Spread Of Contagious Diseases Research Articles

Real-Time Facial Cover Detection System based Deep Convolution Neural Network with Proposed Activation Function

The ongoing global contagion has highlighted the importance of effective preventive measures such as wearing face masks in public spaces. In this study, we suggest a deep learning-based approach for real-time facial covering detection to aid in enforcing mask-wearing protocols. Our system utilizes deep learning networks (CNNs) to automatically detect whether individuals in images or video streams are wearing mask or not. The suggested system includes of 3 main stages: face detection, facial cover detection, and real-time monitoring. Firstly, faces are localized in the input image or video frame using a proposed face detection model. Then, the detected faces are fed into a proposed CNN model for mask classification, which determines whether each face is covered with a mask or not. Finally, the system will provide real-time monitoring and alerts authorities or stakeholders about non-compliance with mask wearing guidelines. We appraise the execution of our system on publicly available datasets and demonstrate its effectiveness in accurately detecting face masks in various scenarios. Additionally, we discuss the challenges and limitations of deploying such as system in real-world settings, including issues related to privacy, bias, and scalability. Overall, our proposed facial covering detection system offers a viable solution for automated monitoring and enforcement of face mask policies, contributing to public health efforts in mitigating the spread of contagious diseases.

Tracing wide-spread infectious contacts using GeoSpark in preventive medicine

A threat to governments and the medical community globally are infectious illness outbreaks such as COVID-19, Spanish flu, and Ebola, which kill millions of people. Furthermore, because people are afraid to go to their employment, infectious diseases hinder economic progress. Therefore, it is imperative to implement interventions to lessen the spread of infectious diseases. From this angle, the practice of tracking contacts might be viewed as a mitigation strategy to stop the spread of contagious diseases. As this is going on, users’ trace data sets are growing exponentially larger over time, making it difficult to conduct contact tracing queries over them. In this paper, a novel Spark contact-tracing query processing method is proposed. This method determines if users are suspected cases by analyzing their paths based on two variables: nearby social distance and exposure time. Additionally, the developed method makes full use of the Spark framework to address scalability issues and effectively respond to contact tracing inquiries over a wide range of trajectories.

Interacting particle models on the impact of spatially heterogeneous human behavioral factors on dynamics of infectious diseases.

Human behaviors have non-negligible impacts on spread of contagious disease. For instance, large-scale gathering and high mobility of population could lead to accelerated disease transmission, while public behavioral changes in response to pandemics may effectively reduce contacts and suppress the peak of the outbreak. In order to understand how spatial characteristics like population mobility and clustering interplay with epidemic outbreaks, we formulate a stochastic-statistical environment-epidemic dynamic system (SEEDS) via an agent-based biased random walk model on a two-dimensional lattice. The "popularity" and "awareness" variables are taken into consideration to capture human natural and preventive behavioral factors, which are assumed to guide and bias agent movement in a combined way. It is found that the presence of the spatial heterogeneity, like social influence locality and spatial clustering induced by self-aggregation, potentially suppresses the contacts between agents and consequently flats the epidemic curve. Surprisedly, disease responses might not necessarily reduce the susceptibility of informed individuals and even aggravate disease outbreak if each individual responds independently upon their awareness. The disease control is achieved effectively only if there are coordinated public-health interventions and public compliance to these measures. Therefore, our model may be useful for quantitative evaluations of a variety of public-health policies.

Investigation of the effects of indoor air stability on the airborne transmission of droplets from violent respiratory events

Indoor air stability is a condition under which the indoor temperature gradients can affect the trajectories of constant breathing flows. However, it remains unclear how indoor air stability affects the airborne contaminants from violent respiratory events such as coughing. Here, we report a study of the dispersion of airborne droplets from coughing under different indoor air stability conditions. The distributions of droplets from coughing processes under stable and unstable conditions were compared. The results revealed that the stable conditions confined the coughed droplets to the breathing zone of the droplet host, whereas the unstable conditions intensified the dispersion of droplets and reduced their local concentration. The dimensionless vertical distance of the droplet cloud under the stable condition was 4 times longer than that of the unstable condition at t = 15 s. In addition, we observed that high ventilation rate caused an intense mixing of the indoor air, thus weakening the effect of indoor air stability on the propagation of droplet cloud. Personal exposure of the stable condition was 6.5 times larger than that of the unstable condition at a ventilation rate of 2.1 ACH, while it decreased to lower than half of that of the unstable condition at 7.1 ACH and 12.3 ACH. Personal exposure to the indoor environment should be assessed by simultaneously considering the indoor air stability conditions and ventilation rates. This study has provided insights into the transmission of cough droplets in indoor environments and has practical significance for preventing the spread of contagious diseases.

Parameter Tuning of Agent-Based Models: Metaheuristic Algorithms

When it comes to modelling complex systems using an agent-based approach, there is a problem of choosing the appropriate parameter optimisation technique. This problem is further aggravated by the fact that the parameter space in complex agent-based systems can have a large dimension, and the time required to perform numerical experiments can be large. An alternative approach to traditional optimisation methods are the so-called metaheuristic algorithms, which provide an approximate solution in an acceptable time. The purpose of this study is to compare various metaheuristic algorithms for parameter tuning and to analyse their effectiveness applied to two agent-based models with different complexities. In this study, we considered commonly used metaheuristic algorithms for agent-based model optimisation: the Markov chain Monte Carlo method, the surrogate modelling approach, the particle swarm optimisation algorithm, and the genetic algorithm, as well as the more novel chaos game optimisation algorithm. The proposed algorithms were tested on two agent-based models, one of which was a simple toy model of the spread of contagious disease, and the other was a more complex model of the circulation of respiratory viruses in a city with 10 million agents and 26 calibrated parameters.

Category and Syntactic Functions in the Collocation of the Words Wabah and Pandemic: A Corpus Linguistics Overview

The COVID-19 pandemic has transformed many aspects of life, including language. Words such as 'outbreak' and 'pandemic' frequently appear in COVID-19-related news, and both carry similar meanings in referring to the rapid spread of contagious diseases that affect a large number of people in extensive areas. Although these terms are often used interchangeably, it remains unclear how they are employed in various sentence contexts. This research aims to analyze the categories and syntactic functions of the collocation of the words 'outbreak' and 'pandemic' in the Nusantara Corpus using a corpus linguistics method. The study utilizes a combined quantitative and qualitative approach to examine the collocation of 'outbreak' and 'pandemic' in the Nusantara Linguistic Corpus. Data analysis is based on corpus linguistic theory and collocation theory. The research findings indicate that the collocation of 'outbreak' and 'pandemic' words is identified in the Nusantara corpus. The frequency of the word 'outbreak' reaches 195 occurrences, while 'pandemic' appears 1,229 times. Therefore, it can be concluded that the collocation of 'outbreak' and 'pandemic' words can be syntactically categorized as verbs, nouns, adjectives, and prepositions. Moreover, in terms of syntactic functions, the collocation of 'outbreak' and 'pandemic' is classified as subjects, objects, complements, and adverbials. This research can be implemented in the use of 'outbreak' and 'pandemic' collocations in sentence construction by considering categories and syntactic functions to avoid semantic errors based on the correct context.

Textile‐based high‐gain bow‐tie antenna using metamaterials for medical applications

AbstractThis paper presents a high‐gain wearable circularly polarized bow‐tie antenna (WCPBTA) with metamaterial unit‐cells and 5 pins (5Ps) technique for telemedicine applications such as telemonitoring the elderly or the patients especially for emergency conditions of contagious diseases. The frequency range of the proposed antenna is 5.725–5.850 GHz, which belongs to the unlicensed‐national information infrastructure (U‐NII‐3) for the industrial, scientific, and medical (ISM) sub‐channels. A light‐weight and flexible felt with dielectric constant εr = 3, thickness 1.27 mm, and tan (δ) = 0.0095 is used as the substrate for patient comfort and wearability. The overall dimensions of the proposed antenna are 64 × 62 × 1.27 mm3 or 0.102 λg3 at 5.8 GHz. The maximum simulated gain at 5.8 GHz is 8.25 dB, which is more than 4 dB compared to that of the original bow‐tie antenna. Besides, the axial ratio (AR) and the specific absorption rate (SAR) are also analyzed, which meet the perfect requirement for medical applications. The fabricated prototype of the antenna shows good compatibility between simulation and measurement results. These characteristics make the proposed WCPBTA a good choice for wireless body area networks (WBANs) in telemonitoring applications especially with the aim of preventing the spread of contagious diseases.

A YOLO-based deep learning model for Real-Time face mask detection via drone surveillance in public spaces

Automating face mask detection in public areas is paramount to maintaining public health, especially in the context of the COVID-19 pandemic. Utilization of technologies such as deep learning and computer vision systems enables effective monitoring of mask compliance, thereby minimizing the risk of virus spread. Real-time detection helps in prompt intervention for and enforcement of the use of masks, thereby preventing potential outbreaks and ensuring compliance with public health guidelines. This method helps save human resources and makes the reinforcement of wearing masks in public areas consistent and objective. Automatic detection of face masks serves as a key tool for preventing the spread of contagious diseases, protecting public health, and creating a safer environment for every person. This study addresses the challenges of real-time face mask detection via drone surveillance in public spaces, with reference to three categories: wearing of mask, incorrect wearing of mask, and no mask. Addressing these challenges entails an efficient and robust object detection and recognition algorithm. This algorithm can deal with a crowd of multiple faces via a mobile camera carried by a mini drone, and performs real-time video processing. Accordingly, this study proposes a You Only Look Once (YOLO) based deep learning C-Mask model for real-time face mask detection and recognition via drone surveillance in public spaces. The C-Mask model aims to operate within a mini drone surveillance system and provide efficient and robust face mask detection. The C-Mask model performs preprocessing, feature extraction, feature generation, feature enhancement, feature selection, and multivariate classification tasks for each face mask detection cycle. The preprocessing task prepares the training and testing data in the form of images for further processing. The feature extraction task is performed using a Convolutional Neural Network (CNN). Moreover, Cross-Stage Partial (CSP) DarkNet53 is used to improve the feature extraction and to facilitate the model’s object detection ability. A data augmentation algorithm is used for feature generation to enhance the model’s training robustness. The feature enhancement task is performed by applying the Path Aggregation Network (PANet) and Spatial Pyramid Pooling Network (SPPNet) algorithms, which are deployed to enhance the extracted and generated features. The classification task is performed through multi-label classification, wherein each object in an image can belong to multiple classes simultaneously, and the network generates a grid of bounding boxes and corresponding confidence scores for each class. The YOLO-based C-Mask model testing is performed by experimenting with various face mask detection scenarios and with varying mask colors and types, to ensure the efficiency and robustness of the proposed model. The C-Mask model test results show that this model can correctly and effectively detect face masks in real-time video streams under various conditions with an overall accuracy of 92.20%, precision of 92.04, recall of 90.83%, and F1-score of 89.95%, for all the three classes. These high scores have been obtained despite mini drone mobility and camera orientation adjustment substantially affecting face mask detection performance.

PalmSpace: Leveraging the palm for touchless interaction on public touch screen devices

Touchscreen is the primary solution to interact with public devices such as Automated Teller Machines (ATMs). However, the touch modality raises health concerns since users have to touch the screens, and therefore risking the spread of contagious diseases. We design PalmSpace, an alternate input technique leveraging users’ hand palms to interact with public devices. With Palmspace, UI elements are mapped onto the users’ palms and can be accessed by touching various locations directly on the palm. We conduct a series of user studies to evaluate several design options, such as interface layout, item size, preferred item location, and suitable feedback for items. Based on the results, we design PalmSpace and compare its performance with mid-air input. We show that PalmSpace is a potential solution to interact with public devices without using their touchscreen. We conclude with design guidelines for using the palm as an alternative input space for touchscreen devices.

Understanding telemedicine strategies through field-based observations, interviews and benchmarking activities

The imperative for telehealth and telemedicine has become increasingly pronounced in addressing and alleviating health inequalities exacerbated by provider shortages and limited access to healthcare, particularly in underserved regions. The pandemic laid bare the vulnerabilities within traditional healthcare models, prompting a paradigmatic shift towards more resilient and inclusive systems. Telehealth has become a critical tool for connecting patients with healthcare providers, especially in areas plagued by shortages of physicians and specialists. In a world where geographical barriers often limit access to medical expertise, telehealth serves as a bridge, enabling patients to consult with a diverse array of doctors and specialists without the burdensome constraints of time-consuming and expensive travel. Moreover, telemedicine has proven instrumental in mitigating the shortage of healthcare personnel by empowering them to deliver remote care. This not only optimizes resource allocation but also enables healthcare professionals to reach patients in remote or rural areas where physical presence might be impractical. The significance of this development in a post-COVID-19 world is underscored by the imperative to build more resilient, adaptable, and equitable healthcare systems.The paper delves into the multifaceted impacts of this revolution, shedding light on its far-reaching consequences for patient care, healthcare systems, and the broader healthcare landscape, especially for small medical practices through field based-observations. Beyond the reactive curative model that has traditionally characterized healthcare, telehealth is propelling a shift towards a proactive, preventive approach. This shift is not only essential for managing and preventing the spread of contagious diseases but also for fostering a more comprehensive and patient-centric approach to healthcare.

Modeling and Visualizing the Dynamic Spread of Epidemic Diseases—The COVID-19 Case

Our aim is to provide an insight into the procedures and the dynamics that lead the spread of contagious diseases through populations. Our simulation tool can increase our understanding of the spatial parameters that affect the diffusion of a virus. SIR models are based on the hypothesis that populations are “well mixed”. Our model constitutes an attempt to focus on the effects of the specific distribution of the initially infected individuals through the population and provide insights, considering the stochasticity of the transmission process. For this purpose, we represent the population using a square lattice of nodes. Each node represents an individual that may or may not carry the virus. Nodes that carry the virus can only transfer it to susceptible neighboring nodes. This important revision of the common SIR model provides a very realistic property: the same number of initially infected individuals can lead to multiple paths, depending on their initial distribution in the lattice. This property creates better predictions and probable scenarios to construct a probability function and appropriate confidence intervals. Finally, this structure permits realistic visualizations of the results to understand the procedure of contagion and spread of a disease and the effects of any measures applied, especially mobility restrictions, among countries and regions.

Consistent Healthcare Safety Recommendation System for Preventing Contagious Disease Infections in Human Crowds

The recent impact of COVID-19, as a contagious disease, led researchers to focus on designing and fabricating personal healthcare devices and systems. With the help of wearable sensors, sensing and communication technologies, and recommendation modules, personal healthcare systems were designed for ease of use. More specifically, personal healthcare systems were designed to provide recommendations for maintaining a safe distance and avoiding contagious disease spread after the COVID-19 pandemic. The personal recommendations are analyzed based on the wearable sensor signals and their consistency in sensing. This consistency varies with human movements or other activities that hike/cease the sensor values abruptly for a short period. Therefore, a consistency-focused recommendation system (CRS) for personal healthcare (PH) was designed in this research. The hardware sensing intervals for the system are calibrated per the conventional specifications from which abrupt changes can be observed. The changes are analyzed for their saturation and fluctuations observed from neighbors within the threshold distance. The saturation and fluctuation classifications are performed using random forest learning to differentiate the above data from the previously sensed healthy data. In this process, the saturated data and consistency data provide safety recommendations for the moving user. The consistency is verified for a series of intervals for the fluctuating sensed data. This alerts the user if the threshold distance for a contagious disease is violated. The proposed system was validated using a prototype model and experimental analysis through false rates, data analysis rates, and fluctuations.

Recent progress in the conversion of agricultural waste into functional materials

AbstractAgricultural waste is enriched with a variety of environmentally friendly materials that can potentially boost economic growth, reduce the spread of contagious diseases, and serve as a carbon-free renewable energy bioresource. Biopolymers produced from agricultural waste have a range of applications in medicine, agriculture, pharmaceutics, and industrial factories. The chemical extraction of biopolymers from biomass requires a series of alternating alkali, acid, and alkali treatments at controlled temperatures. Chemical extraction of plant-based biopolymers requires elevated temperatures (70–100°C), while for animal and sea organism-based biopolymers, moderate temperatures of 25–60°C are used. The obtained biopolymers are functionalized into various materials for application in a wide range of industries. The reported functional materials are loaded with inorganic nanomaterials, plant extracts, and organic compounds, which resulted in a synergistic effect and enhanced activity of the materials. Several researchers have synthesized biopolymers with synthetic polymers to improve their bioavailability, tensile strength, shelf life, and UV adsorption. This review article reports the extraction techniques of biopolymers from agricultural waste and their application in wound healing, water treatment, food storage, passive cooling, and cosmetics. The dearth of scientific articles on the applications of biopolymers generated from agricultural waste produced from food crops grown in Africa is a motivation for the present compilation.

Beyond the Frontlines: Assessing PPE Waste Disposal in Lahore's Major Public Hospitals During COVID-19 and its Environmental Consequences

The COVID-19 pandemic spread around the world like fire in a forest. Many preventative measures were suggested, among which one was the use of PPE. PPE was already being used in hospitals to prevent the spread of contagious diseases. The current pandemic led to an enormous increase in PPE usage, both in hospitals and domestic circuits. However, it has been observed that PPE waste is not being handled and administered appropriately in some major hospitals in Lahore, Pakistan. This mishandling can lead to the rapid spread of diseases, more specifically, the current pandemic, COVID-19. Our study aimed to describe the management of PPE waste in public hospitals. The data was collected by direct and indirect observations of staff in the hospital, including the ones in administration, and the way they managed the waste. The condition of infectious waste in the waste depots of hospitals was also observed and noted. It was observed that not all the hospitals were following the guidelines provided by the WHO about PPE waste. Segregation, storage and transportation were below Pakistan’s biosafety rules for 2015 and WHO standards. It could lead to many health and environmental problems. Our study highlights the need for staff training regarding segregation and the proper storage and disposal of hospitals' waste. Certain recommendations in this regard have also been reported to put a stop to the issue of PPE waste and its drastic consequences for the environment.

Face Mask Detection System Using Machine Learning Algorithms

The project presented in this report is a real-time face mask detection system using computer vision and deep learning techniques. The primary objective of this project is to develop a system that can detect whether a person is wearing a face mask or not, with a focus on real-time performance.
 The system utilizes pre-trained deep learning models for face detection and mask classification. It leverages the MobileNetV2 architecture as a feature extractor and deploys the model in real-time video streams. When a face is detected, the system classifies it as "Mask" or "No Mask" with associated confidence scores. The project involves key components, including the use of OpenCV for image processing and real-time video capture, TensorFlow/ Keras for deep learning, and the integration of pre-trained models. The code is well- structured, and it demonstrates proficiency in model loading, image preprocessing, and real-time video processing.
 The findings of the project showcase a practical application for face mask detection, which has gained significance in the context of public health and safety. The system provides a valuable tool for monitoring mask compliance in public spaces and can contribute to efforts to mitigate the spread of contagious diseases. The project demonstrates the importance of combining computer vision and deep learning in real-world applications, and it serves as a reference for those interested in similar projects or applications in the field of image processing and object detection. In summary, this project illustrates the successful implementation of a real-time face mask detection system and underscores its potential contributions to public health and safety measures.

Applications and Challenges of Telemedicine: Privacy-Preservation as a Case Study.

Today, technology has an important impact on the development of medical services, especially during the outbreak of COVID-19. Telemedicine, known by terms such as telehealth and digital health, refers to the utilization of technology to provide health care services at a distance that leads to improved monitoring, detecting and treatment of disease, and provision of individual care. It has been considered in various fields such as radiology, cardiology, pulmonology, psychiatry, emergency care and surgery. The most important advantages of using telemedicine are saving time for the doctor and the patient, reducing the cost of multiple visits to the doctor, reducing the spread of contagious diseases and caring for patients who cannot see a doctor, such as the elderly. In this paper, we review the research in the field of applying telemedicine, as well as its advantages and disadvantages. Next, we discuss the challenges in the field of using telemedicine which are privacy preserving, data security, cost of infrastructures, lack of physical examination and responsibility for patients' compensation. One of the most important challenges is privacy preserving of patients' information during transmission and process. We categorize and compare the various methods that have been proposed to protect peoples' privacy.

Utilizing Mobility Tracking to Identify Hotspots for Contagious Disease Spread

A significant global health problem nowadays is the incidence of serious infectious illnesses. An extraordinary humanitarian crisis has been brought on by the current COVID-19 pandemic, which has spread around the world. The spread of new viruses has put established healthcare institutions under tremendous strain and created a number of pressing problems. It is important to predict the future movement and pattern of the illness in order to decrease infectious instances and maximize recovered cases. This research paper aims to utilize mobility tracking as a means to identify hotspots for contagious disease spread. The study focuses on collecting and analyzing mobility data from UNITEN students using Google Map data over a period of two weeks. The paper describes the data collection process, data pre-processing steps, and the application of the HDBSCAN algorithm for hotspot clustering. The results demonstrate the effectiveness of HDBSCAN in identifying hotspots based on the mobility data. The findings highlight the potential of mobility tracking for disease surveillance and provide insights for public health interventions and preventive measures.

RFID Attendance System with Contagious Disease Prevention Module using Internet-of-Things Technology

This paper presents the development and implementation of an innovative Internet of Things (IoT)-based RFID attendance system integrated with a module focused on curbing the spread of contagious diseases in densely populated settings, such as schools and workplaces. Recognizing the limitations of existing radio frequency identification (RFID) attendance systems, an extensive analysis of prior works was conducted to identify crucial functionalities necessary for an effective solution. Leveraging the IoT paradigm, we devised a 3-layer architecture encompassing thermometer sensors, RFID technology, Arduino microcontrollers, Wi-Fi connectivity, and the message queuing telemetry transport (MQTT) protocol to facilitate seamless data transmission and storage. The resulting prototype enables real-time monitoring of feverish symptoms, serving as an early warning system for potential contagious illnesses while streamlining attendance management. Data collected from the IoT devices is securely stored in a centralized database and accessed through an intuitive information system embedded within the IoT application. Users can effortlessly view and manage attendance data, while administrators gain access to health-related metrics, enabling timely responses to health concerns. User evaluations of the developed system resulted in an outstanding “A” rating, validating its reliability, functionality, and user satisfaction. Future improvements involve real-world testing, scalability assurance, integration with health authorities for comprehensive data management, and automated alerts for potential disease outbreaks.

Effect of Tourism and Air Travel Restrictions on the Initial International Spread of the Covid-19 Pandemic

The study aims to model the initial international spread of coronavirus disease 2019 (COVID-19) and analyze the risk of early initial spread using data from 105 countries. A generalized linear model (GLM) is developed to analyze the relationship between the initial international spread and its factors. Findings showed that the countries with high tourism activity are affected early by COVID-19, and countries with a better quality healthcare system can adequately detect the spread. Countries that promptly restricted air passengers have experienced a delay in the initial international spread. A regression analysis of fatalities during the initial stage suggested that the countries with an early initial spread of COVID-19 coupled with a high proportion of the elderly population possess a relatively high risk of fatalities. The study provides insights into the factors influencing the initial international spread of contagious diseases and the effect of air travel restrictions on the spread. This study can aid in contingency planning during the initial spread of pandemics, especially for countries with high tourism activity.

Digital Contact Tracing for COVID-19

The spread of the COVID-19 virus across the globe has permanently changed life for billions of people. Manual contact tracing has been utilized to assist in limiting the spread of contagious diseases for many years. The ubiquitous use of smartphones and similar wireless devices has allowed this process to become digital in nature through contact tracing applications installed on these devices. Various countries, and even various regional units within those countries, developed contact tracing applications. Such applications relied on location and short-range communication technologies associated with wireless devices and found varying degrees of success. This work reviews research conducted by universities, governmental organizations, and other entities with respect to the adoption, use, and ultimate success of, digital contact tracing applications across various countries and points to their rather limited success in fighting the spread of the disease. The authors also briefly discuss some implications regarding privacy and security that affected their use in certain countries.