Socioeconomic analysis of infectious diseases based on different scenarios using uncertain SEIAR system dynamics with effective subsystems and ANFIS

PurposeThis paper aims to analyze the socioeconomic impacts of infectious diseases based on uncertain behaviors of social and effective subsystems in the countries. The economic impacts of infectious diseases in comparison with predicted gross domestic product (GDP) in future years could be beneficial for this aim along with predicted social impacts of infectious diseases in countries.Design/methodology/approachThe proposed uncertain SEIAR (susceptible, exposed, infectious, asymptomatic and removed) model evaluates the impacts of variables on different trends using scenario base analysis. This model considers different subsystems including healthcare systems, transportation, contacts and capacities of food and pharmaceutical networks for sensitivity analysis. Besides, an adaptive neuro-fuzzy inference system (ANFIS) is designed to predict the GDP of countries and determine the economic impacts of infectious diseases. These proposed models can predict the future socioeconomic trends of infectious diseases in each country based on the available information to guide the decisions of government planners and policymakers.FindingsThe proposed uncertain SEIAR model predicts social impacts according to uncertain parameters and different coefficients appropriate to the scenarios. It analyzes the sensitivity and the effects of various parameters. A case study is designed in this paper about COVID-19 in a country. Its results show that the effect of transportation on COVID-19 is most sensitive and the contacts have a significant effect on infection. Besides, the future annual costs of COVID-19 are evaluated in different situations. Private transportation, contact behaviors and public transportation have significant impacts on infection, especially in the determined case study, due to its circumstance. Therefore, it is necessary to consider changes in society using flexible behaviors and laws based on the latest status in facing the COVID-19 epidemic.Practical implicationsThe proposed methods can be applied to conduct infectious diseases impacts analysis.Originality/valueIn this paper, a proposed uncertain SEIAR system dynamics model, related sensitivity analysis and ANFIS model are utilized to support different programs regarding policymaking and economic issues to face infectious diseases. The results could support the analysis of sensitivities, policies and economic activities.Highlights:A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors;Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities;Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years;An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them;A real case study is considered to evaluate the performances of the proposed models.

A Network Dynamics Model for the Transmission of COVID-19 in Diamond Princess and a Response to Reopen Large-Scale Public Facilities

Background: The current epidemic of COVID-19 has become the new normal. However, the novel coronavirus is constantly mutating. In public transportation or large entertainment venues, it can spread more quickly once an infected person is introduced. This study aims to discuss whether large public facilities can be opened and operated under the current epidemic situation. Methods: The dual Barabási–Albert (DBA) model was used to build a contact network. A dynamics compartmental modeling framework was used to simulate the COVID-19 epidemic with different interventions on the Diamond Princess. Results: The effect of isolation only was minor. Regardless of the transmission rate of the virus, joint interventions can prevent 96.95% (95% CI: 96.70–97.15%) of infections. Compared with evacuating only passengers, evacuating the crew and passengers can avoid about 11.90% (95% CI: 11.83–12.06%) of infections; Conclusions: It is feasible to restore public transportation services and reopen large-scale public facilities if monitoring and testing can be in place. Evacuating all people as soon as possible is the most effective way to contain the outbreak in large-scale public facilities.

Dynamic Simulation Analysis of Track Tension and the Influencing Factors of Deep-Sea Tracked Mining Vehicle

The deep-sea mining miner requires good passability to operate in complex and changeable terrain environment of the seabed. Among them, the track tension is an important factor to ensure the normal running of the vehicle. Aiming at the complex track tension problem on track link, the forces on each component of the tracked system were analyzed, and the theoretical formulas of track tension were established. The theoretical estimation and dynamic simulation of track tension in uniform speed were carried out by using the multibody dynamics model of the tracked vehicle, and the rationality of the theoretical estimation formula was verified. The influencing factors of track tension also were analyzed, and the significance of each factor on track was discussed by dynamic simulation. The results provided a theoretical basis for design of tracked vehicles.

Improving robotic impedance control performance employing a cascaded controller based on virtual dynamics model

We propose a cascaded impedance control algorithm based on a virtual dynamics model (VDM) to achieve robust and effective mechanical impedance for a robot interacting with unknown environments. This cascaded controller consists of an internal loop of virtual impedance control based on a VDM and an external loop of impedance reference control. The VDM-based virtual impedance control can achieve the same effect as the conventional admittance control; its intermediate output of force/torque serves as the input for the external loop reference impedance control. Therefore, this cascaded controller shows superior performance by combining the advantages of admittance control and impedance control. We evaluate the controller in multiple-contact experiments on a six-degrees of freedom (6-DOF) industrial robot manipulator. The result shows that under various contact situations such as soft and rigid surfaces and free space, the proposed method can rapidly track the target and effectively maintain stability. In the experiments conducted on the robot in contact with various environments, the proposed control method reduced the steady-state error by more than 20% compared with the conventional admittance control.