedented situation, which required enormous sacrifices and very stringent limitations. Within this context, trust has played a crucial role: People decided to trust their institutions to tackle the pandemic and that trust made the strong restrictive measures effective. This work aims to study the response of the Italian population to the early stages of the pandemic. Making use of a survey addressed to 4260 Italian citizens, we realized an agent-based simulation to model and analyze citizen trust starting from its main cognitive sub-components, with particular reference to the dimensions of competence and willingness. The results of this work can be of great interest, both for understanding what happened in the past, but also for designing effective strategies in the future. © 2021 CEUR-WS. All rights reserved.

Effects of misinformation diffusion during a pandemic.

The role of misinformation diffusion during a pandemic is crucial. An aspect that requires particular attention in the analysis of misinfodemics is the rationale of the source of false information, in particular how the behavior of agents spreading misinformation through traditional communication outlets and social networks can influence the diffusion of the disease. We studied the process of false information transmission by malicious agents, in the context of a disease pandemic based on data for the COVID-19 emergency in Italy. We model communication of misinformation based on a negative trust relation, supported by findings in the literature that relate the endorsement of conspiracy theories with low trust level towards institutions. We provide an agent-based simulation and consider the effects of a misinfodemic on policies related to lockdown strategies, isolation, protection and distancing measures, and overall negative impact on society during a pandemic. Our analysis shows that there is a clear impact by misinfodemics in aggravating the results of a current pandemic.