Putting the world back to work: An expert system using big data and artificial intelligence in combating the spread of COVID-19 and similar contagious diseases

Academic and healthcare efforts from Cessation to complete resumption of professional football tournaments during COVID-19 pandemic: A narrative review

The Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus led to over 626 million infections and 6.5 million deaths worldwide and forced to cancel or postpone several sporting events. Effective control techniques are therefore urgently required to avoid COVID-19 spread at these local and global events. This narrative review addressed the healthcare and research efforts on the intersections between COVID-19 and major professional sports leagues worldwide, with special reference to the FIFA World Cup football 2022. This explained how the broader transformation of COVID-19 from being a potential risk to an urgent pandemic public health emergency, caused the world of Football to halt between February and March 2020. This review could add to the growing literature on the importance of scientific research in understanding the relationship between mass sports events and COVID-19 trajectory, concerning studies conducted globally and particularly for the recommencement of major professional football competitions. The information outlined in the article may help sports organizations understand the risks associated with sports and their settings and improve their preparedness for future events under unprecedented circumstances. There were tremendous global healthcare and research efforts to deal with this unprecedented pandemic. The successful FIFA World Cup football tournament was an indicator of the success of these efforts.

Mental workload assessments of aerial photography missions performed by novice unmanned aerial vehicle operators

BACKGROUND

Mental workload is one of the important variables in understanding human performance in drone operation.

OBJECTIVE

To test the effects of gender, age group, flight route, and altitude on the flight performance and mental workload of the novice drone operators.

METHODS

Ten male and 10 female participants without prior drone operating experience joined. They were split into two age groups. After attending a training, the participants operated a drone to perform photo taking missions under flight route and altitude conditions. The weighted NASA task-load-index (TLX), modified Cooper-Harper (MCH) scale, heart rate, and interbeat interval were measured to assess the mental workload of the participants. Flight time to complete the mission was adopted to indicate flight performance.

RESULTS

The effect of age group was significant (p <  0.05) on flight time, weighted TLX score, and MCH score. Flight route and altitude were not significant on the two subjective ratings and two cardiac measures.

CONCLUSION

The flight performance of younger participants was significantly better than that of their older counterpart. The effects of both the flight route and altitude on the perceived mental workload of the drone operators were insignificant. Both the weighted NASA TLX and MCH scales were appropriate in measuring the mental workload of the novice drone operators.

A Robust Design-Based Expert System for Feature Selection and COVID-19 Pandemic Prediction in Japan

Expert systems are frequently used to make predictions in various areas. However, the practical robustness of expert systems is not as good as expected, mainly due to the fact that finding an ideal system configuration from a specific dataset is a challenging task. Therefore, how to optimize an expert system has become an important issue of research. In this paper, a new method called the robust design-based expert system is proposed to bridge this gap. The technical process of this system consists of data initialization, configuration generation, a genetic algorithm (GA) framework for feature selection, and a robust mechanism that helps the system find a configuration with the highest robustness. The system will finally obtain a set of features, which can be used to predict a pandemic based on given data. The robust mechanism can increase the efficiency of the system. The configuration for training is optimized by means of a genetic algorithm (GA) and the Taguchi method. The effectiveness of the proposed system in predicting epidemic trends is examined using a real COVID-19 dataset from Japan. For this dataset, the average prediction accuracy was 60%. Additionally, 10 representative features were also selected, resulting in a selection rate of 67% with a reduction rate of 33%. The critical features for predicting the epidemic trend of COVID-19 were also obtained, including new confirmed cases, ICU patients, people vaccinated, population, population density, hospital beds per thousand, middle age, aged 70 or older, and GDP per capital. The main contribution of this paper is two-fold: Firstly, this paper has bridged the gap between the pandemic research and expert systems with robust predictive performance. Secondly, this paper proposes a feature selection method for extracting representative variables and predicting the epidemic trend of a pandemic disease. The prediction results indicate that the system is valuable to healthcare authorities and can help governments get hold of the epidemic trend and strategize their use of healthcare resources.

Telemedicine System Applicability Using Drones in Pandemic Emergency Medical Situations

Drones have evolved significantly in recent years, acquiring greater autonomy and carrier capacity. Therefore, drones can play a substantial role in civil medicine, especially in emergency situations or for the detection and monitoring of disease spread, such as during the COVID-19 pandemic. The aim of this paper is to present the real possibilities of using drones in field rescue operations, as well as in nonsegregated airspace, in order to obtain solutions for monitoring activities and aerial work in support of the public health system in crisis situations. The particularity of our conceptual system is the use of a “swarm” of fast drones for aerial reconnaissance that operate in conjunction, thus optimizing both the search and identification time while also increasing the information area and the operability of the system. We also included a drone with an RF relay, which was connected to a hub drone. If needed, a carrier drone with medical supplies or portable devices can be integrated, which can also offer two-way audio and video communication capabilities. All of these are controlled from a mobile command center, in real time, connected also to the national dispatch center to shorten the travel time to the patient, provide support with basic but life-saving equipment, and offer the opportunity to access remote or difficult-to-reach places. In conclusion, the use of drones for medical purposes brings many advantages, such as quick help, shortened travel time to the patient, support with basic but life-saving equipment, and the opportunity to access remote or difficult-to-reach places.

Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand "ThaiChana"

The continuous rise of the COVID-19 Omicron cases despite the vaccination program available has been progressing worldwide. To mitigate the COVID-19 contraction, different contact tracing applications have been utilized such as Thai Chana from Thailand. This study aimed to predict factors affecting the perceived usability of Thai Chana by integrating the Protection Motivation Theory and Technology Acceptance Theory considering the System Usability Scale, utilizing deep learning neural network and random forest classifier. A total of 800 respondents were collected through convenience sampling to measure different factors such as understanding COVID-19, perceived severity, perceived vulnerability, perceived ease of use, perceived usefulness, attitude towards using, intention to use, actual system use, and perceived usability. In total, 97.32% of the deep learning neural network showed that understanding COVID-19 presented the most significant factor affecting perceived usability. In addition, random forest classifier produced a 92% accuracy with a 0.00 standard deviation indicating that understanding COVID-19 and perceived vulnerability led to a very high perceived usability while perceived severity and perceived ease of use also led to a high perceived usability. The findings of this study could be considered by the government to promote the usage of contact tracing applications even in other countries. Finally, deep learning neural network and random forest classifier as machine learning algorithms may be utilized for predicting factors affecting human behavior in technology or system acceptance worldwide.

Returning to Work after the COVID-19 Pandemic Earthquake: A Systematic Review

Background: The ongoing SARS-CoV-2 pandemic has disrupted life and work habits and has produced landmark changes worldwide. This systematic review aimed to analyse the management of Return to Work (RTW) by work organisations following the virus spread. Methods: A selection of 2477 papers, using string research on PubMed, Embase, Web of Science and Scopus from January 2020 to October 2021, were analysed. Results: Fifty-one articles were finally included, and the results obtained were discussed from three different points of view. Twenty articles concerning ‘Remodelling of Work Organization’ proposed some model strategies for resumption to work. Twenty-one papers, including ‘Clinical Evaluation of Workers’, mostly explored the psychosocial impact of returned workers. Finally, twelve articles explored the best ‘Testing Strategies related to RTW’. Despite the heterogeneity of included articles, several interesting approaches have emerged in managing RTW. Conclusions: The reported experiences could help to develop an RTW model for COVID-19 and future pandemics.

Impact of Using Drones in Emergency Medicine: What Does the Future Hold?

The use of unmanned aerial vehicles or "drones" has expanded in the last decade, as their technology has become more sophisticated, and costs have decreased. They are now used routinely in farming, environmental surveillance, public safety, commercial product delivery, recreation, and other applications. Health-related applications are only recently becoming more widely explored and accepted. The use of drone technology in emergency medicine is especially promising given the need for a rapid response to enhance patient outcomes. The purpose of this paper is to describe some of the main current and expanding applications of drone technology in emergency medicine and to describe challenges and future opportunities. Current applications being studied include delivery of defibrillators in response to out-of-hospital cardiac arrest, blood and blood products in response to trauma, and rescue medications. Drones are also being studied and actively used in emergency response to search and rescue operations as well as disaster and mass casualty events. Current challenges to expanding their use in emergency medicine and emergency medical system (EMS) include regulation, safety, flying conditions, concerns about privacy, consent, and confidentiality, and details surrounding the development, operation, and maintenance of a medical drone network. Future research is needed to better understand end user perceptions and acceptance. Continued technical advances are needed to increase payload capacities, increase flying distances, and integrate drone networks into existing 9-1-1 and EMS systems. Drones are a promising technology for improving patient survival, outcomes, and quality of life, particularly for those in areas that are remote or that lack funds or infrastructure. Their cost savings compared with ground transportation alone, speed, and convenience make them particularly applicable in the field of emergency medicine. Research to date suggests that use of drones in emergency medicine is feasible, will be accepted by the public, is cost-effective, and has broad application.