COVID-19 medical waste transportation risk evaluation integrating type-2 fuzzy total interpretive structural modeling and Bayesian network

Study on sustainable transportation mode of medical waste in big city hospitals based on multi-agent

BackgroundMedical waste should be collected, classified, and transported to the treatment plant within 48 h. If it is not disposed of in time, it will cause cross-infection, increasing the risk of disease transmission and environmental pollution. How to reasonably plan transportation routes to ensure that the medical waste can be transported to the treatment plant in time is very important.ObjectiveThere are usually two modes of transportation, the fastest speed and shortest path, how to reasonably plan the transportation scheme so that medical waste can be transported to the treatment plant for disposal in the specified time is the main purpose of this article.MethodsThe multi-agent modeling method is adopted. AnyLogic simulation software is used to model the transportation routes of 118 Grade III hospitals and 2 treatment plants in Beijing under the two transportation modes of fastest speed and shortest path.ResultsBased on the traffic index in Beijing, the speed range of 20 km/h-32 km/h is set up and divided into 4 parts and 24 levels with 0.5 km/h as the unit, and the 24 levels of medical waste transportation data set is formed. The key speed nodes of 21 km/h, 24 km/h and 29.5 km/h are identified.ConclusionsThe medical waste transportation model and transport data set formed in this paper have enriched the theory and data basis of medical waste transportation management. The key speed nodes of transportation model selection have important practical significance for the transportation management decision of medical waste in big cities.

Experimental study on recycling rubber to increase the impact resistance of cement mortar

The COVID-19 pandemic has led to a surge in medical waste generation, posing hazards to both the environment and global health. The impacts of the COVID-19 pandemic's medical waste hazard may persist long after the pandemic itself subsides. Improper disposal of medical waste can contaminate environment, posing risks to ecosystems and public health. Discarded medical rubber gloves, for example, can become a source of infection, improper disposal of these gloves can escalate the spread of infectious diseases and increase the risk of transmission of the virus to the general public. This study proposes an innovative and sustainable method to reinforce cement mortar by adding recycled glove rubber as an additive to cement mortar to increase its resistance to impact loads. This study conducted uniaxial compression tests, separating hopkinson pressure bar (SHPB) experiments and SEM observations to evaluate the quasi-static compressive strength and dynamic stress of recycled rubber fiber mortar (RRFM) with varying recycled rubber fiber (RRF) contents (0, 1%, 2%, 3%). Strain curves, dynamic increase factor (DIF), energy absorption rules, failure modes, and microstructure of RRFM mixtures. The experimental results demonstrate that with the addition of RRF, the dynamic stress-strain curve flattens and the peak strain gradually increases. The RRFM sample shows stronger toughness. In comparison to regular cement mortar (NM), RRFM has a higher DIF and specific absorbed energy, a faster increase in dynamic compressive strength, and the ability to absorb more energy per unit volume. Under the same impact load, RRFM has fewer and smaller cracks than NM. Scanning electron microscopy (SEM) testing also observed that RRF formed a strong connection pattern with the cement mortar matrix.

Risk Management for Whole-Process Safe Disposal of Medical Waste: Progress and Challenges

Over the past decade, the global outbreaks of SARS, influenza A (H1N1), COVID-19, and other major infectious diseases have exposed the insufficient capacity for emergency disposal of medical waste in numerous countries and regions. Particularly during epidemics of major infectious diseases, medical waste exhibits new characteristics such as accelerated growth rate, heightened risk level, and more stringent disposal requirements. Consequently, there is an urgent need for advanced theoretical approaches that can perceive, predict, evaluate, and control risks associated with safe disposal throughout the entire process in a timely, accurate, efficient, and comprehensive manner. This article provides a systematic review of relevant research on collection, storage, transportation, and disposal of medical waste throughout its entirety to illustrate the current state of safe disposal practices. Building upon this foundation and leveraging emerging information technologies like Internet of Things (IoT), cloud computing, big data analytics, and artificial intelligence (AI), we deeply contemplate future research directions with an aim to minimize risks across all stages of medical waste disposal while offering valuable references and decision support to further advance safe disposal practices.

A comprehensive protocol for evaluating health, safety, and environmental risks of hospital solid waste through FMEA technique

This protocol outlines a comprehensive approach to evaluating hospital solid waste levels and assessing associated health, safety, and environmental (HSE) risks using the Failure Mode and Effects Analysis (FMEA) methodology. The study focuses on Imam Khomeini Hospital (RA) and employs both quantitative and qualitative methods. Over a 3-month period, waste production and potential risks are assessed, with specific attention to household, infectious, medicinal, and sharps waste. Through FMEA, potential failure modes and associated risks in waste management sectors are identified, enabling targeted interventions for risk mitigation. The protocol emphasizes the importance of aligning waste management practices with international standards and highlights the need for comprehensive training, awareness campaigns, and effective waste management methods to ensure the safety and environmental responsibility of hospital waste management practices.

An application of BWM for risk control in reverse logistics of medical waste

The pollution posed by medical waste complicate the procedures of medical waste logistics (MWL), and the increasingly frequent occurrence of public health emergencies has magnified the risks posed by it. In this study, the authors established an index of the factors influencing the risks posed by MWL along five dimensions: the logistics business, emergency capacity, equipment, personnel, and management. The best-worst case method was used to identify the critical risk-related factors and rank them by importance. Following this, we assessed the risk posed by MWL in four major cities in China as an example and propose the corresponding measures of risk control. The results showed that the linking of business processes was the most important factor influencing the risk posed by MWL. The other critical risk-related factors included the location of the storage site, the capacity for emergency transportation, measures to manage emergencies, and the safety of packaging. Of the cities considered, Beijing was found to be a high-risk city, and its MWL needed to be improved as soon as possible in light of the relevant critical risks. Shanghai, Guangzhou, and Shenzhen were evaluated as general-risk cities, which meant that the risks of MWL were not a priority in these areas, and the other goals of urban development should be comprehensively considered during the long-term planning for MWL in these municipalities.

Research on transportation management model of COVID-19 medical waste: a case study in Beijing, China

During the COVID-19 pandemic, disposable masks, protective clothing, gloves, and nasopharyngeal swabs collected by nucleic acid testing formed a large amount of medical waste. Medical waste has strict temporary storage time requirements in hospitals, which need to be transported to medical waste disposal plants within the specified time. However, as most of disposal plants are far away from downtown, they also need to be responsible for the transportation and disposal of medical waste in many hospitals, and put forward higher requirement for transportation routes. Rapid and safe disposal of all types of medical waste generated by COVID-19 is crucial to the prevention and control of the epidemic. This paper designs the transportation route optimization model using Anylogic simulation software based on the regional distribution of 118 tertiary hospitals and 2 large medical waste disposal plants in Beijing, China. At the same time, transportation routes of 118 tertiary hospitals in the morning peak, evening peak, all-day, and ordinary periods were simulated based on the Beijing traffic index in 2017. On this basis, through the analysis of the simulation data, the selection of medical waste transport routes for 118 tertiary hospitals in the morning peak, evening peak, all day, and ordinary periods is further clarified, so as to ensure that medical waste can be transported to the medical waste disposal plant in the shortest time. The shortest path and fastest speed transport mode, medical waste transport data set, and the selection of transport mode of 118 tertiary hospitals formed by this research provide certain reference experience for the rapid and safe transport of medical waste during the epidemic period, and also provides corresponding data support for medical waste transportation management in the post-epidemic era and medical waste transportation decision-making when facing major public health problems.

Interval type-2 Fuzzy control and stochastic modeling of COVID-19 spread based on vaccination and social distancing rates

BACKGROUND AND OBJECTIVE

Besides efforts on vaccine discovery, robust and intuitive government policies could also significantly influence the pandemic state. However, such policies require realistic virus spread models, and the major works on COVID-19 to date have been only case-specific and use deterministic models. Additionally, when a disease affects large portions of the population, countries develop extensive infrastructures to contain the condition that should adapt continuously and extend the healthcare system's capabilities. An accurate mathematical model that reasonably addresses these complex treatment/population dynamics and their corresponding environmental uncertainties is necessary for making appropriate and robust strategic decisions.

METHODS

Here, we propose an interval type-2 fuzzy stochastic modeling and control strategy to deal with the realistic uncertainties of pandemics and manage the size of the infected population. For this purpose, we first modify a previously established COVID-19 model with definite parameters to a Stochastic SEIAR (S2EIAR) approach with uncertain parameters and variables. Next, we propose to use normalized inputs, rather than the usual parameter settings in the previous case-specific studies, hence offering a more generalized control structure. Furthermore, we examine the proposed genetic algorithm-optimized fuzzy system in two scenarios. The first scenario aims to keep infected cases below a certain threshold, while the second addresses the changing healthcare capacities. Finally, we examine the proposed controller on stochasticity and disturbance in parameters, population sizes, social distance, and vaccination rate.

RESULTS

The results show the robustness and efficiency of the proposed method in the presence of up to 1% noise and 50% disturbance in tracking the desired size of the infected population. The proposed method is compared to Proportional Derivative (PD), Proportional Integral Derivative (PID), and type-1 fuzzy controllers. In the first scenario, both fuzzy controllers perform more smoothly despite PD and PID controllers reaching a lower mean squared error (MSE). Meanwhile, the proposed controller outperforms PD, PID, and the type-1 fuzzy controller for the MSE and decision policies for the second scenario.

CONCLUSIONS

The proposed approach explains how we should decide on social distancing and vaccination rate policies during pandemics against the prevalent uncertainties in disease detection and reporting.