A perspective on early detection systems models for COVID-19 spreading

Detection of COVID-19 epidemic outbreak using machine learning

Background

The coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.

Objective

In this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.

Methods

We developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days.

Results

ML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection.

Conclusion

The study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic.

Trajectories of resilience and mental distress to global major disruptions

The COVID-19 pandemic has resulted in a major societal disruption, raising the question of how people can maintain or quickly regain their mental health (i.e., be resilient) during such times. Researchers have used the pandemic as a use case for studying resilience in response to a global, synchronously starting, and chronic set of stressors on the individual and societal level. Our review of this recent literature reveals that mental distress trajectories during the pandemic largely resemble mental distress responses to individual-level macro-stressors, except for a lower prevalence of recovery trajectories. Results suggest more resilient responses in older adults, but trajectories are less consistent for younger and older ages compared with middle-aged adults. We call for more research integrating state-of-the-art operationalizations of resilience and using these to study resilience over the lifespan.

Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision

In the context of global pandemic Coronavirus disease 2019 (COVID-19) that threatens life of all human beings, it is of vital importance to achieve early detection of COVID-19 among symptomatic patients. In this paper, a computer aided diagnosis (CAD) model Cov-Net is proposed for accurate recognition of COVID-19 from chest X-ray images via machine vision techniques, which mainly concentrates on powerful and robust feature learning ability. In particular, a modified residual network with asymmetric convolution and attention mechanism embedded is selected as the backbone of feature extractor, after which skip-connected dilated convolution with varying dilation rates is applied to achieve sufficient feature fusion among high-level semantic and low-level detailed information. Experimental results on two public COVID-19 radiography databases have demonstrated the practicality of proposed Cov-Net in accurate COVID-19 recognition with accuracy of 0.9966 and 0.9901, respectively. Furthermore, within same experimental conditions, proposed Cov-Net outperforms other six state-of-the-art computer vision algorithms, which validates the superiority and competitiveness of Cov-Net in building highly discriminative features from the perspective of methodology. Hence, it is deemed that proposed Cov-Net has a good generalization ability so that it can be applied to other CAD scenarios. Consequently, one can conclude that this work has both practical value in providing reliable reference to the radiologist and theoretical significance in developing methods to build robust features with strong presentation ability.

Multiwalled Carbon Nanotube-Based On-Body Patch Antenna for Detecting COVID-19-Affected Lungs

A novel rectangular patch antenna based on multiwall carbon nanotubes has been designed and developed for assisting the initial detection of COVID-19-affected lungs. Due to their highly conductive nature, each nanotube echoes electromagnetic waves in a unique manner, influencing the increase in bandwidth. The proposed antenna operates at 6.63, 7.291, 7.29, and 7.22 GHz with a higher bandwidth classified as an ultrawide band and can be used on a human body phantom model because of its flexibility and decreased radiation qualities. Flame retardant 4 is chosen as a substrate with a uniform thickness of 1.62 mm due to its inexpensive cost and excellent electrical properties. The maximum specific absorption rate of the proposed antenna is obtained as 1.77 W/kg for 10 g of tissues. For testing purposes, a model including all the known features of COVID-19-affected lungs is developed. The designed antenna exhibits excellent performance in free space, normal lungs, and affected lung environments. It might be utilized as a first screening device for COVID-19 patients, especially in resource-constrained areas where traditional medical equipment such as X-ray and computerized tomography scans are scarce.

Optimizing Cycle Time of Industrial Robotic Tasks with Multiple Feasible Configurations at the Working Points

Industrial robot applications should be designed to allow the robot to provide the best performance for increasing throughput. In this regard, both trajectory and task order optimization are crucial, since they can heavily impact cycle time. Moreover, it is very common for a robotic application to be kinematically or functionally redundant so that multiple arm configurations may fulfill the same task at the working points. In this context, even if the working cycle is composed of a small number of points, the number of possible sequences can be very high, so that the robot programmer usually cannot evaluate them all to obtain the shortest possible cycle time. One of the most well-known problems used to define the optimal task order is the Travelling Salesman Problem (TSP), but in its original formulation, it does not allow to consider different robot configurations at the same working point. This paper aims at overcoming TSP limitations by adding some mathematical and conceptual constraints to the problem. With such improvements, TSP can be used successfully to optimize the cycle time of industrial robotic tasks where multiple configurations are allowed at the working points. Simulation and experimental results are presented to assess how cost (cycle time) and computational time are influenced by the proposed implementation.

Analysis on the spatio-temporal characteristics of COVID-19 in mainland China

COVID-19 has brought many unfavorable effects on humankind and taken away many lives. Only by understanding it more profoundly and comprehensively can it be soundly defeated. This paper is dedicated to studying the spatial-temporal characteristics of the epidemic development at the provincial-level in mainland China and the civic-level in Hubei Province. Moreover, a correlation analysis on the possible factors that cause the spatial differences in the epidemic's degree is conducted. After completing these works, three different methods are adopted to fit the daily-change tendencies of the number of confirmed cases in mainland China and Hubei Province. The three methods are the Logical Growth Model (LGM), Polynomial fitting, and Fully Connected Neural Network (FCNN). The analysis results on the spatial-temporal differences and their influencing factors show that: (1) The Chinese government has contained the domestic epidemic in early March 2020, indicating that the number of newly diagnosed cases has almost zero increase since then. (2) Throughout the entire mainland of China, effective manual intervention measures such as community isolation and urban isolation have significantly weakened the influence of the subconscious factors that may impact the spatial differences of the epidemic. (3) The classification results based on the number of confirmed cases also prove the effectiveness of the isolation measures adopted by the governments at all levels in China from another aspect. It is reflected in the small monthly grade changes (even no change) in the provinces of mainland China and the cities in Hubei Province during the study period. Based on the experimental results of curve-fitting and considering the time cost and goodness of fit comprehensively, the Polynomial(Degree = 18) model is recommended in this paper for fitting the daily-change tendency of the number of confirmed cases.

The impact of the COVID-19 pandemic on the safety management in Italian Seveso industries

The paper discusses the impact of the COVID-19 pandemic on the Italian chemical and process industries, where Directive 2012/18/EU Seveso III, for the control of Major Accident Hazard (MAH), is enforced. The Safety Management System (SMS) for the control of MAH, which has been mandatory for 20 years in Italian Seveso Establishments, has been highly stressed by the external pressure, related in some way to the COVID-19 pandemic. Fairly, most companies, in particular in oil and gas sectors, have demonstrated an adequate capability to reconcile operation continuity and health requirements. This experience is providing the establishment operators and the regulators with valuable suggestions for the improvements of the SMS-MAH. Within this framework, an innovative organisational resilience model is proposed, aiming at the development of a higher capability to face future new crisis. The current SMS-MAH already includes some basic pillars to enhance resilience, which were valuable during the pandemic crisis, but a full and rationale development is still needed. Starting from the first pandemic phase experience, this paper presents a novel tool to assess the degree of "resilience" of a SMS-MAH. It is based on a questionnaire, featuring 25 questions grouped into eight items, according to the typical SMS-MAH structure. A two level AHP model has been developed in order to define the weights to be assigned to each point. The AHP panel included industrial practitioners, regulators, authorities and researchers. The results are based on the COVID-19 experience and consequently the developed model is tailored to face health emergencies, but the approach may be easily transferred to other external crises.

Trends and Opportunities of Tertiary Education in Safety Engineering Moving towards Safety 4.0

Industry and related work and workplaces are constantly changing as a result of the implementation of new technologies, substances and work processes, changes in the composition of the workforce and the labor market, and new forms of employment and work organization. The implementation of new technologies represents certain ambivalence. Next to the positive impact on workers’ health, new risks and challenges can arise in the area of process and occupational safety and health of people at work. On these bases, it follows the need for predicting and handling the new risks, in order to ensure safe and healthy workplaces in the future. The aim of most forecasting studies is not only to identify new emerging risks, but also to foresee changes that could affect occupational safety and health. However, a number of questions still require proper investigation, i.e., “What impact do new emerging risks have on tertiary education in the area of Safety engineering? Has tertiary education already reacted to progress in science and research and does it have these innovations in its syllabus? How are tertiary graduates prepared for the real world of new technologies?” This paper represents a first attempt in the literature to provide answers to the raised questions, by a survey approach involving academics, Health Safety and Environment (HSE) industrial experts and university students in the Czech Republic. Even if statistical evaluation is limited to a single Country and to a small sample size, the obtained results allow suggesting practical recommendations that can contribute to ensuring new challenges in the area of education by addressing relevant culture issues needed to support new workplace realities according to the newly defined Safety 4.0.