Contagious Spread Research Articles

Bio-inspired algorithm integrated with sequential quadratic programming to analyze the dynamics of hepatitis B virus

BackgroundThere are a variety of lethal infectious diseases that are seriously affecting people's lives worldwide, particularly in developing countries. Hepatitis B, a fatal liver disease, is a contagious disease spreading globally. In this paper, a new hybrid approach of feed forward neural networks is considered to investigate aspects of the SEACTR (susceptible, exposed, acutely infected, chronically infected, treated, and recovered) transmission model of hepatitis B virus disease (HBVD). The combination of genetic algorithms and sequential quadratic programming, namely CGASQP, is applied, where genetic algorithm (GA) is used as the main optimization algorithm and sequential quadratic programming (SQP) is used as a fast-searching algorithm to fine-tune the outcomes obtained by GA. Considering the nature of HBVD, the whole population is divided into six compartments. An activation function based on mean square errors (MSEs) is constructed for the best performance of CGASQP using proposed model.ResultsThe solution's confidence is boosted through comparative analysis with reference to the Adam numerical approach. The results revealed that approximated results of CGASQP overlapped the reference approach up to 3–9 decimal places. The convergence, resilience, and stability characteristics are explored through mean absolute deviation (MAD), Theil’s coefficient (TIC), and root mean square error (RMSE), as well as minimum, semi-interquartile range, and median values with respect to time for the nonlinear proposed model. Most of these values lie around 10−10–10−4 for all classes of the model.ConclusionThe results are extremely encouraging and indicate that the CGASQP framework is very effective and highly feasible for implementation. In addition to excellent reliability and level of precision, the developed CGASQP technique also stands out for its simplicity, wider applicability, and flexibility.

WELL-POSEDNESS OF A NOVEL MATHEMATICAL MODEL FOR PERTUSSIS WITH VACCINATION AND ASYMPTOMATIC CLASSES

Bordetella pertussis is the bacterium that causes pertussis, a highly contagious respiratory disease spread primarily through droplet coughing or sneezing. Despite all efforts to reduce or even eliminate the transmission of pertussis disease, it continues to be hazardous in newborns and adults, causing illness and death in both age groups. Numerous studies have examined the factors that influence the spread of pertussis epidemiology and have used mathematical models to predict the possible effects of different vaccination approaches. In this paper, a mathematical model of pertussis has been formulated by incorporating the vaccination and Asymptomatic classes. We established that the existence and uniqueness of solution, boundedness and positivity of solution holds for the epidemiological model.

The contagious spread of bullying among Chinese adolescents through large school-based social networks

This study investigated the spread of bullying behaviors in school-based social networks among 13,195 Chinese adolescents (48.7% female). Using a modified Chinese version of the Multidimensional Peer Victimization Scale, we examined five types of bullying. The study focused on the association between an individual’s bullying behavior and that reported by their friends, the dose-response correlation between the number of friends involved in bullying and the prevalence of bullying behavior, and the moderators influencing the magnitude of peer influence. Our results indicate a significant contagion effect in bullying behaviors. A participant was more likely to engage in all five types of bullying if their friends reported similar behaviors. This influence persisted up to three degrees of separation for physical, verbal, possession, and sexual bullying, and extended to four degrees for relational bullying. A strong dose-response relationship was observed, indicating that the more friends involved in bullying, the higher the likelihood of a participant engaging in similar behaviors. Gender dynamics played a crucial role; females were more influenced by same-gender peers in relational and verbal bullying, whereas males showed a higher influence from male peers in physical and sexual bullying. These findings highlight the complex nature of bullying spread within social networks and the importance of considering peer influence and gender dynamics. They carry significant implications for future research, school policies, and public health interventions targeting bullying prevention.

The greatest contribution to medical science is the transformation from studying symptoms to studying their causes-the unrelenting legacy of Robert Koch and Louis Pasteur-and a causality perspective to approach a definition of SLE.

The basic initiative related to this study is derived from the fact that systemic lupus erythematosus (SLE) is a unique and fertile system science subject. We are, however, still far from understanding its nature. It may be fair to indicate that we are spending more time and resources on studying the complexity of classified SLE than studying the validity of classification criteria. This study represents a theoretical analysis of current instinctual SLE classification criteria based on "the causality principle." The discussion has its basis on the radical scientific traditions introduced by Robert Koch and Louis Pasteur. They announced significant changes in our thinking of disease etiology through the implementation of the modern version of "the causality principle." They influenced all aspects of today's medical concepts and research: the transformation of medical science from studies of symptoms to study their causes, relevant for monosymptomatic diseases as for syndromes. Their studies focused on bacteria as causes of infectious diseases and on how the immune system adapts to control and prevent contagious spreading. This is the most significant paradigm shift in the modern history of medicine and resulted in radical changes in our view of the immune system. They described acquired post-infection immunity and active immunization by antigen-specific vaccines. The paradigm "transformation" has a great theoretical impact also on current studies of autoimmune diseases like SLE: symptoms and their cause(s). In this study, the evolution of SLE classification and diagnostic criteria is discussed from "the causality principle" perspective, and if contemporary SLE classification criteria are as useful as believed today for SLE research. This skepticism is based on the fact that classification criteria are not selected based on cogent causal strategies. The SLE classification criteria do not harmonize with Koch's and Pasteur's causality principle paradigms and not with Witebsky's Koch-derived postulates for autoimmune and infectious diseases. It is not established whether the classification criteria can separate SLE as a "one disease entity" from "SLE-like non-SLE disorders"-the latter in terms of SLE imitations. This is discussed here in terms of weight, rank, and impact of the classification criteria: Do they all originate from "one basic causal etiology"? Probably not.

Detection of Human Adenovirus from Clinical Samples of Conjunctivitis by Real-time PCR: A Cross-sectional Descriptive Study

Introduction: Human Adenoviruses (HAdV) have been implicated in a variety of infections, including conjunctivitis, respiratory tract infections, genitourinary infections and gastroenteritis. Epidemic Keratoconjunctivitis (EKC) is a severe ocular surface infection strongly associated with HAdV, known to occur in widespread outbreaks. HAdV species B, D and E are associated with ocular manifestations ranging from simple follicular conjunctivitis (types 3, 4, 7) and pharyngoconjunctival fever (types 3, 7, 14) to the more severe EKC (types 8, 19, 37, 53, 54, 56 and 64). Viral cell cultures of conjunctival specimens help confirm adenovirus infection through the characteristic Cytopathic Effect (CPE) and Polymerase Chain Reaction (PCR) is the best standard method to diagnose viral conjunctivitis due to its sensitivity, accuracy and rapidity. Aim: To diagnose HAdV in clinically suspected cases of viral conjunctivitis using real-time PCR. To isolate HAdV in viral cell culture and confirm it by observing its characteristic CPE and performing adenoviral real-time PCR. Materials and Methods: A cross-sectional descriptive study was conducted from July 2023 to September 2023 at the state-level Virus Research and Diagnostic Laboratory (VRDL) at Bangalore Medical College and Research Institute (BMCRI), Bengaluru, Karnataka, India. A total of 45 conjunctival swab samples from patients with suspected viral conjunctivitis, who attended the Ophthalmology Outpatient Department (OPD) or were admitted to the wards, were included in the study. Conjunctival swab samples collected in Viral Transport Medium (VTM) were centrifuged at 3000 rpm for five minutes at 4°C. Real-time PCR was performed and samples positive for HAdV by real-time PCR were taken up for viral cell culture. Confirmatory PCR was conducted for samples showing CPEs in the cell line. The data collected were analysed using descriptive statistics. Results: The majority of patients in the present study were in the age group of 18-45 years, comprising 25 (56%) of the total patients. Out of 45 conjunctival samples tested, 8 samples were positive in adenovirus real-time PCR. All eight PCR-positive samples showed a CPE in viral cell culture on the A549 cell line. The study found PCR positivity in 8 samples (17.7%), while adenoviral recovery from cell culture was observed in 6 samples (13.3%). Conclusion: Real-time PCR has become the standard diagnostic procedure for detecting adenovirus conjunctivitis. Rapid and accurate diagnosis is key to interrupting the contagious spread of adenoviral conjunctivitis, along with timely treatment.

Multiplex primers employment for detection of rinderpest virus RNA by PCR

Given the high contagiousness and rapid spread of the rinderpest virus, timely and accurate diagnosis plays a key role in preventing epidemics and taking measures to control the disease. The study aims to evaluate the efficiency of using multiplex primers in the polymerase chain reaction method for the detection of rinderpest virus ribonucleic acid. The study included the analysis of samples such as blood serum and conjunctival swabs from 50 animals with clinical manifestations of the disease. The experiment involved the collection of clinical samples such as blood serum and conjunctival washings. The results demonstrate the high specificity of the developed primers. These primers stand out because they use two pairs of the same gene region with different variable sequences that are specific for all strains of the rinderpest virus. In the polymerase chain reaction, both pairs of primers are used simultaneously at equal concentrations and under the same conditions. An additional polymerase chain reaction performed using these primers at the optimal annealing temperature confirmed the successful amplification and specificity of the primers. The absence of dimers and nonspecific products in the negative control confirmed the purity and reliability of the results. Thus, these results demonstrate that the use of these multiplex polymerase chain reaction primers allows for the efficient detection of the ribonucleic acid of the rinderpest virus of different strains. The developed multiplex primers represent an innovative method for the diagnosis of rinderpest virus with the potential for use in veterinary practice and animal disease control

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.

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.

The Impact of the Initial Phase of the COVID-19 Pandemic on Patients With Autoimmune Bullous Diseases.

Introduction The follow-up of patients with autoimmune bullous diseases (AIBDs) was temporarily interrupted during the initial phase of the COVID-19 pandemic due to restrictions in healthcare services, given the high contagiousness and rapid spread of SARS-CoV-2. Our objective was to assess the impact of the initial phase of the COVID-19 pandemic on the treatments and disease activity of AIBD patients. Methods We conducted a telephone survey of patients with AIBDs who had been regularly followed up in our hospital prior to the onset of the pandemic. A structured questionnaire that we designed was used. This questionnaire comprised questions examining the following issues between March and June of 2020: patients' follow-up, treatment, COVID-19 infection status, and changes in disease activity. Results Thirty-nine patients were included in the study. Among those, 26 (66.7%) were immunosuppressed. The frequency of follow-up for 37 patients (94.9%) changed significantly (p<0.001): 28 patients (71.8%) did not visit the hospital, and 26 of them (92.9%) did not communicate at all. The treatment for 10 patients (25.6%) was altered, either by their physician or by themselves. Disease activity reactivated in patients who altered their own treatments. There was only one patient (2.6%) who contracted COVID-19. Conclusions Documenting this period revealed that some patients were negatively impacted by the pandemic initially. The most significant contributing factor was the interruption of patient-physician communication.

Efficient simulations of epidemic models with tensor networks: Application to the one-dimensional susceptible-infected-susceptible model.

The contact process is an emblematic model of a nonequilibrium system, containing a phase transition between inactive and active dynamical regimes. In the epidemiological context, the model is known as the susceptible-infected-susceptible model, and it is widely used to describe contagious spreading. In this work, we demonstrate how accurate and efficient representations of the full probability distribution over all configurations of the contact process on a one-dimensional chain can be obtained by means of matrix product states (MPSs). We modify and adapt MPS methods from many-body quantum systems to study the classical distributions of the driven contact process at late times. We give accurate and efficient results for the distribution of large gaps, and illustrate the advantage of our methods over Monte Carlo simulations. Furthermore, we study the large deviation statistics of the dynamical activity, defined as the total number of configuration changes along a trajectory, and investigate quantum-inspired entropic measures, based on the second Rényi entropy.

Incorrect facemask-wearing detection using image processing and deep learning

Now and in the future, a face mask is a very important strategy to protect people when a new contagious life threatens disease spread through the air appears. Currently, there is a serious health emergency because of the coronavirus disease 2019 (COVID-19) epidemic. The negative consequences of this pandemic need to be protected in public areas. Numerous methods are advised by the World Health Organization (WHO) to reduce infection rates and prevent depleting the available medical resources in the absence of efficient antivirals. Wearing masks is a non-pharmaceutical strategy to lessen the susceptibility to COVID-19 infection. This research aims to create a face mask identification system that is efficient and uses deep learning, which has proven to be beneficial in many real-world applications. This system has also used a transfer learning method with the MobileNetV2 model to classify people who wear face masks properly, wear face masks improperly, and are without masks. The results demonstrate that the proposed system has an accuracy of 99.4% which is higher than current systems.

Incorrect facemask-wearing detection using image processing and deep learning

Now and in the future, a face mask is a very important strategy to protect people when a new contagious life threatens disease spread through the air appears. Currently, there is a serious health emergency because of the coronavirus disease 2019 (COVID-19) epidemic. The negative consequences of this pandemic need to be protected in public areas. Numerous methods are advised by the World Health Organization (WHO) to reduce infection rates and prevent depleting the available medical resources in the absence of efficient antivirals. Wearing masks is a non-pharmaceutical strategy to lessen the susceptibility to COVID-19 infection. This research aims to create a face mask identification system that is efficient and uses deep learning, which has proven to be beneficial in many real-world applications. This system has also used a transfer learning method with the MobileNetV2 model to classify people who wear face masks properly, wear face masks improperly, and are without masks. The results demonstrate that the proposed system has an accuracy of 99.4% which is higher than current systems.

Epidemic threshold influenced by non-pharmaceutical interventions in residential university environments

The control of highly contagious disease spreading in campuses is a critical challenge. In residential universities, students attend classes according to a curriculum schedule, and mainly pack into classrooms, dining halls and dorms. They move from one place to another. To simulate such environments, we propose an agent-based susceptible–infected–recovered model with time-varying heterogeneous contact networks. In close environments, maintaining physical distancing is the most widely recommended and encouraged non-pharmaceutical intervention. It can be easily realized by using larger classrooms, adopting staggered dining hours, decreasing the number of students per dorm and so on. Their real-world influence remains uncertain. With numerical simulations, we obtain epidemic thresholds. The effect of such countermeasures on reducing the number of disease cases is also quantitatively evaluated.

Finding shortest and nearly shortest path nodes in large substantially incomplete networks by hyperbolic mapping

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Computing shortest paths is a straightforward task when the network of interest is fully known, and there are a plethora of computational algorithms for this purpose. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions and the Internet at the autonomous system level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks, where numbers of missing links exceed those of observable links. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security.

Coronary bypass surgery on the background of Covid-19. A clinical case

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a huge impact on the health of the world's population. Due to the high contagiousness and rapid spread of the infection, on March 11, 2020, the World Health Organization (WHO) announced the beginning of a pandemic. Meanwhile, despite the COVID-19 pandemic, coronary heart disease is one of the most common diseases in the world and still ranks first in the structure of mortality. Currently, one of the optimal and most effective methods of surgical treatment of coronary heart disease with atherosclerotic lesions of the coronary bed is direct myocardial revascularization — coronary bypass surgery. However, cardiac surgical interventions during the COVID-19 pandemic are associated with certain risks. Active infection is a serious aggravating factor that can lead to the development of complications and death. This article presents a clinical case of the death of a 65‑year-old patient after elective coronary artery bypass grafting, where previously undiagnosed COVID-19 was detected in the early postoperative period.

Monkeypox: Cutaneous clues to clinical diagnosis

Monkeypox: Cutaneous clues to clinical diagnosis

Incorporation of mechanisms for providing green environment post COVID-19

It has been observed that the damage to the environment caused has grown multi-fold in recent years especially with the exponential rise in population. The sad part of it lies with the fact that no part of earth be it the mountains or oceans or plains have been spared. In order to predict the global climate change, a number of models have been developed over the years. All reliable models clearly indicate the overall deterioration in the quality of air levels. The geographical distribution of precipitation changes at different places leading to changes in water availability. Living a healthy life in a clean environment has soon become a distant dream for the common man. To add to the same, the arrival of Corona virus in the year 2019 has put human life in the doldrums as its contagious and fast spreading nature has put day to day life on hold besides causing deaths and forcing people to throng to hospitals at a rapid rate not witnessed during the recent past. On the other hand, the outbreak of the COVID-19 Pandemic had a positive impact on the environment as most of the highly polluted cities witnessed a clear blue sky. Air Quality Index (AQI) and Particulate Matter (PM2.5) levels in all the major cities improved during the lockdown period. However, with life slowly limping back to normal, there is a dire need to ensure that humans do not go back to their old ways of polluting the environment. The way of work needs to change in each and every type of industry. This paper focuses on mechanisms for providing a green enterprise transformation for some of the commonly functioning industries post COVID-19 times. The research study focused on a specific country in Asian continent, and it can be extended to other countries/continents.

Vaccination control measures of an epidemic model with long-term memristive effect

COVID-19 is a drastic air-way tract infection that set off a global pandemic recently. Most infected people with mild and moderate symptoms have recovered with naturally acquired immunity. In the interim, the defensive mechanism of vaccines helps to suppress the viral complications of the pathogenic spread. Besides effective vaccination, vaccine breakthrough infections occurred rapidly due to noxious exposure to contagions. This paper proposes a new epidemiological control model in terms of Atangana Baleanu Caputo (ABC) type fractional order differ integrals for the reported cases of COVID-19 outburst. The qualitative theoretical and numerical analysis of the aforesaid mathematical model in terms of three compartments namely susceptible, vaccinated, and infected population are exhibited through non-linear functional analysis. The hysteresis kernel involved in AB integral inherits the long-term memory of the dynamical trajectory of the epidemics. Hyer–Ulam’s stability of the system is studied by the dichotomy operator. The most effective approximate solution is derived by numerical interpolation to our proposed model. An extensive analysis of the vigorous vaccination and the proportion of vaccinated individuals are explored through graphical simulations. The efficacious enforcement of this vaccination control mechanism will mitigate the contagious spread and severity.

COVID-19 infection prediction from CT scan images of lungs using Iterative Convolution Neural Network model

World Health Organization has defined COVID-19 as a contagious, communicable and fast spreading disease engendered by the Corona virus, SARS-CoV-2, is a respirational microorganism. Computerized Tomography (CT) scan images of the chest helps in detecting COVID 19 infection in a fast way with much reliability. In this paper, chest CT scan images of COVID and Non-COVID categories are considered to train the supervised classifier, Iterative convolution Neural Network. The training process is done with six different training data size. The trained models are iterated for the fixed size of testing data (20 images). The same set of training and testing processes are done with two different Iterative Convolutional Neural Network architectures, one with two hidden layers (CNN1) and another with three hidden layers (CNN2). The iterations are extended up to 7, but the model performance is degraded after the 6th iteration, which makes to fix the iteration level as 5 for both CNN models. Six different training sets with five iterations have led into 30 CNN models. For two different CNN architectures, which lead to 60 different models. The model designed with 100 training sets in both CNN1 and CNN2, have produced the high accuracy in COVID classification than any other models. The better classification accuracy 89% is achieved from CNN2 model with its 5th iteration.

Skin Disease Detection for Kids at School Using Deep Learning Techniques

Due to the rapid spread of skin diseases among children in school, and the fact that skin disease is the most common contagious disease spreading within students in school, this study investigates the factors that could help in early detection of these skin diseases using AI techniques. The texture and color of the skin can change as a result of the disease. Examples of these diseases are chickenpox, impetigo, scabies, infectious erythema, skin warts, and other infectious skin diseases. Skin disorders are long-term and contagious, it can be detected early and with high accuracy before it become a long-term problem. This research builds a system of skin disease detection using the CNN technique and a pre-trained VGG19 model. In addition, the dataset contains 4500 images that were collected from different sources to train the VGG19 model. Data augmentation technique such as zooming, cropping, and rotating were used. After that, the Adamax optimizer, which is most suitable for the proposed methodology, was used to obtain high accuracy and required results. This study achieved a high accuracy of 99% compared to other similar researchs. It can be concluded that this system is very reliable which can be integrated to smart schools as part of IOT systems.