Infected People Research Articles

Reducing direct exposure to exhaled aerosol through a portable desktop fan

Vulnerable individuals close to infected people emitting a respiratory cloud containing infectious load can inhale a pathogen dose, experiencing a more severe impact on their health compared to other individuals breathing the mixed air in the same room. In crowded spaces, this issue is crucial. Employing local airflow patterns can reduce the proximity risk of inhalation and subsequent transmission across short distances. This study proposes an experimental and numerical analysis of a novel personal and portable device creating a short-range air barrier to transmitting airborne pathogens in proximity. The portable device adopts V-shaped air blades affecting the trajectory of the particle-laden respiratory cloud emitted by the respiratory system of the infected individual. Experimental results, supported by CFD analysis, indicate that controlling local airflow through the V-shaped jet significantly reduces local particle concentrations by more than 60%, compared to typical scenarios without a local airflow control.

A Caputo fractional derivative dynamic model of hepatitis E with optimal control based on particle swarm optimization

Hepatitis E, as a zoonotic disease, has been a great challenge to global public health. Therefore, it has important research value and practical significance for the transmission and control of hepatitis E virus (HEV). In the exploration of infectious disease transmission dynamics and optimal control, mathematical models are often applied. Among them, the fractional differential model has become an important and practical tool because of its good memory and genetic characteristics. In this paper, an HEV propagation dynamic model is constructed by the Caputo fractional derivative. First, the properties of the model are analyzed, including the existence, non-negativity, boundedness, and stability of the equilibrium points. Then, from the perspective of fractional optimal control (FOC), control measures were proposed, including improving the awareness and prevention of hepatitis E among susceptible people, strengthening the treatment of infected people, and improving environmental hygiene. Then, an FOC model of HEV was constructed. After analyzing the necessary conditions for optimality, the particle swarm optimization is introduced to optimize the control function. In addition, four control strategies are applied. Finally, the numerical simulation is completed by the fractional Adams–Bashforth–Moulton prediction correction algorithm. The four strategies and no control were compared and analyzed. The numerical simulation results of different fractional orders are also compared and analyzed. The results illustrate that the optimal strategy, compared with no control, reduces the HEV control time by nearly 60 days. Therefore, this method would contribute to the study of HEV transmission dynamics and control mechanisms, thus contributing to the development of global public health.

Peculiarities of the clinical picture and diagnosis of dirofilariasis in the maxillofacial area: a series of cases from practice

Dirofilariasis is a helminthiasis that occurs in carnivorous animals (dogs, cats, foxes, wolves, etc.) and less often in humans, although according to some authors it is believed that the number of infected animals approximately corresponds to the number of infected people. Dirofilariasis of the maxillofacial area is not a widespread condition, its cases are described in short series, therefore, the aim of this work is to share our own experience of managing patients with this rare condition.The work presents our own experience in the management of 14 dirofilariasis patients with lesions of the maxillofacial area, of them there are 3 men and 11 women aged 29 to 54. Most patients sought help in the late autumn, winter and spring periods and only 2 people in the summer period. All patients were examined by a dentist, and an ultrasound examination of the area with a formation was performed. According to the clinical manifestations, the disease began acutely in nine patients and had a picture of inflamed atheroma (2 – in the zygomatic area, 1 – in the parotid, 2 – in the infraorbital area, 2 – in the temporal area, and 2 – in the area of the nasal bridge). All patients were operated on and tumor-like formations in dense membranes were removed, inside which one live nematode 11-18 cm long was found. In 3 observations, fragments of dead, partially lysed nematodes were removed from the oral cavity, the capsules of which imitated a migrating granuloma. According to the location – 5 capsules with dirofilaria were located in the subcutaneous fatty tissue, 3 – in the submucosal layer of the gums fused to the periosteum of the upper jaw, 1 – in the submucous layer of the lower jaw, 2 – in the fibers of the temporalis muscle and 1 – in the fibers of the buccal muscle. In each case, the localization of the nematode was close to the places of accumulation of adipose tissue or large blood vessels. In the postoperative period complex therapy was prescribed, including symptomatic anti-inflammatory and desensitizing agents, and consultation with infectionist was recommended. Thus, according to our observations, dirofilariasis most often affects areas of the face in which there are large vessels and accumulation of fatty tissue. In connection with the prevalence of dirofilariosis in humans and the predominant localization of parasitic infection in the face area, dentists, ophthalmologists and otolaryngologists need to be familiar with this pathology and show definite vigilance. Treatment dirofilariosis patients should be carried out comprehensively, together with parasitologists, with surgical excision of the lesion and appropriate drug therapy.

Time based Meticulous analysis of pandemic spreading ratio using Simpy framework

Due to the unavoidable spread of COVID-19 and even taking all substantial measures, the ratio of infected people and death rate seems to be out of control. In this increasingly worsening situation, the aim of this article is that it is important to take extraordinary measures to deal with this exceptional pandemic situation, and this is only possible if the actual ratio of the spread of the pandemic is known. Therefore, ingenious pandemic models are being developed to produce real-time infection statistics on an hourly, weekly, and monthly basis. This clever model leverages well-known data sets and when they will be applied to determine the status of three types of infections: the number of infected people, the time the pandemic started, and the time it ended. The time-based results are generated by conduction simulation in the Simpy Python framework, and the resulting results are characteristic of the real-time infection spread ratio. This shows when extraordinary measures for the infection ratio are necessary and when they become reasonable.

A Smart Contract Based Secure Ride Sharing System

A ride-sharing system provides many advantages. It reduces energy consumption. Moreover, it mitigates traffic congestion. Furthermore, it provides benefits to drivers and passengers in that they share travel costs (gas, toll ticket). Ride-sharing has been getting very popular since the COVID-19 pandemic. Since COVID-19 is very contagious and spread by infected people via coughs, sneezes, or talking, people avoid using public transportation to get the disease. People prefer to travel with a few people to protect their health. To prevent people from getting the disease and to have the advantages of a ride-sharing system, we introduce a ride-sharing system in which a driver only travels with a passenger for each event. Traveling with only one person provides advantages to both the driver and passenger. The driver (passenger) not only shares the cost of the trip but also reduces the risk of getting or spreading the disease. This paper proposes a smart-contract-based ride-sharing system that uses a $Vickrey$ (second highest bid) auction mechanism during a pandemic to determine the passenger. The system provides a decentralized, transparent, trackable, verifiable, and secure ride-sharing with the help of consortium blockchain. Using smart contracts in the system allows users’ bids to be transparent and verifiable. Furthermore, the proposed system provides a secure and lightweight mechanism to protect users' (drivers and passengers) travel data (locations and cost of travel). Their travel data for each event is going to be unlinkable.

Smart Medical Application of Deep Learning (MUNet) for Detection of COVID-19 from Chest Images

Fighting the outbreak of COVID-19 is now one of humanity's most critical matters. Rapid detection and isolation of infected people are crucial for decelerating the disease's spread. Due to the pandemic, the conventional technique for COVID-19 detection, reverse transcription-polymerase chain reaction, is time-consuming and in small abundance. Therefore, studies have been searching for alternate methods for detecting COVID-19, and thus applying deep learning methods to patients' chest images has been rendering impressive performance. The primary objective of this study is to suggest a technique for COVID-19 detection in chest images that is both efficient and reliable. We propose a deep learning method for COVID-19 classification based on a modified UNet called (Covid-MUNet). The Covid-MUNet model is trained using publicly available datasets, including chest X-ray images for multi-class classification (3-class and 4-classes) and CT scans images for binary/multi-class classification (2-classes and 3-classes). Using chest images, the Covid-MUNet is a successful methodology that helps physicians rapidly identify patients with COVID-19, thereby delaying the fast spread of COVID-19. The proposed model achieved an overall accuracy of 97.44% in classifying three categories (COVID-19, Normal, and Pneumonia) and an accuracy of 96.57% in classifying two categories (COVID-19 and Normal).

Application of geospatial multi-agent system for simulation of different aspects of tuberculosis transmission

BACKGROUND. The article presents the modeling of the epidemiological picture of tuberculosis (TB) using a multi-agent system. This is to provide an in-depth multi-agent modeling study with comprehensive strategies related to TB treatment and prevention.
 OBJECTIVE. To develop an algorithm that uses the capabilities of artificial intelligence and neural networks to build a geospatial model of TB transmission.
 MATERIALS AND METHODS. This study involves the development of an algorithm that uses the potential of artificial intelligence to create a geospatial model that highlights the different routes of TB transmission. The simulation process itself is characterized by a number of key stages, including initialization of the city, calibration of health parameters, simulation of the working day, spread of infection, evolution of disease trajectories, rigorous statistical calculations and transition to the next day. A comprehensive description of the course of active TB according to official data of the World Health Organization is given. Each agent is provided with an appropriate and consistent model that includes relevant health attributes and necessary rules for their dynamic evolution.
 RESULTS AND DISCUSSION. The process of modeling the transmission of TB in the urban landscape has been thoroughly investigated. Attributes and functional dependencies are divided into three different groups: static (or deterministic), stochastic, and empirical. Empirical parameters that determine the contagiousness of TB patients are subjected to careful analysis, during which the dynamic nature of their actual contagiousness is carefully compared. Therefore, the empirical parameters that determine infectivity are carefully established, which additionally allows for the quantitative determination of the level of TB infection. A complex simulation is presented that illustrates the spread of TB in a completely healthy environment devoid of any preventive or therapeutic measures. This simulation serves as a crucial study of the spread of the disease. To test the adequacy of the model and its sensitivity to the main parameters that determine the course of TB, a series of experiments using three different approximations, namely: a basic model, a model that includes mortality factors and a comprehensive model covering all relevant aspects, was conducted. Statistics used in this context include the number of infected and latent persons, recoveries and deaths. The article presents a comparative analysis between statistical information and a model that offers a comprehensive picture of the state of health of each person. The model allows careful monitoring of various factors, such as interpersonal contacts, place of residence, workplace, modes of transport used to move from place to place, and other vital aspects of a person’s life, together contributing to a more complete understanding of the dynamics of the disease.
 CONCLUSIONS. The calculations show the stability of the results and the absence of large fluctuations. The obtained statistical values of infected, latent and recovered people correlate with known medical data, and therefore, confirm the adequacy of the proposed model. This model allows tracking and analyzing the life and behavior of each agent, which allows for a detailed assessment and analysis of the spread of TB, as well as to develop a strategy to prevent the spread of this infection.

ENTROPY-RANDOMIZED ESTIMATION OF NONLINEAR DYNAMICAL MODEL PARAMETERS ON OBSERVATION OF DEPENDENT PROCESS

The work is devoted to the development of the method of randomized machine learning in the direction of estimating dynamic models of related processes using real data, one of which is considered as the main one, and the other as a dependent one. The model of the main process in this concept is implemented by a dynamic model based on differential equations with parameters, which in turn are implemented by a static model in a different time scale. Randomized machine learning is a new theory at the intersection of data science, machine learning and data mining, based on the concept of entropy to estimate the probabilistic characteristics of model parameters. Such characteristics are the probability distributions of the corresponding objects, the estimates of which are the distributions implemented by the probability density functions or discrete distributions. Achieving this goal becomes possible thanks to the idea of moving from models with deterministic parameters to models with random parameters and, additionally, measured at the output with random noise, which achieves the consideration of the stochastic nature, which is obviously present in any natural phenomenon. As a demonstration of the proposed method, we consider the problem of predicting the total number of infected people based on the SIR dynamic epidemiological model, in which one of the parameters is considered as the state of the associated process implemented by the static model. Its evaluation is carried out according to the observations of the main process, and forecasting is carried out using the model of the associated one. Conducted experiment using real data on COVID-19 cases in Germany shows the efficiency of the proposed approach. The forecast obtained by the classical least squares method leads to an underestimation of the model output compared to the real observed data, while the proposed approach demonstrates more flexibility and potentially allows you to obtain forecasts that are more adequate to the real data, which confirms its effectiveness and adequacy under conditions a small amount of data with a high level of uncertainty.

Challenges of mesenchymal stem cells in the clinical treatment of COVID-19.

The 2019 coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has brought an enormous public health burden to the global society. The duration of the epidemic, the number of infected people, and the widespread of the epidemic are extremely rare in modern society. In the initial stage of infection, people generally show fever, cough, and dyspnea, which can lead to pneumonia, acute respiratory syndrome, kidney failure, and even death in severe cases. The strong infectivity and pathogenicity of SARS-CoV-2 make it more urgent to find an effective treatment. Mesenchymal stem cells (MSCs) are a kind of pluripotent stem cells with the potential for self-renewal and multi-directional differentiation. They are widely used in clinical experiments because of their low immunogenicity and immunomodulatory function. Mesenchymal stem cell-derived exosomes (MSC-Exo) can play a physiological role similar to that of stem cells. Since the COVID-19 pandemic, a series of clinical trials based on MSC therapy have been carried out. The results show that MSCs are safe and can significantly improve patients' respiratory function and prognosis of COVID-19. Here, the effects of MSCs and MSC-Exo in the treatment of COVID-19 are reviewed, and the clinical challenges that may be faced in the future are clarified.

HUMAN CYSTIC ECHINOCOCCOSIS IN ENDEMIC REGIONS IN BULGARIA - ULTRASOUND SURVEILLANCE, TREATMENT AND FOLLOW-UP OF PATIENTS

Introduction: Cystic echinococcosis (CE) is a chronic disease in humans, usually asymptomatic, but in some cases could be very severe with fatal outcomes. This parasitosis is a health and economic problem in many areas of the world. The actual spread among people with CE in endemic regions is not known. The purpose of prophylactic ultrasound examinations of people in some areas was to clarify the real prevalence of CE and to detect undiagnosed asymptomatic cases and recurrences. Materials and methods: An abdominal ultrasound survey in four endemic regions in Bulgaria with the highest rate of registered patients with CE in Bulgaria for the last five years was conducted. For every patient with CE an epidemiological and clinical history, abdominal ultrasound examination, blood tests and ELISA IgG test for CE were performed. Results: From all 8602 people examined, in seventeen persons (0.2%), liver hydatid cysts were found for the first time. Ssixty five (0.8%) reported a history of previous surgical treatment for CE, of which 54 (83.1%) had passed surgery because of liver CE and 11 (16.9%) had extrahepatic CE (pulmonary, cardiac, bone, spleen). In three (5.6%) patients, who were operated years before for liver CE, recurrences were discovered. In 11 (64.7%) patients devitalized CE4 cysts were observed without any data for previous treatment. Three of the discovered patients with CE were treated with surgery, one with puncture-aspiration-injection-reaspiration, four only with Albendazole. The patients were successfully treated and followed for a period of five years. Conclusions: During the prophylactic ultrasound examinations, a higher percentage of people infected with liver cystic echinococcosis was found compared to the official information. It should be noted that these results are found in areas with the highest incidence of this disease in the country. US screenings give the opportunity for early detection, timely treatment of infected people and to prevent severe complications in patient with CE disease.

Analysis of neuropsychological and laboratory parameters in patients with cerebrovascular disease and SARS-CoV-2 compared to those without SARS-CoV-2

Background. Severe acute respiratory syndrome сoronavіrus 2 (SARS-CoV-2, formerly known as 2019-nCoV) is the cause of coronavirus disease 2019 (COVID-19), and was first reported in Wuhan, China. However, it is also contagious to humans and spreads rapidly around the world through close contact between infected people or through a relatively simple transmission mechanism (airborne transmission). COVID-19 is known to affect almost all systems of the human body. Initial reports suggest that hypertension may be a risk factor for susceptibility to SARS-CoV-2 infection, a more severe course of COVID-19, and increased mortality associated with COVID-19. It is estimated that 1–3 % of COVID-19 patients experience transient ischemic attacks with a frequency similar to other coronavirus infections (SARS-CoV-1 and MERS-CoV). The cause of ischemic stroke associated with COVID-19 is unknown, but previous studies have suggested that an inflammatory cytokine storm may cause hypercoagulation and endothelial damage. We see that COVID-19 is closely related to neurological complications because there are potential factors that can cause them. Materials and methods. Cerebrovascular diseases were analyzed in 111 patients infected with SARS-CoV-2 (n = 71) and those without a history of SARS-CoV-2 (n = 40). The subject of the study was neuropsychological and laboratory indicators. The following methods were used: psychometric — Beck Anxiety Inventory, Hamilton Depression Rating Scale, Fatigue Assessment Scale; neuropsychological — Mini-Mental State Examination, Montreal Cognitive Assessment, Frontal Assessment Battery; clinical — neurological status; polymerase chain reaction to detect COVID-19 RNA; statistical methods. Results. In patients who suffered transient ischemic attack and ischemic stroke with a minimal neurological deficit and COVID-19, there were elevations in the erythrocyte sedimentation rate, leukocytes, segmented neutrophils, while an increase in C-reactive protein was noted in all participants with cerebrovascular disease and COVID-19, with more significant levels among those with ischemic stroke. All subgroups with COVID-19 showed an increase in D-dimer and fibrinogen with higher content in patients after ischemic stroke. Also in this subgroup, the procalcitonin index exceeded the norm, which indicates the severity of the course of COVID-19 with the addition of co-infection. Data of neuropsychological tests in patients with ischemic stroke with a minimal neurological deficit with SARS-CoV-2 revealed a decrease in the Montreal Cognitive Assessment score, indicating mild cognitive changes in these patients. The level of anxiety in patients with hypertension with frequent crises and ischemic stroke with a minimal neurological deficit was above the reference values, with a slight predominance in patients who did not have COVID-19. It follows that both laboratory and neuropsychological parameters differed in three subgroups depending on cerebrovascular disease, as well as the presence and absence of SARS-CoV-2, which makes it possible to develop more appropriate diagnostic methods in order to predict the course and outcome of COVID-19.

Viruela símica: reporte de un caso clínico

Monkeypox is a zoonosis-type disease caused by a virus of the genus Orthopoxvirus. Usually accompanied by systemic symptoms such as fever, headache, myalgia, asthenia, skin rashes or mucosal lesions. This disease is transmitted by physical contact with infected people, infected materials or animals. We present the case of a 6-year-old patient who came to primary care for symptoms compatible with clinical suspicion. Monkeypox was diagnosed under positive PCR and discharged with outpatient symptomatic management.

Analyzing dynamics and stability of single delay differential equations for the dengue epidemic model

This paper introduces a mathematical model that simulates the transmission of the dengue virus in a population over time. The model takes into account aspects such as delays in transmission, the impact of inhibitory effects, the loss of immunity, and the presence of partial immunity. The model has been verified to ensure the positivity and boundedness. The basic reproduction number R0 of the model is derived using the advanced next-generation matrix approach. An analysis is conducted on the stability criteria of the model, and equilibrium points are investigated. Under appropriate circumstances, it was shown that there is local stability in both the virus-free equilibrium and the endemic equilibrium points when there is a delay. Analyzing the global asymptotic stability of equilibrium points is done by using the appropriate Lyapunov function. In addition, the model exhibits a backward bifurcation, in which the virus-free equilibrium coexists with a stable endemic equilibrium. By using a sensitivity analysis technique, it has been shown that some factors have a substantial influence on the behavior of the model. The research adeptly elucidates the ramifications of its results by effortlessly validating theoretical concepts with numerical examples and simulations. Furthermore, our research revealed that augmenting the rate of inhibition on infected vectors and people leads to a reduction in the equilibrium point, suggesting the presence of an endemic state.

PHARE: a bioinformatics pipeline for compositional profiling of multiclonal Plasmodium falciparum infections from long-read Nanopore sequencing data.

The emergence of drug-resistant clones of Plasmodium falciparum is a major public health concern, and the ability to detect and track the spread of these clones is crucial for effective malaria control and treatment. However, in endemic settings, malaria infected people often carry multiple P. falciparum clones simultaneously making it likely to miss drug-resistant clones using traditional molecular typing methods. Our goal was to develop a bioinformatics pipeline for compositional profiling in multiclonal P. falciparum samples, sequenced using the Oxford Nanopore Technologies MinION platform. We developed the 'Finding P. falciparum haplotypes with resistance mutations in polyclonal infections' (PHARE) pipeline using existing bioinformatics tools and custom scripts written in python. PHARE was validated on three control datasets containing P. falciparum DNA of four laboratory strains at varying mixing ratios. Additionally, the pipeline was tested on clinical samples from children admitted to a paediatric hospital in the Central African Republic. The PHARE pipeline achieved high recall and accuracy rates in all control datasets. The pipeline can be used on any gene and was tested with amplicons of the P. falciparum drug resistance marker genes pfdhps, pfdhfr and pfK13. The PHARE pipeline helps to provide a more complete picture of drug resistance in the circulating P. falciparum population and can help to guide treatment recommendations. PHARE is freely available under the GNU Lesser General Public License v.3.0 on GitHub: https://github.com/Fippu/PHARE.

Prevalence Study of the Intestinal Protozoal Parasitic Infections Rates at Babylon and Kerbala Cities

Intestinal protozoa are parasites transmitted by the consumption of contaminated water and food and mainly affect children and older people and cause considerable health problems. They are leading to causes of outpatient morbidity in developing countries due to diarrhea. The aim of this study was invetigation the prévalence of Entamoeba histolytica and Giardia lamblia parasites at Kerbala and Babylon cities in 2022. The research was conducted during the period starting from 1/1/2021 until 31/12/2021 in the provinces of Babylon and Kerbala. The Imam Al-Sadiq Hospital in Babylon and the AlHusseini Hospital in Karbala were taken. Data were collected in both hospitals to detect the spread of the disease (E.histolytica) and they were taken. Considering the percentage of injuries for age groups and gender of the injured during the months of the year. Results of this study showed that rates of intestinal parasitic infections at the Hospitals of Babylon City at Imam Sadiq Hospital, while the total infection rates was 6.58% While the total infection rate with the same parasite at Al-Hashimiya Hospital was 29%. At Hospitals of Kerbala city, the total infection rate at Al-Hussein Hospital was 21.37% While the rate of infected people at Al-Husseiniya Hospital was 26.4% The results of infection rates with Giardia lamblia in the current study showed the following:- At hospitals of Babylon city the total infection rate was 14.3% When it reached Imam Sadiq Hospital, the infection rate was 1.72% While the rate of infection in Al-Hashimiya Hospital was: 1.69% At hospitals of Kerbala city the infection rate was in Al-Hussein Hospital 1.83% While the rate of infected people was at Al-Husseiniya Hospital 2.21% The total infection rate was .4..% as the infection rate was in Al-Hussein Hospital 1.83%, while the infection rate in Al-Husseiniyah Hospital was 2.21%.

Modeling and dynamics of measles via fractional differential operator of singular and non-singular kernels

Young children frequently die from measles, which is a major global health concern. Despite being more prevalent in infants, pregnant women, and people with compromised immune systems, it can infect anyone. Novel fractional operators, the constant-proportional Caputo operator, and the constant-proportional Atangana–Baleanu operator are used to create a hybrid fractional order model that helps analyze the dynamic transmission of the measles virus. We assess the measles-free and endemic equilibrium, reproductive number, biological viability, boundedness, well-posedness, and positivity of the model. We apply the Banach contraction principle to verify the uniqueness of the system’s solutions. The proposed system is confirmed to be Ulam–Hyres stable by using fixed point theory results. The aforementioned operators are further analyzed, and the Laplace-Adomian decomposition method is used to numerically simulate the system of fractional differential equations. To support our findings, the outcomes are graphically displayed. The efficacy and memory impact of fractional operators are illustrated through comparisons. Based on fractional parameter values, the study determines important disease-control strategies and shows that vaccinations greatly reduce the spread of measles. By reducing the number of infected people, increasing vaccination coverage lowers the burden of disease on the general population.

Clearance of persistent SARS-CoV-2 associates with increased neutralizing antibodies in advanced HIV disease post-ART initiation

SARS-CoV-2 clearance requires adaptive immunity but the contribution of neutralizing antibodies and T cells in different immune states is unclear. Here we ask which adaptive immune responses associate with clearance of long-term SARS-CoV-2 infection in HIV-mediated immunosuppression after suppressive antiretroviral therapy (ART) initiation. We assembled a cohort of SARS-CoV-2 infected people in South Africa (n = 994) including participants with advanced HIV disease characterized by immunosuppression due to T cell depletion. Fifty-four percent of participants with advanced HIV disease had prolonged SARS-CoV-2 infection (>1 month). In the five vaccinated participants with advanced HIV disease tested, SARS-CoV-2 clearance associates with emergence of neutralizing antibodies but not SARS-CoV-2 specific CD8 T cells, while CD4 T cell responses were not determined due to low cell numbers. Further, complete HIV suppression is not required for clearance, although it is necessary for an effective vaccine response. Persistent SARS-CoV-2 infection led to SARS-CoV-2 evolution, including virus with extensive neutralization escape in a Delta variant infected participant. The results provide evidence that neutralizing antibodies are required for SARS-CoV-2 clearance in HIV-mediated immunosuppression recovery, and that suppressive ART is necessary to curtail evolution of co-infecting pathogens to reduce individual health consequences as well as public health risk linked with generation of escape mutants.

Dynamical analysis of healthcare policy effects in an integrated economic-epidemiological model

We study the static and dynamical properties of a model that describes the interaction between the economic and epidemiological domains. The epidemiological sphere is represented by a susceptible–infected–susceptible model, while the economic domain consists of an overlapping generations model in which the workers correspond to the non-infected population of adults. The productivity of the firms and the propensity to save for retirement of the households are negatively affected by the disease spread. A capital tax is levied and the collected resources are used to curb the spread of the outbreak. We show that multiple endemic steady states can arise from the interaction between the two domains, and different stable endemic attractors can coexist with the stable disease free steady state. We study analytically and numerically the complex dynamics and the evolution of the basins of attraction in the case of multistability. We show that the effect of taxation can be beneficial from both the epidemiological and the economic points of view, as it can give rise to new steady states characterized by reduced shares of infected people and increased capital level, it can simplify the dynamical behaviors and reduce the size of the basins of attraction of those outcomes in which large shares of infected people and low capital levels are observed.

A SIR-UC EPIDEMIC MODEL: THE ANALYSIS OF SUSCEPTIBLE-INFECTED-REMOVED (SIR) EPIDEMIC MODEL WITH THE COVERAGE OF HEALTH INSURANCE (UNCOVERED AND COVERED INDIVIDUALS)

Susceptible-Infected-Removed (SIR) model is a widely used epidemic model that simulates the spread of infectious diseases within a population. It classifies individuals into susceptible, infected, and removed states, with the number of individuals in each state being time-dependent variables denoted by S(t), I(t), and R(t), respectively. The model considers direct contact transmission between infected and susceptible individuals. In developed countries, some people cannot afford medical treatment. In contrary, the recovery rate of infected individual in the population is directly proportional to the number of people receiving medical treatment. Affordable health insurance increases the number of people receiving medical treatment thus insurance should be considered aspect in epidemic model. The main purpose of this research is to analyze the effect of insurance on the SIR epidemic model. This research classifies individuals in both S(t) and I(t) based on their insurance coverage status. This model assumes permanent immunity for R(t), thus it is unnecessary to classify individuals in this state based on their insurance coverage status because they do not spread the disease nor have potential to be re-infected. Numerical simulation is organized to find the effect of insurance in SIR model by analyzing the equilibrium point. The result based on the equilibrium point suggests that the insurance in SIR epidemic model: (1) decrease the I(t) because it accelerate the recovery rate; (2) decrease theR(t) because there is less infected people for recovery; (3) increase the S(t) because there is less infected people to transmit the disease, compared to the SIR model without the insurance.

Epidemiology of HIV infection and AIDS

Background: The expansion of antiretroviral therapy (ART) has resulted in a reduction in numbers of new human immunodeficiency virus (HIV) infections and acquired immunodeficiency syndrome (AIDS)-related deaths worldwide, except in some regions. As people with HIV live long and healthy lives, their health issues are also changing. In this review, we briefly summarize the global HIV epidemic and examine the changing trends in the epidemiology of HIV in Korea.Current Concepts: Since 2013, more than 1,000 new infections have been reported in Korea every year, and after reaching its peak at 1,223 in 2019, approximately 1,000 were reported each year during the coronavirus disease 2019 pandemic. Most newly infected people were men, and the proportion of young people aged 20 to 40 years, men who have sex with men, and foreigners has been steadily increasing. AIDS was the most common cause of death, and cancer, chronic diseases, and suicide were also important causes of death in Korea.Discussion and Conclusion: Achieving sustained ART and viral suppression is approaching the UNAIDS 95-95-95 targets in Korea, but additional research and efforts are required to reduce late presentation and scale-up of HIV diagnosis. Targeted and coordinated prevention programs to prevent HIV among people from key populations are also needed. Additionally, supportive services for older people with HIV, including cancer screening and chronic disease management, and multifaceted efforts to reduce stigma and discrimination against people with HIV are needed in Korea.