Spread Of COVID-19 Disease Research Articles

A study on passenger flow model and simulation in aspect of COVID-19 spreading on public transport bus stops

Public transport during COVID-19 has been crucial in ensuring the safety and health of both passengers and staff while maintaining essential public transport services. Currently public transport is gradually resuming its operations, the pandemic's influence is expected to persist for a long time. The vast majority of studies in this aspect concern the likelihood of spreading the virus inside the means of transport during travel. Nevertheless, there exists a substantial body of articles addressing the manner in which passenger movement within public transport systems has been impacted by the safety concerns and altered satisfaction levels following the propagation of the pandemic. This paper presents a model that accurately represents how passengers move through different parts of a public transport system, such as a bus or train station and stops. This model takes into account factors like how long it takes for passengers to board and exit a vehicle, how they move through different parts of the stops, and how their movements are affected by factors like crowding and delays. To reduce the risk of transmission on public transport focused on bus stops areas, the research paper formulated a passenger flow model using simulation programs like PTV Vissim and FlexSim with assumptions on minimum distance and concept of area cross sections. These programs were used to simulate passenger exchange scenarios, using data collected from real data. The paper aimed to develop a passenger exchange model that could reduce the risk of infection. By understanding the passenger flow model and how passengers interact with the public transport system, we can implement effective measures to minimize the spread of COVID-19 and other infectious diseases.

THE IMPACT OF THE PANDEMIC CRISIS ON CHANGES IN THE MANAGEMENT OF HOSPITALITY BUSINESSES IN SLOVAKIA

The main topic of the paper is the impact of the global pandemic, which arose because of the spread of COVID-19 disease to accommodation and catering facilities in Slovakia. The paper consists of three parts. In the first part of the paper, we define the basic concepts and information related to the studied issue, which were obtained through literary research mainly  of foreign Internet sources. In the second part of the thesis, we focus on identifying factors influencing the operation of accommodation and catering facilities during the period of global crisis and proposing solutions to mitigate their impact. We drew attention to the current crisis, which arose because of the outbreak of the global pandemic and the war in Ukraine.  Hospitality establishments have been hardest hit by this crisis, so they have also become the subject of our investigation. In the third part of the paper, we present the results of the analysis of primary data obtained through a questionnaire survey from persons in leading positions of accommodation and catering facilities in Slovakia.  The research results were processed using mathematical-statistical methods, presented graphically and conclusions and recommendations were drawn from them.

Real-time face mask position recognition system using YOLO models for preventing COVID-19 disease spread in public places

Real-time face mask position recognition system using YOLO models for preventing COVID-19 disease spread in public places

Family Child Care Program Closure in Alabama During the COVID-19 Pandemic

Family child care (FCC) is uniquely positioned to address challenges with insufficient early care and education supply and access in the United States. FCC programs were steadily declining before COVID-19, and many child care programs, both center- and home-based, were at risk of closure early in the pandemic. This study examines closure among Alabama FCC providers during COVID-19. Specifically, we examine the timing and predictors of FCC providers’ closure using discrete time-hazard modeling. We analyze administrative data for the 788 FCC programs licensed in March 2020. Over the following 28 months, FCC closure risk was closely linked with COVID-19 disease spread trends. Providers who participated in child care subsidy or national accreditation had lower closure risk than those who did not. The protective effects of subsidy participation were amplified in lower-opportunity communities. Results have implications for mitigating FCC decline and identifying resources to support FCC providers’ continued operation.

Real-time face mask position recognition system using YOLO models for preventing COVID-19 disease spread in public places

Real-time face mask position recognition system using YOLO models for preventing COVID-19 disease spread in public places

A New UAV-Based Social Distance Detector for COVID-19 Outbreaks Reduction, Using IoT, Computer Vision and Deep Learning Technologies

Nowadays, we are living in a dangerous environment and our health system is under the threatened causes of Covid19 and other diseases. The people who are close together are more threatened by different viruses, especially Covid19. In addition, limiting the physical distance between people helps minimize the risk of the virus spreading. For this reason, we created a smart system to detect violated social distance in public areas as markets and streets. In the proposed system, the algorithm for people detection uses a pre-existing deep learning model and computer vision techniques to determine the distances between humans. The detection model uses bounding box information to identify persons. The identified bounding box centroid's pairwise distances of people are calculated using the Euclidean distance. Also, we used jetson nano platform to implement a low-cost embedded system and IoT techniques to send the images and notifications to the nearest police station to apply forfeit when it detects people’s congestion in a specific area. Lastly, the suggested system has the capability to assist decrease the intensity of the spread of COVID-19 and other diseases by identifying violated social distance measures and notifying the owner of the system. Using the transformation matrix and accurate pedestrian detection, the process of detecting social distances between individuals may be achieved great confidence. Experiments show that CNN-based object detectors with our suggested social distancing algorithm provide reasonable accuracy for monitoring social distancing in public places, as well.

Public Health Safety in Community Living Circles Based on a Behavioral Motivation Perspective: Theoretical Framework and Evaluation System.

Public health problems, such as the spread of COVID-19 and chronic diseases, are mainly caused by the daily life activities of community residents. Therefore, there is a need to build a healthy and safe community living circle through the evaluation of health behaviors in daily life. This paper proposes a theoretical framework and evaluation system for public health safety in community living circles, from a behavioral motivation perspective. Firstly, based on the behavioral motivation theory, a theoretical framework for the study of public health safety in community living circles is constructed from the perspective of the "project-activity-health" coupling relationship network, regarding community residents' daily life activities. Then, a public health safety evaluation system for community living circles is proposed based on this framework, which includes the following: (1) identifying the scope of community living circles based on Spatio-temporal Activities Analysis; (2) Based on the theory of protection motivation, a health behavior evaluation model based on the three elements of "spatial and temporal geographical environment-daily life activities-public health safety" is established; (3) Based on the hierarchy of public health problems, a public health safety evaluation model of the community living circle is established. The behavioral motivation-based evaluation system explores a new approach and research paradigm for community-scale public health safety theory; this will help to achieve the goal of "healthy communities" when further empirical evidence is available.

Perception of Medical Students towards online Teaching During the Covid-19 Pandemic from M.R Medical College, Kalaburgi, Karnataka

Introduction: The nationwide lockdown restrictions to control the spread of Covid-19 disease have impacted all aspects of life wherein medical education has also been affected. Such measures have resulted in a sudden shift in teaching methods towards online teaching. Timely feedback from the students and faculty and analysis of the feedback will improve the effectiveness of online teaching and learning. Aim: 1. To assess the barriers and benefits that students found during online classes. 2. To assess the challenges faced by the students in transition from traditional to online learning.Material and Methods: A cross sectional study for 2 months (July 2021 - August 2021) at Mahadevappa Rampure Medical College, Kalaburgi. among 150 Students from the 3rd year MBBS, Data was collected from a Questionnaire in google form format from the students. Results: 20% of participants found online learning was not effective for medical subjects. 60% found it to be stressful. 76% of students had device of their own and 14% had to share with others. Conclusion: It is important therefore that medical colleges are aware of the barriers and provide appropriate solutions to the development of this type of learning and support the use of online learning amongst staff and students. Timely feedback from the students and faculty and analysis of the feedback will improve the effectiveness of online teaching and learning.

A statistical look at the COVID-19 vaccine development and vaccine policies.

The global outbreak of COVID-19 caused by the SARS-CoV-2 virus elicited immense global interest in the development and distribution of safe COVID-19 vaccines by various governments and researchers, capable of stopping the spread of COVID-19 disease. After COVID-19 was declared a global pandemic, several vaccines have been developed for emergency use authorization. The accelerated development of the vaccines was attributed to many factors but mainly by capitalizing on years of research and technology development. Although several countries tried to develop COVID-19 vaccines only a few countries succeeded. Therefore, we applied statistical methods to find factors that have contributed to the fast development of COVID-19 vaccines. All 11 countries that developed vaccines were considered and chose other 24 countries for comparison purposes according to different criteria of their R&D. Fourteen R&D indicator variables that are a measure of the R&D for all countries [World Development Indicators (WDI)] were obtained from the World Bank DataBank and data on the COVID-19 vaccine R&D were obtained from The Knowledge Portal of the Graduate Institute Geneva and Global Health Center. The World Bank records WDI yearly, and 2019 was chosen because of a few missing values. Also, different vaccine policies were adopted by different countries during the COVID-19 vaccination period, producing different impacts of vaccinations on the population. So, we applied the generalized estimating equations (GEE) approach to find policies that contributed greatly to decreasing the spread of COVID-19 using data from the Oxford COVID-19 Government Response Tracker (OxCGRT) and age-specific vaccination data from the European Center for Disease and Prevention and Control. Logistic regression, two-sample t-test, and Wilcoxon rank-sum test found scientific and technical journals, liability, and COVID-19 Vaccine R&D Funding (investment in pharmaceutical industry US$) are significantly associated with fast COVID-19 vaccine development. Vaccine prioritization and government vaccine financial support were significantly associated with COVID-19 daily cases. The impact of vaccination on lowering the rate of new cases is greatly observed among the mid-aged populations (25-64 years) and lower or non-significant among the younger (<25 years) and (>65 years) older populations. Therefore, these age-groups especially > 79 can be prioritized during vaccine roll-out.

Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 2

Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for future pandemics through spatial algorithms and systems to collect, capture, curate and analyze complex, multi-scale human movement data to solve problems such as infectious diseases prediction, contact tracing, and risk assessment. In exploring and deepening the conversation around this topic, the five articles included in the second volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to close contact modeling, infectious diseases spread prediction, mobility analysis, effective testing and intervention strategies. Rather than focusing on a narrow set of problems, these articles provide a glimpse into the diverse possibilities of leveraging spatial and spatiotemporal data for pandemic preparedness.

Role of Tocilizumab in Combating Cytokine Storm in Moderate &amp; Severe Covid 19 Patients: Retrospective Observational Study

Background: The wide spread of COVID-19 disease and mortality associated with it as never seen before urged the medical research field to formulate and investigate the efficacy of various new drugs alongside with the existing drugs.&#x0D; Objective: To evaluate the potential benefits of Tocilizumab (TCZ) in addition to the Standard of Care (SoC) in reducing mortality in moderate &amp; severely ill COVID 19.&#x0D; Methodology: Study population is sorted by 1:1 matching depending on the High-Resolution Computed Tomography (HRCT) Chest CT Severity Score. Outcome is measured as deaths, discharge with or without Long Term Oxygen Therapy (LTOT) along with clinical improvement of the patient measured using 9 points ordinary scale score of WHO and length of stay in Intensive Care Unit (ICU) post administration &amp; levels of inflammatory markers (CRP, IL 6) on day of administration- baseline (D0), 1- &amp; 3-days post administration (D+1 &amp; D+3). The group received TCZ+SoC is considered as Test Group and the group received only SoC as Control Group.&#x0D; Results: Deaths and discharge with LTOT were slightly higher in test group with OR 1.68 &amp; 1.63 respectively. Clinical improvement in terms of 9-point ordinary scale score &amp; difference in the levels of CRP &amp; IL 6 was insignificant in both the groups. No difference in post administration ICU stay is observed between the groups.&#x0D; Conclusions: Our study concludes no marked benefits with the addition of TCZ to the SoC in treating moderate and severe COVID 19 patients.

SEIR Model Analysis for The Spread of Covid-19 Disease Post New Normal Using Euler Method and Matlab-Assisted Heun Method

In early 2020 Corona Virus Disease 2019 (COVID-19) was declared by WHO as a pandemic and the Government of Indonesia based on Presidential Decree Number 11 of 2020 concerning the Determination of Public Health Emergency of Corona Virus Disease 2019 (COVID-19). On this basis, this study aims to find out the solution to the numerical solution of the SEIR epidemic on the rate of Coronavirus Disease (COVID-19) by using the Euler method and the Heun method and to find out the design of a mathematics lesson plan in high school that is in accordance with the concept of the numerical method. In analyzing the spread of the Covid-19 disease after the new normal, a mathematical model of the Covid-19 disease was developed using the SEIR type with the stages of Literature Study, Mathematical Modeling, Model Analysis, Simulation and Drawing conclusions. The target to be achieved in this research is to produce mathematical modeling from the results of the analysis that carried out, reports can be confirmed and report results can be published in international journals. This research will produce TKT types of social humanities and education with the results of the status of R&amp;D results are important and significant for supporting decisions and policies. Keywords : Model SEIR, EULER, HEUN, MATLAB. DOI: 10.7176/JEP/13-33-16 Publication date: November 30 th 2022

Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 1

Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for future pandemics through spatial algorithms and systems to collect, capture, curate, and analyze complex, multi-scale human movement data to solve problems such as infectious diseases prediction, contact tracing, and risk assessment. In exploring and deepening the conversation around this topic, the eight articles included in the first volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to infectious diseases simulation, risk prediction, response policy design, mobility analysis, and case diagnosis. Rather than focusing on a narrow set of problems, these articles provide a glimpse into the diverse possibilities of leveraging spatial and spatiotemporal data for pandemic preparedness.

Recovery material from a new designed surgical face mask: A complementary approach based on mechanical and thermo-chemical recycling

The usage of disposable face mask to control the spread of COVID-19 disease has led to the alarming generation of a huge amount of plastic waste in a short span of time. On other hand, face masks are made of high-quality thermoplastic polymers that could be recovered and converted into valuable products. The aim of this study is to investigate a complementary approach for the recycling of face mask in lab-scale plants: the mechanical recycling of the filter in polypropylene (PP) and the chemical recycling of the whole face mask. For this purpose, a new designed surgical face mask was chemically and physically characterized. The results shows that the face mask was composed of 92.3 wt% high grade PP (filter), very similar to virgin PP but with a high melt volume index (MVI, 385 cm3/10 min) due to its non-woven manufacturing. The PP from face mask was mixed with recycled virgin PP in order to obtain a MVI suitable for the extrusion process and recycled as filament for 3D printing. This filament was used to print a specimen with a very similar visual quality of that printed with a commercial PP filament. Simultaneously, the whole face mask underwent a pyrolysis process to produce new feedstocks or fuels. Low-cost catalysts derived from coal fly ash (CFA) were employed to enhance the production of light hydrocarbons. In particular, the synthetized acid X zeolite (HX/CFA) improved the yield of light fractions up to 91 wt% (79 wt% for thermal pyrolysis) and the quality of the light oil with the 85% of C6–C10 (55% for thermal pyrolysis). Furthermore, HX/CFA decreased the degradation temperature of PP to 384 °C versus 458 °C of thermal cracking.

Measuring sensitivity to social distancing behavior during the COVID-19 pandemic

Social distancing remains an effective nonpharmaceutical behavioral interventions to limit the spread of COVID-19 and other airborne diseases, but monitoring and enforcement create nontrivial challenges. Several jurisdictions have turned to “311” resident complaint platforms to engage the public in reporting social distancing non-compliance, but differences in sensitivity to social distancing behaviors can lead to a mis-allocation of resources and increased health risks for vulnerable communities. Using hourly visit data to designated establishments and more than 71,000 social distancing complaints in New York City during the first wave of the pandemic, we develop a method, derived from the Weber-Fechner law, to quantify neighborhood sensitivity and assess how tolerance to social distancing infractions and complaint reporting behaviors vary with neighborhood characteristics. We find that sensitivity to non-compliance is lower in minority and low-income neighborhoods, as well as in lower density areas, resulting in fewer reported complaints than expected given measured levels of overcrowding.

SARS-CoV-2 Wastewater Based Epidemiology in Low and Lower Middle-Income Countries: A Systematic Review

BACKGROUND AND AIM: Wastewater based epidemiology (WBE) is a promising tool to monitor the prevalence of COVID-19 disease at community level through detection of SARS-CoV-2 RNA in wastewater. Although WBE has been largely adopted in developed countries as complementary to clinical surveillance, the approach is still not widely applied in low-and-lower-middle-income countries (LMICs) owing to number of drawbacks. Despite these limitations, a small number of studies on wastewater based epidemiology for SARS-CoV-2 detection have been conducted in LMICs. The current systematic review provides an overview of the efficacy of WBE in LMICs. METHODS: We conducted keyword searches in PubMed, Web of Science, and Scopus for articles published between January 2020 to February 2022. A total of 2,057 relevant articles were identified, and 21 met our inclusion/exclusion criteria. RESULTS: The most common analytical steps used in included studies are sampling, storage, concentration, extraction, and detection. Studies utilized either composite/grab raw sludge or influents mostly from wastewater treatment plants, which covered a population of one to ten million. Among the studies, Among the studies, the most commonly used concentration technique was Poly Ethylene Glycol (PEG), and the most frequently targeted gene was the N gene. The overall sample positivity was found 81.18%, with viral loads of 40 to 45,000 copies/L. Most of the studies found positive correlation between SARS-CoV-2 viral loads in wastewater and the detected COVID-19 cases. Besides, few studies found higher signals in wastewater with a time lag of one to two weeks before clinical case detection. CONCLUSIONS: These findings of systematic review illustrate the analytical methods used in WBE research, as well as the future of WBE in LMICs, thereby allowing decision-makers to take strategic preventative actions to curb the spread of COVID-19 diseases in LMICs.

A report on preparation, expansion and future outlook of COVID-19 testing in Gambia.

BackgroundThe outbreak of COVID-19 disease and rapid spread of the virus outside China led to its declaration as a Public Health Emergency of International Concern (PHEIC) in January 2020. Key elements of the early intervention strategy focused on laboratory diagnosis and screening at points of entry and imposition of restrictions in crossborder activities.ObjectiveWe report the role the Medical Research Council Unit, The Gambia (MRCG) played in the early implementation of molecular testing for COVID-19 in The Gambia as part of the national outbreak response.MethodsLaboratory staff members, with experience in molecular biology assays, were identified and trained on COVID-19 testing at the Africa CDC training workshop in Dakar, Senegal. Thereafter risks assessments, drafting of standard operating procedures (SOPs) and inhouse training enabled commencement of testing using commercial RTPCR kits. Subsequently, testing was expanded to the National Public Health Laboratroy and also implemented across field sites for rapid response across the country.ResultsCapacity for COVID-19 testing at MRCG was developed and can process aproximately 350 tests per day, which can be further scaled up as the demand for testing increases.ConclusionThe long presence of the Unit in The Gambia and strong collaborative relationship with the National Health Ministry, allowed for a synergistc approach in mounting an effective response that contributed in delaying the establishment of community transmission in the country.

Factors Influencing the Results of COVID-19 Case Outreach-Results From a California Case Investigation/Contact Tracing Program.

Context:Considerable research has examined impacts of case investigation and contact tracing (CI/CT) programs on the spread of infectious diseases such as COVID-19, but there are few reports on factors affecting the ability of these programs to obtain interviews and acquire key information.Objective:To investigate programmatic and case-specific factors associated with CI outcomes using data from the Public Health Institute's Tracing Health CI/CT program. Analyses were designed to detect variability in predictors of whether interviews and key information were obtained rather than quantify specific relationships.Design:Logistic regression models examined variability in the predictive value of interview timeliness and respondent characteristics on outreach outcomes and interview results.Setting and Participants:Participants were members of a large California health care network with a positive laboratory test for COVID-19 and outreach from January 1 to July 31, 2021.Main Outcome Measures:The primary outcome was the result of outreach attempts: completed interview, refused interview, or failure to reach the infected person. Secondary outcomes considered whether respondents provided information on symptom onset, employment, and contact information or a reason for declining to provide information, and whether resource support was requested or accepted.Results:Of 9391 eligible records, 65.6% were for completed interviews, 6.0% were refusals, and 28.3% were failed outreach. One-third of respondents (36.7%) provided information on contacts (mean = 0.97 contacts per respondent, 2.6 for those naming at least 1). Privacy concerns were the most common reasons for not providing contact information. Among respondent characteristics and interview timeliness, only race and number of symptoms showed statistically significant effects in all adjusted analyses.Conclusions:Significant variation existed in outreach outcomes by subject characteristics and interview timeliness. CI/CT programs carefully focused to characteristics and needs of specific communities will likely have the greatest impact on the spread of COVID-19 and other communicable diseases.

Gambaran Pengetahuan Cuci Tangan dan Memakai Masker di Masa Pandemi pada Anak Sekolah Kelas 4-5 di SD Negeri Dolok Nagodang Tahun 2021

Knowledge of hand washing and wearing masks is an action or behavior in school children that comes from experience they have or from other sources such as parents, teachers, friends and others that can reduce the spread of Covid-19 disease in order to protect themselves or control themselves from droplets. This study aims to describe the knowledge of hand washing and wearing masks during in pandemic in grade 4-5 school children at SD Negeri Dolok Nagodang Kecamatan Uluan. This research is a quantitative research and uses a total sampling. The subjects in this study were students in grade 4-5 as many 30 people. Data collection was carried out by distributing questionnaires offline. Data analysis using univariate. The results showed that of the 30 respondents who had knowledge of washing hands and wearing masks in good category, there were 22 respondents (73,3%) and knowledge of washing hands and wearing masks with sufficient category was 8 respondents (26,7%). Knowledge of washing hands and wearing masks in school children is increashing well.

Modelling airborne transmission of SARS-CoV-2 at a local scale.

The coronavirus disease (COVID-19) pandemic has changed our lives and still poses a challenge to science. Numerous studies have contributed to a better understanding of the pandemic. In particular, inhalation of aerosolised pathogens has been identified as essential for transmission. This information is crucial to slow the spread, but the individual likelihood of becoming infected in everyday situations remains uncertain. Mathematical models help estimate such risks. In this study, we propose how to model airborne transmission of SARS-CoV-2 at a local scale. In this regard, we combine microscopic crowd simulation with a new model for disease transmission. Inspired by compartmental models, we describe virtual persons as infectious or susceptible. Infectious persons exhale pathogens bound to persistent aerosols, whereas susceptible ones absorb pathogens when moving through an aerosol cloud left by the infectious person. The transmission depends on the pathogen load of the aerosol cloud, which changes over time. We propose a ‘high risk’ benchmark scenario to distinguish critical from non-critical situations. A parameter study of a queue shows that the new model is suitable to evaluate the risk of exposure qualitatively and, thus, enables scientists or decision-makers to better assess the spread of COVID-19 and similar diseases.