Contagious Illness Research Articles

Optimizing chest tuberculosis image classification with oversampling and transfer learning

AbstractTuberculosis (TB) is an extremely contagious illness caused by Mycobacterium tuberculosis. Chest tuberculosis classification is conducted based on a deep convolutional neural network architecture. In this research, a pre‐trained network is utilized to demonstrate the advantage of using the oversampling technique on the classification of TB and compare results with recent research that used the same dataset. Therefore, the dataset consists of 3500 uninfected TB cases and 700 infected with TB. This paper circumvents the imbalance by using the oversampling technique in X‐ray TB images to be fed into several pre‐trained networks for TB classification. The oversampling technique is crucial in enhancing the performance of TB classification compared with other pre‐trained models reported here. Inceptionv3 shows a promising result compared to other pre‐trained models; it achieves 99.94% accuracy, 99.88% precision, 100% recall, and 99.94% F1‐Score.

In-Silico Primer Designing and PCR for Detection of Foot and Mouth Disease Virus (FMDV)

This study focuses on Foot-and-mouth disease (FMD), a contagious viral illness affecting cloven-hoofed animals. Caused by the Foot-and-mouth disease virus (FMDV), it is known for rapid replication and various transmission modes. Clinical symptoms vary among animals, including vesicular lesions and lameness in cattle. Global outbreaks continue to pose challenges. Amid the pandemic crisis, quick and sensitive diagnostic tests are essential. Polymerase Chain Reaction (PCR) and Real-time PCR (qPCR) are precise methods for viral detection. The study aims to design FMDV-specific primers using bioinformatics tools, targeting conserved genome regions. The study successfully designs primers, demonstrating specificity against FMDV. These primers hold promise for commercial PCR-based kits, offering rapid, sensitive, and specific FMDV detection in various samples. Further validation on infected cattle is required. In conclusion, this study presents potential primer sets for FMDV detection, contributing to accurate and efficient diagnosis in disease control efforts.

Estimating the economic burden of influenza on the older population in Malaysia

Background Influenza is a contagious respiratory illness that can cause life-threatening complications among high-risk groups. Estimating the economic burden of influenza is essential to guide policy‐making on influenza vaccination programmes, especially in resource‐limited settings. This study aimed to estimate the economic burden of influenza on older adults (those aged ≥60 years) in Malaysia from the provider’s perspective. Methods The main data source in this study was the MY-DRG Casemix database of a teaching hospital in Malaysia. Cases with principal and secondary diagnoses coded in the International Classification of Diseases version 10 (ICD-10) as J09, J10.0, J10.1, J10.8, J11.0, J11.1, J11.8, J12.8, and J12.9, which represent influenza and its complications, were included in the study. The direct cost of influenza at all severity levels was calculated from the casemix data and guided by a clinical pathway developed by experts. The effect of the variations in costs and incidence rate of influenza for both the casemix and clinical pathway costing approaches was assessed with sensitivity analysis. Results A total of 1,599 inpatient and 407 outpatient influenza cases were identified from the MY-DRG Casemix database. Most hospitalised cases were aged <18 years (90.6%), while 77 cases (4.8%) involved older people. Mild, moderate, and severe cases comprised 56.5%, 35.1%, and 8.4% of cases, respectively. The estimated average annual direct costs for managing mild, moderate, and severe influenza were RM2,435 (USD579), RM6,504 (USD1,549), and RM13,282 (USD3,163), respectively. The estimated total annual economic burden of influenza on older adults in Malaysia was RM3.28 billion (USD782 million), which was equivalent to 10.7% of the Ministry of Health Malaysia budget for 2020. The sensitivity analysis indicated that the influenza incidence rate and cost of managing severe influenza were the most important factors influencing the total economic burden. Conclusions Overall, our results demonstrated that influenza imposes a substantial economic burden on the older Malaysian population. The high cost of influenza suggested that further efforts are required to implement a preventive programme, such as immunisation for older people, to reduce the disease and economic burdens.

Complete genome sequencing and comparative genomic analysis of three donkey Streptococcus equi subsp. equi isolates.

Streptococcus equi subspecies equi (S. equi) is the causative agent of strangles, which is one of the most common and highly contagious respiratory infectious illnesses in horses. Streptococcus equi (S. equi) is a horse-specific pathogen that originated from the closely related zoonotic pathogen Streptococcus equi subspecies zooepidemicus (S. zooepidemicus). Despite decades of research, the movement of genetic material across host-restricted diseases remains a mystery. Three S. equi donkey isolates (HTP133, HTP232, and HT1112) were recently isolated from a strangles epidemic on donkey farms in China's Xinjiang Province. In this study, we performed a comprehensive comparative analysis of these isolates using whole genome sequencing and compared them to the published genomic sequences of equine strain S. equi 4047 to uncover evidence of genetic events that shaped the evolution of these donkey S. equi isolates' genomes. Whole genome sequencing indicated that both strains were closely related, with comparable gene compositions and a high rate of shared core genomes (1788-2004). Our comparative genomic study indicated that the genome structure is substantially conserved across three donkey strains; however, there are several rearrangements and inversions when compared to the horse isolate S. equi 4047. The virulence factors conveyed by genomic islands and prophages, in particular, played a key role in shaping the pathogenic capacity and genetic diversity of these S. equi strains. Furthermore, we discovered that the HT133 isolate had a strong colonization ability and increased motility; the HT1112 isolates had a significantly higher ability for antimicrobial resistance and biofilm formation, and the HT232 isolate gained pathogenic specialization by acquiring a bacteriophage encoding hyaluronate lyase. In summary, our findings show that genetic exchange across S. equi strains influences the development of the donkey S. equi genome, offering important genetic insights for future epidemiological studies of S. equi infection.

National Survey of Patient Safety Experiences in Hospital Medicine During the COVID-19 Pandemic

During the COVID-19 pandemic, hospitals were caring for increasing numbers of patients with a novel and highly contagious respiratory illness, forcing adaptations in care delivery. The objective of this study was to understand the impact of these adaptations on patient safety in hospital medicine. The authors conducted a nationwide survey to understand patient safety challenges experienced by hospital medicine clinicians during the COVID-19 pandemic. The survey was distributed to members of the Society of Hospital Medicine via an e-mail listserv. It consisted of closed- and open-ended questions to elicit respondents' experience in five domains: error reporting and communication, staffing, equipment, personal protective equipment (PPE) and isolation practices, and infrastructure. Quantitative questions were reported as counts and percentages; qualitative responses were coded and analyzed for relevant themes. Of 196 total responses, 167 respondents (85.2%) were attending physicians and 85 (43.8%) practiced at teaching hospitals. Safety concerns commonly identified included nursing shortages (71.0%), limiting patient interactions to conserve PPE (61.9%), and feeling that one was practicing in a more hazardous environment (61.4%). In free-text responses, clinicians described poor outcomes and patient decompensation due to provider and equipment shortages, as well as communication lapses and diagnostic errors resulting from decreased patient contact and the need to follow isolation protocols. Efforts made to accommodate shortages in staff and equipment, adapt to limited PPE, and enforce isolation policies had unintended consequences that affected patient safety and created a more hazardous environment characterized by less efficient care, respiratory decompensations, diagnostic errors, and poor communication with patients.

A review on in silico virtual screening methods in COVID-19 using anticancer drugs and other natural/chemical inhibitors.

The present coronavirus disease 2019 (COVID-19) pandemic scenario has posed a difficulty for cancer treatment. Even under ideal conditions, malignancies like small cell lung cancer (SCLC) are challenging to treat because of their fast development and early metastases. The treatment of these patients must not be jeopardized, and they must be protected as much as possible from the continuous spread of the COVID-19 infection. Initially identified in December 2019 in Wuhan, China, the contagious coronavirus illness 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Finding inhibitors against the druggable targets of SARS-CoV-2 has been a significant focus of research efforts across the globe. The primary motivation for using molecular modeling tools against SARS-CoV-2 was to identify candidates for use as therapeutic targets from a pharmacological database. In the published study, scientists used a combination of medication repurposing and virtual drug screening methodologies to target many structures of SARS-CoV-2. This virus plays an essential part in the maturation and replication of other viruses. In addition, the total binding free energy and molecular dynamics (MD) modeling findings showed that the dynamics of various medications and substances were stable; some of them have been tested experimentally against SARS-CoV-2. Different virtual screening (VS) methods have been discussed as potential means by which the evaluated medications that show strong binding to the active site might be repurposed for use against SARS-CoV-2.

Forecasting Covid-19 outbreak using CLR optimized stacked generalization computational models

Coronavirus disease 2019 (Covid-19) is a contagious pandemic illness characterized by severe acute respiratory syndrome. The daily rise of Covid-19 instances and fatalities has resulted in worldwide lockdowns, quarantines and social distancing. Researchers have been working incredibly to develop precisely focused strategies to warfare the Covid-19 pandemic. This study aims to develop a cyclical learning rate optimized stacked generalization computational models (CLR-SGCM) for predicting Covid-19 pandemic outbreaks. Stacked generalization framework performs hierarchical two-phase prediction. In the first phase, deep learning models namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and statistical model Auto Regressive Integrated Moving Average (ARIMA) are used as sub models to create pooled datasets (PDS). Cyclical learning rate (CLR) optimizer is used to enhance learning rate of ensemble deep learning models namely LSTM and GRU. In the second phase, meta learner is trained on dataset PDS using four different regression algorithms such as linear regression, polynomial regression, lasso regression and ridge regression to perform the final predictions. Time series data from India, Brazil, and the United States were utilized to forecast the Covid-19 pandemic outbreak. According to experimental finding, the presented stacking ensemble model outpaces the individual learners in terms of accuracy and error rate.

Influenza A and B Viruses in Fine Aerosols of Exhaled Breath Samples from Patients in Tropical Singapore.

Influenza is a highly contagious respiratory illness that commonly causes outbreaks among human communities. Details about the exact nature of the droplets produced by human respiratory activities such as breathing, and their potential to carry and transmit influenza A and B viruses is still not fully understood. The objective of our study was to characterize and quantify influenza viral shedding in exhaled aerosols from natural patient breath, and to determine their viral infectivity among participants in a university cohort in tropical Singapore. Using the Gesundheit-II exhaled breath sampling apparatus, samples of exhaled breath of two aerosol size fractions ("coarse" > 5 µm and "fine" ≤ 5 µm) were collected and analyzed from 31 study participants, i.e., 24 with influenza A (including H1N1 and H3N2 subtypes) and 7 with influenza B (including Victoria and Yamagata lineages). Influenza viral copy number was quantified using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Infectivity of influenza virus in the fine particle fraction was determined by culturing in Madin-Darby canine kidney cells. Exhaled influenza virus RNA generation rates ranged from 9 to 1.67 × 105 and 10 to 1.24 × 104 influenza virus RNA copies per minute for the fine and coarse aerosol fractions, respectively. Compared to the coarse aerosol fractions, influenza A and B viruses were detected more frequently in the fine aerosol fractions that harbored 12-fold higher viral loads. Culturable virus was recovered from the fine aerosol fractions from 9 of the 31 subjects (29%). These findings constitute additional evidence to reiterate the important role of fine aerosols in influenza transmission and provide a baseline range of influenza virus RNA generation rates.

The impacts of meteorological factors on the incidence of hand, foot, and mouth disease in China: An interactive perspective

Hand, foot, and mouth disease (HFMD) is a contagious illness and a matter of public health concern globally. It has a widespread impact in China, where it poses a significant risk to the health of young children. Previous research has discovered that the occurrence and spread of HFMD are closely linked to meteorological factors, but the specifics of these intricate nonlinear relationships and how they interact with the incidence of HFMD are still uncertain. In the present study, we filled this gap in knowledge by first obtaining data on hand, foot, and mouth disease from the National Health Commission of China alongside real-time surveillance data from meteorological stations. We then used Spearman's correlation analysis to identify the variables significantly related to HFMD, investigating the existence of co-curvilinearity among the contributing factors by using the independent effects model in the generalized additive model to analyze both the contribution of individual factors and their combined effects on the incidence of HFMD. The interactions between meteorological factors were then modeled to investigate their influence on HFMD. The results showed that, in descending order of effect size, air temperature (AT), sunshine duration (SD), wind speed (WS), and barometric pressure (BP) had a significant nonlinear impact on the incidence of HFMD, which displayed a fluctuating increase in line with rising temperatures. The interactions of (1) high temperature and low air pressure and (2) low wind speed and poor sunshine had the largest influence on HFMD. In descending order, the strengths of the interaction effects between factors were as follows: AT ∼ SD > AT ∼ WS > AT ∼ BP > WS ∼ BP > WS ∼ SD > BP ∼ SD. Our findings will inform policy initiatives contributing to the Healthy China strategy and assist in the prevention and control of HFMD by enabling the prediction and early warning of disease outbreaks.

On-chip screening of SARS-CoV-2 cDNA by LAMP-integrated rotational diffusometry

The unprecedented pandemic has raised the demand for prompt, precise, and large-scale virus detection techniques to control the transmission of contagious illnesses. In this study, a loop-mediated isothermal amplification (LAMP) based on-chip platform was developed to address this challenge using rotational diffusometry and functionalized Janus particles. A recombinant plasmid containing a cDNA sequence of the SARS-CoV-2 non-structural protein 2 (nsp2) gene was employed here as a proof-of-concept for COVID-19 detection. Specifically, designed primers and the functionalized Janus particles were simultaneously loaded on a microfluidic chip to perform the LAMP reaction on a hot plate. The optimal Janus particle concentrations for diffusometric analysis were thoroughly validated, and the performance of the on-chip LAMP reaction was assessed using thermal image analysis. Utilization of the highly sensitive rotational diffusometry achieved a limit of detection of 1 pg/μL in just 10 min with a sample volume of 20 μL. Our method delivered a tenfold higher sensitivity than the conventional method by utilizing only half of its usual required time. Overall, this study proposes a potential nucleic acid (NA) amplification device to aid the rapid diagnosis of various diseases by modifying the primers for different target genes.

COVID-19 spatio-temporal forecast in England

The 2019 novel coronavirus disease (COVID-19, SARS-CoV-2) being contagious illness with allegedly high potential for global transmission, low potential for morbidity and fatality, and certain impact on global public health.This study describes a novel bio-system reliability spatio-temporal approach, that is especially appropriate for multi-regional environmental, biological and health systems and that, when observed for a sufficient amount of time, produces a reliable long-term forecast of the likelihood of an outbreak of a highly pathogenic virus. Conventional statistical approaches do not have the benefit of effectively handling large regional dimensionality and cross-correlation between various regional observations. These methods deal with temporal observations of multi-regional phenomena. The most afflicted districts of England's COVID-19 daily counts of reported patients were used for this investigation. In order to extract the essential data from dynamically observed patient numbers while taking into consideration pertinent geographical mapping, this study utilized recently developed bio-reliability methodology. With the use of the spatio-temporal approach described in this study, future epidemic outbreak risks for multi-regional public health systems may be predicted with sufficient accuracy.

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.

TECOVIRIMAT: A COMPREHENSIVE REVIEW OF NOVEL DRUG FOR MONKEYPOX DISEASE

Orthopoxvirus infections caused by pathogenic agents require therapeutic intervention. Animal models of orthopoxvirus disease are crucial for assessing the effectiveness of antiviral medications and determining the right dose and duration of treatment in the absence of disease-affected individuals. Antiviral drugs for the treatment of severe orthopoxvirus infections have been developed as a result of research on smallpox preparedness. Concerns for the public's health have been raised by the monkeypox virus, a new zoonotic orthopoxvirus that can infect humans and produce a serious, contagious illness. An infection with the monkeypox virus causes a systemic, febrile rash condition that resembles smallpox in many ways. Monkeypox virus is an emergent human pathogen. It can nonetheless result in severe morbidity and mortality in humans while being less deadly than smallpox. In this review, we have explored a new antiviral drug, Tecovirmat. This drug prevents the development of the extracellular enveloped virus required for cell-to-cell transmission. It acts by limiting the production of egress-competent enveloped virions, which are necessary for the virus's spread within the host, by decreasing the action of the orthopoxvirus VP37 envelope-wrapping protein. It was approved by US-FDA for the treatment of smallpox as per Animal Rule, which bases marketing approval on a product's effectiveness in pertinent animal models.

Measles: Contemporary considerations for the emergency physician.

Measles, or rubeola, is a highly contagious acute febrile viral illness. Despite the availability of an effective vaccine since 1963, measles outbreaks continue worldwide. This article seeks to provide emergency physicians with the contemporary knowledge required to rapidly diagnose potential measles cases and bolster public health measures to reduce ongoing transmission.

Pursuing safety in social connection regulates the risk-regulation, social-safety, and behavioral-immune systems.

A new goal-systems model is proposed to help explain when individuals will protect themselves against the risks inherent to social connection. This model assumes that people satisfy the goal to feel included in safe social connections-connections where they are valued and protected rather than at risk of being harmed-by devaluing rejecting friends, trusting in expectancy-consistent relationships, and avoiding infectious strangers. In the hypothesized goal system, frustrating the fundamental goal to feel safe in social connection sensitizes regulatory systems that afford safety from the risk of being interpersonally rejected (i.e., the risk-regulation system), existentially uncertain (i.e., the social-safety system), or physically infected (i.e., the behavioral-immune system). Conversely, fulfilling the fundamental goal to feel safe in social connection desensitizes these self-protective systems. A 3-week experimental daily diary study (N = 555) tested the model hypotheses. We intervened to fulfill the goal to feel safe in social connection by repeatedly conditioning experimental participants to associate their romantic partners with highly positive, approachable words and images. We then tracked how vigilantly experimental versus control participants protected themselves when they encountered social rejection, unexpected behavior, or contagious illness in everyday life. Multilevel analyses revealed that the intervention lessoned self-protective defenses against each of these risks for participants who ordinarily felt most vulnerable to them. The findings provide the first evidence that the fundamental goal to feel safe in social connection can co-opt the risk-regulation, social-safety, and behavioral-immune systems as independent means for its pursuit. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Complete Blood Count (CBC) in COVID-19 Patients Attending at Bangladesh Institute of Health Sciences (BIHS) General Hospital, Dhaka, Bangladesh

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the infectious agent that causes Coronavirus Disease 2019 (COVID-19), a contagious illness. The epidemic has continued since it was first discovered in Wuhan, Hubei, China, in December 2019. The WHO estimated that 800 million people worldwide (or one in ten) may have COVID-19 infection on October 5. More than 1.07 million fatalities and 37 million cases have been documented as of October 11, 2020, spanning 188 nations and territories; more than 25.7 million patients have recovered. Fever, coughing, exhaustion, shortness of breath or other breathing issues, and a loss of taste and smell are typical symptoms. Acute respiratory distress syndrome (ARDS), which can be brought on by cytokine storm, multi-organ failure, septic shock, and blood clots, can affect some people while the majority only have moderate symptoms. One to fourteen days may pass during the incubation period. The aim of this study was to observe the Complete blood count in COVID-19 & Non COVID-19 Patients in Bangladesh. A descriptive cross-sectional study was conducted for the study. Data were collected from Bangladesh Institute of Health Sciences (BIHS) General Hospital and Hematology Department of Bangladesh University of Health Sciences (BUHS). A pretested semi-structured questionnaire was used for the data collection. The standard method of diagnosis is by real-time reverse transcription polymerase chain reaction (RT-PCR) from a nasopharyngeal swab. The results showed that, among 100 patients, 50% patient was Covid-19 positive &50% patient was negative. In comparison between Covid-19 positive & negative CBC result showing that Hb% remain almost same, total count is slightly increased in covid-19 positive patient than non-covid-19. RBC are found in Normal range for both positive and negative patients. Platelet count & PCV are lower in Covid-19 positive patient than negative patient. Neutrophil are significantly raised in Covid-19 positive patient. Lymphocytes are significantly decreased in Covid-19 positive patient than negative. In this study males are affected 64% & females are 36% so the ratio between male& female is 3:2. More positive patients were found in the age between 50-70 years & has risk among 40-80 years. According to this study, it can be concluded that there is an association among neutrophil, lymphocyte and other cell status in clinically in COVID-19 & non COVID-19 Patients.

Dynamics of Cytokine, SARS-CoV-2-Specific IgG, and Neutralizing Antibody Levels in COVID-19 Patients Treated with Convalescent Plasma.

Coronavirus disease 2019 (COVID-19) is a contagious illness worldwide. While guidelines for the treatment of COVID-19 have been established, the understanding of the relationship among neutralizing antibodies, cytokines, and the combined use of antiviral medications, steroid drugs, and convalescent plasma therapy remains limited. Here, we investigated the connection between the immunological response and the efficacy of convalescent plasma therapy in COVID-19 patients with moderate-to-severe pneumonia. The study included a retrospective analysis of 49 patients aged 35 to 57. We conducted clinical assessments to determine antibody levels, biochemical markers, and cytokine levels. Among the patients, 48 (98%) were discharged, while one died. We observed significantly higher levels of anti-nucleocapsid, anti-spike, and neutralizing antibodies on days 3, 7, and 14 after the transfusion compared to before treatment. Serum CRP and D-dimer levels varied significantly across these four time points. Moreover, convalescent plasma therapy demonstrated an immunoregulatory effect on cytokine parameters, with significant differences in IFN-β, IL-6, IL-10, and IFN-α levels observed at different sampling times. Evaluating the cytokine signature, along with standard clinical and laboratory parameters, may help to identify the onset of a cytokine storm in COVID-19 patients and determine the appropriate indication for anti-cytokine treatment.

Acceptance and awareness regarding influenza vaccine among healthcare workers in a tertiary care centre, Kerala

Background: The seasonal Influenza is a contagious respiratory illness. The most important step in preventing flu is to get a flu vaccination each year along with everyday preventive actions. The objective of this research was to study the acceptance and awareness of Influenza vaccine among health care workers of a tertiary care centre, Kerala and to explore the preventive practices. Methods: This study was a hospital based cross sectional study conducted among 204 health care workers in a tertiary centre in Kerala. A simple random sampling technique was adopted. Data was collected using self-administered pretested semi structured questionnaires. Results: 165 (80.9%) are aware of the Influenza vaccine while 39 (19.1%) are not aware of the Influenza vaccine. 30 (14.7%) has received at least one dose of Influenza vaccine in last five years while 174 (85.2%) has not received the vaccine. 161(78.9%) says they are willing to get vaccinated against Influenza while 43(21.1%) are not willing to get vaccinated. Conclusions: Even though there is good awareness about Influenza vaccine, the number of vaccine recipients is less than ideal. However, majority are willing to take Influenza vaccine in the future which shows good acceptance.

Utilization of street-based COVID-19 vaccination clinics in Phoenix's homeless population.

The novel Coronavirus (COVID-19) is a highly contagious viral illness that has caused the most significant global health crisis in recent human history. Individuals experiencing homelessness represent one of the more vulnerable populations for COVID-19 infection and morbidity. Amongst individuals experiencing homelessness in Phoenix, a student-led interprofessional organization called Street Medicine Phoenix (SMP) sought to both reduce the risk of COVID-19 transmission and morbidity/mortality related to infection. Through collaborations with the Maricopa County Department of Public Health and various community organizations, SMP developed a format for street-based vaccination clinics. SMP deployed these clinics on numerous occasions to the streets directly surrounding the community homeless shelter, allowing SMP to vaccinate individuals directly in their encampments. Through SMP's efforts starting in February 2021, 400 individuals experiencing homelessness have received at least one COVID-19 vaccine. Challenges encountered included low health literacy, lack of established rapport and trust, low vaccine confidence, difficulty verifying patients' vaccination status, difficulty obtaining sufficient information from patients to create a record in the Arizona State Immunization Information System (ASIIS), monitoring patients post-vaccination, transporting vaccine supplies from encampment to encampment, and lack of patient awareness of the mobile vaccine clinic services. Despite challenges, SMP's outreach efforts have demonstrated the feasibility and importance of mobile public health services to reach homeless encampments, particularly mobile vaccination clinics in response to disease outbreaks, and the necessity of strategic partnerships with community agencies to effectively meet the needs of underserved populations.

Quality Analysis of COVID-19 Drugs through Graph Polynomial

Coronavirus disease-19 (COVID-19), a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related sickness that has already claimed millions of lives throughout the world, is one of the most contagious illnesses in known human history. At this time, neither vaccines nor effective medicines exist that can effectively cure COVID-19 patients or stop the virus from spreading. Even though the creation of drugs and vaccines may take a while, scientific communities all over the world have reacted rapidly and have been working ceaselessly on them. In light of this uncertainty, repurposing currently existing antiviral drugs may be the best line of action to speed the creation of effective SARS-CoV-2 therapeutics. Updated information is also required on potential pharmacological targets, therapeutic and vaccine development, and results. In this study, we propose topological descriptors and status distance-based polynomials for many COVID-19 antiviral drugs. The quantitative structure–property relationship (QSPR) connection of the proposed topological indices (TIs) is also investigated. Curve fitting models are obtained and looked at for the physico-chemical properties of the COVID-19 drugs in line with the specified indices. Our models and results may contribute to the identification of innovative therapeutics for the treatment of COVID-19.