Etiologies of Extreme Leukocytosis.

OBJECTIVE: The objective of this study was to determine the etiologies and co-morbidities associated with extreme leukocytosis, which is characterized by a white blood cell (WBC) count ≥ 35 × 109 leukocytes/L.  Method: Retrospective chart review was conducted for all patients, aged 18 years and older, admitted to the internal medicine department between 2015 and 2021 with an elevated WBC count ≥ 35 × 109 leukocytes/L within the first 24 hours of admission.  Results: Eighty patients were identified to have WBC count ≥ 35 × 109 leukocytes/L. The overall mortality was 16% and increased to 30% in those presenting with shock. Mortality increased from 2.8% in patients with WBC count in the range of 35-39.9 × 109 leukocytes/L to 33% in those with WBC count in the range of 40-50 × 109 leukocytes/L. There was no correlation with underlying co-morbidities or age. Pneumonia was the most common infection (38%), followed by UTI or pyelonephritis (28%) and abscesses (10%). There was no predominant organism responsible for these infections. The most common etiology for WBC count between 35-39.9 × 109 leukocytes/L and 40-50 × 109 leukocytes/L was infections, while malignancies (especially chronic lymphocytic leukemia) were more common with WBC count > 50 × 109 leukocytes/L.  Conclusion: For WBC counts in the range of 35-50 × 109 leukocytes/L, infections were the main reason for admission to the internal medicine department. Mortality increased from 2.8% to 33% as WBC counts increased from 35-39.9 × 109 leukocytes/L to 40-50 × 109 leukocytes/L. Overall, mortality for all WBC counts ≥ 35 × 109 leukocytes/L was 16%. The most common infections were pneumonia, followed by UTI or pyelonephritis and abscesses. The underlying risk factors did not correlate with WBC counts or mortality.

Critical appraisal of the Coronavirus preparedness among countries using the global health security index

Introduction: This critical study was aimed to investigate the utility of the Global Health Security Index in predicting the current COVID-19 responses. Material and Methods: Number of infected patients, deaths, incidence and the death rate per 100,000 populations related to 55 countries per week for 26 weeks were extracted. The relationship of GHSI scores and country preparedness for the pandemic was compared. Results: According to the GHSI, the incidence rate in most prepared countries was higher than the incidence rate in the more prepared countries, and which was higher than the incidence rate in the least prepared countries. However, Prevention, Detection and reporting, Rapid response, Health system, compliance with international norms and Risk environment, as well as Overall, the incidence and death rate per 100,000 people have not been like this. Conclusion: Due to mismatch between the GHSI score and fact about COVID-19 incidence, it seems necessary to investigate the factors involved in this discrepancy. © 2022, Published by Frontiers in Health Informatics.

The Geographical Correlation Between Historical Preterm Birth Disparities and COVID-19 Burden.

Similar to obstetric outcomes, rates of SARS-CoV-2 (COVID-19) infection are not homogeneously distributed among populations; risk factors accumulate in discrete locations. This study aimed to investigate the geographical correlation between pre-COVID-19 regional preterm birth (PTB) disparities and subsequent COVID-19 disease burden. We performed a retrospective, ecological cohort study of an upstate New York birth certificate database from 2004 to 2018, merged with publicly available community resource data. COVID-19 rates from 2020 were used to allocate ZIP codes to "low-," "moderate-," and "high-prevalence" groups, defined by median COVID-19 diagnosis rates. COVID-19 cohorts were associated with poverty and educational attainment data from the US Census Bureau. The dataset was analyzed for the primary outcome of PTB using ANOVA. GIS mapping visualized PTB rates and COVID-19 disease rates by ZIP code. Within 38 ZIP codes, 123,909 births were included. The median COVID-19 infection rate was 616.5 (per 100 K). PTB (all) and COVID-19 were positively correlated, with high- prevalence COVID-19 ZIP codes also being the areas with the highest prevalence of PTB (F = 11.06, P = .0002); significance was also reached for PTB < 28 weeks (F = 15.87, P < .0001) and periviable birth (F = 16.28, P < .0001). Odds of PTB < 28 weeks were significantly higher in the "high-prevalence" COVID-19 cohort compared to the "low-prevalence" COVID 19 cohort (OR 3.27 (95% CI 2.42-4.42)). COVID-19 prevalence was directly associated with number of individuals below poverty level and indirectly associated with median household income and educational attainment. GIS mapping demonstrated ZIP code clustering in the urban center with the highest rates of PTB < 28 weeks overlapping with high COVID-19 disease burden. Historical disparities in social determinants of health, exemplified by PTB outcomes, map community distribution of COVID-19 disease burden. These data should inspire socioeconomic policies supporting economic vibrancy to promote optimal health outcomes across all communities.

How Effective are Policy Interventions Against the COVID-19 Infection Rates?

Studies on the COVID-19 pandemic are more likely to concentrate on the effects of the virus while ignoring its timeseries characteristics, particularly its stationarity characteristics. Thus, this study attempts to investigate the effectiveness of policy interventions against COVID-19 by determining the permanent or transitory effects in 5 major regions and the ten most infected countries. Using the endogenous multiple breaks unit root tests introduced by Kapetanios (2005), the findings indicate that only the impacts of shocks to COVID-19 infection rates in France are likely to be permanent. However, the transitory effect is found in Brazil, Germany, Iran, Italy, Russia, Spain, Turkey, the United Kingdom, and the United States. The country where the shock has a permanent impact is suitable for policy interventions, including lockdowns, social isolation, and local isolation. While herd immunity, which protects the entire population against COVID-19, is better ideal for application in countries that experience shocks with a transitory effect.

NFNets-CNN for Classification of COVID-19 from CT Scan Images

Coronavirus disease (COVID-19) is an infectious disease caused by the coronavirus was first found in Wuhan, China in December 2019. It has infected more than 300 million people with more than 5 million of death cases. Until now, the virus is still evolving producing new variants of concern contributes to the increase the infection rate around the world. Thus, various diagnostic procedures are in need to help physicians in diagnosis disease certainly and rapidly. In this study, deep learning approach is used to classify normal and COVID-19 cases from CT scan images. Normalizer Free CNN network (NFNets) model is implemented on the images. Statistical measures such as accuracy, precision, sensitivity (also known as recall) are used to evaluate the performance of the model against the previous studies. Loss of 0.0842, accuracy of 0.7227, precision of 0.9751 and recall of 0.9727 are achieved. Thus, further optimization on the NFNets learning algorithm is required to improve the classification performanceClinical Relevance-Implementation of deep learning technique to automate diagnosis of diseases such as COVID-19 cases from CT scan images will simplify the clinical flow towards providing reliable intelligent aids for patient care. © 2022 IEEE.

Covid-19 Pandemic Predictive System Using Machine Learning

Corona -virus disease commonly known as COVID-19 that outbreak in late December 2019 is continuously spreading worldwide and infecting people due to which it's required to analysis research on the expansion of CODID-19.In this research, a more improved model. HYBRID ARTIFICAL MODEL (AI) is suggested for prediction. In conventional model, it treats similar infection rate for all people, an improvised ISI (improved susceptible-infected) is suggested to gauge the infection rate to calculate the development mode. We have build the hybrid AI model by using natural language processing(NLP) model and long short-term memory(LSTM) network modules inside ISI module.According to the attentive results, it represents more infections from three to eight days.In comparison to both the models , our developed new AI model can remarkably reduces the prediction result's error and prevail the mean percentage errors with different percentage for the six consecutive days in different countries.For example-China , Italy, France, etc. © 2022 IEEE.

Assessment of respiratory disease infection risk and natural ventilation intervention countermeasures in teaching spaces: A campus case study

The risk of indoor respiratory disease transmission can be significantly reduced through interventions that target the built environment. Several studies have successfully developed theoretical models to calculate the effects of built environment parameters on infection rates. However, current studies have mainly focused on calculating infection rate values and comparing pre- and post-optimization values, lacking a discussion of safe baseline values for infection rates with risk class classification. The purpose of this paper is to explore the design of interventions in the built environment to improve the ability of buildings to prevent virus transmission, with a university campus as an example. The study integrates the Wells-Riley model and basic reproduction number to identify teaching spaces with high infection risk on campus and proposes targeted intervention countermeasures based on the analysis of critical parameters. The results showed that teaching buildings with a grid layout pattern had a higher potential risk of infection under natural ventilation. By a diversity of building environment interventions designed, the internal airflow field of classrooms can be effectively organized, and the indoor virus concentration can be reduced. We can find that after optimizing the building mentioned above and environment intervention countermeasures, the maximum indoor virus infection probability can be reduced by 22.88%, and the basic reproduction number can be reduced by 25.98%, finally reaching a safe level of less than 1.0. In this paper, we support university campuses' respiratory disease prevention and control programs by constructing theoretical models and developing parametric platforms. © 2023 Elsevier Ltd

Risk Perception and Misinformation in Brazilian Twitter during COVID-19 Infodemic

During the COVID-19 pandemic, different groups had different perceptions of how dangerous the coronavirus was. This difference in behavior was intensified by the large amount of misinformation shared across social media. This work presents an analysis aimed at understanding the extent to which people perceived risk at different levels, and at uncovering the relationship between these differences and the spread of misinformation. In particular, we focus on Brazil, because it is well-known that its Ministry of Health has sponsored campaigns that raised suspicious regarding the effectiveness of the vaccines. To achieve this goal, we gathered tweets written in Portuguese related to the COVID-19 and analyzed their psycholinguistic traits. Among those traits, we found 'Anxiety' to be a good proxy for risk perception. We validate this choice by showing that, at moments of high (resp. low) infection rates in the world, the Anxiety score was higher (resp. lower). We grouped users into 'low' and 'high' risk perception based on the users' anxiety score, and analyzed the relation of each group with the spread of misinformation. Our results show that Twitter users with a lower perceived risk were more inclined to share fake news and harmful information, while the group with a higher level of anxiety tends to share more scientifically-backed information. This is an important step towards helping minimize the spread of false and harmful health information around the internet. © 2022 IEEE.

Adaptive Sentinel Testing in Workplace for COVID-19 Pandemic.

Testing and isolation of infectious employees is one of the critical strategies to make the workplace safe during the pandemic for many organizations. Adaptive testing frequency reduces cost while keeping the pandemic under control at the workplace. However, most models aimed at estimating test frequencies were structured for municipalities or large organizations such as university campuses of highly mobile individuals. By contrast, the workplace exhibits distinct characteristics: employee positivity rate may be different from the local community because of rigorous protective measures at workplace, or self-selection of co-workers with common behavioral tendencies for adherence to pandemic mitigation guidelines. Moreover, dual exposure to COVID-19 occurs at work and home that complicates transmission modeling, as does transmission tracing at the workplace. Hence, we developed bi-modal SEIR (Susceptible, Exposed, Infectious, and Removed) model and R-shiny tool that accounts for these differentiating factors to adaptively estimate the testing frequency for workplace. Our tool uses easily measurable parameters: community incidence rate, risks of acquiring infection from community and workplace, workforce size, and sensitivity of testing. Our model is best suited for moderate-sized organizations with low internal transmission rates, no-outward facing employees whose position demands frequent in-person interactions with the public, and low to medium population positivity rates. Simulations revealed that employee behavior in adherence to protective measures at work and in their community, and the onsite workforce size have large effects on testing frequency. Reducing workplace transmission rate through workplace mitigation protocols and higher sensitivity of the test deployed, although to a lesser extent. Furthermore, our simulations showed that sentinel testing leads to only marginal increase in the number of infections even for high community incidence rates, suggesting that this may be a cost-effective approach in future pandemics. We used our model to accurately guide testing regimen for three campuses of the Jackson Laboratory.

Forecasting of COVID-19 Outbreak in India: A Time Series Analysis

The World Health Organization (WHO) declared the status of coronavirus disease 2019 (COVID-19) to a global pandemic on March 11, 2020. Since then, numerous statistical, epidemiological and mathematical models have been used and investigated by researchers across the world to predict the spread of this pandemic in different geographical locations. The data for COVID-19 outbreak in India has been collated on daily new confirmed cases from March 12, 2020 to April 10, 2021. A time series analysis using Auto Regressive Integrated Moving Average (ARIMA) model was used to investigate the dataset and then forecast for the next 30-day time-period from April 11, 2021, to May 10, 2021. The selected model predicts a surge in the number of daily new cases and number of deaths. An investigation into the daily infection rate for India has also been done. © 2023 The authors and IOS Press.

Current state-of-The-Art review of nanotechnology-based therapeutics for viral pandemics: Special attention to COVID-19

Over the past two centuries, most pandemics have been caused by zoonotic RNA viruses with high mutation, infection, and transmission rates. Due to the importance of understanding the viruses' role in establishing the latest outbreak pandemics, we briefly discuss their etiology, symptomatology, and epidemiology and then pay close attention to the latest chronic communicable disease, SARS-CoV-2. To date, there are no generally proven effective techniques in the diagnosis, treatment, and spread strategy of viral diseases, so there is a profound need to discover efficient technologies to address these issues. Nanotechnology can be a promising approach for designing more functional and potent therapeutics against coronavirus disease 2019 (COVID-19) and other viral diseases. Moreover, this review intends to summarize examples of nanostructures that play a role in preventing, diagnosing, and treating COVID-19 and be a comprehensive and helpful review by covering notable and vital applications of nanotechnology-based strategies for improving health and environmental sanitation. © 2023 the author(s), published by De Gruyter.

Research on Anti-epidemic Material Delivery Robot

After the occurrence of the COVID-19, preventing cross infection has become a top priority. Therefore, it is proposed to use robots to replace people to distribute anti epidemic materials, so as to reduce human contact. By planning the trajectory of the robot in advance, and using mechanical arms and claws to achieve accurate grasp and delivery of anti epidemic materials, it can carry out material distribution in the isolated inpatient department, and can independently locate and deliver products, goods, etc. in a complex environment. It has strong cargo carrying capacity, and has the dual functions of traditional delivery robots and indoor delivery services. Its use can greatly reduce the infection rate in the epidemic and deliver materials in time. © 2022 IEEE.

Human Mobility Driven Modeling of an Infectious Disease

In conventional disease models, disease properties are dominant parameters (e.g., infection rate, incubation pe-riod). As seen in the recent literature on infectious diseases, human behavior - particularly mobility - plays a crucial role in spreading diseases. This paper proposes an epidemiological model named SEIRD+m that considers human mobility instead of modeling disease properties alone. SEIRD+m relies on the core deterministic epidemic model SEIR (Susceptible, Exposed, Infected, and Recovered), adds a new compartment D - Dead, and enhances each SEIRD component by human mobility information (such as time, location, and movements) retrieved from cell-phone data collected by SafeGraph. We demonstrate a way to reduce the number of infections and deaths due to COVID-19 by restricting mobility on specific Census Block Groups (CBGs) detected as COVID-19 hotspots. A case study in this paper depicts that a reduction of mobility by 50 % could help reduce the number of infections and deaths in significant percentages in different population groups based on race, income, and age. © 2022 IEEE.

Stability analysis of a non-singular fractional-order covid-19 model with nonlinear incidence and treatment rate

In this paper, a non-singular SIR model with the Mittag-Leffler law is proposed. The nonlinear Beddington-DeAngelis infection rate and Holling type II treatment rate are used. The qualitative properties of the SIR model are discussed in detail. The local and global stability of the model are analyzed. Moreover, some conditions are developed to guarantee local and global asymptotic stability. Finally, numerical simulations are provided to support the theoretical results and used to analyze the impact of face masks, social distancing, quarantine, lockdown, immigration, treatment rate of the disease, and limitation in treatment resources on COVID-19. The graphical results show that face masks, social distancing, quarantine, lockdown, immigration, and effective treatment rates significantly reduce the infected population over time. In contrast, limitation in the availability of treatment raises the infected population. © 2023 The Author(s). Published by IOP Publishing Ltd.

COVID-19 Risk Prediction and Contact Tracing

The emergence of the coronavirus COVID- 19 switched the limelight onto digital health technologies. To help the infection rates from surging, numerous governments are looking into applications that could help disrupt infection chains beforehand. We created a Self-Assessment Test using COVID Symptoms, that's capable of assessing the threat of COVID- 19 in the user using ML. The data also tracks the user and gives safety tips and recommendations. Using the Track Module, the user is notified of the nearby containment zones. The contact tracing module helps the user to maintain a specified distance from others. © 2022 IEEE.

A Retrospective Database Analysis of Before and After Social Distancing in Relation to Pediatric Infection Rate and Healthcare Services Usage During the COVID-19 Pandemic.

BACKGROUND: Social distancing policy was introduced in Israel in 2020 to reduce the spread of COVID-19. The aim of this study was to analyze the effect of social distancing on other infections in children, by comparing disease rate and healthcare utilization before and after social distancing. METHODS: This was a before-and-after study. Within this retrospective database analysis of parallel periods in 2019 (Period 1 and 2) and 2020 (period 3 - pre-lockdown period, and Period 4 - lockdown period) we included all pediatric population registered in the electronic medical records of the Maccabi Healthcare Services, Israel, looking at the occurrence of non-COVID infections, antibiotic purchasing, doctor visits, Ambulatory Emergency Care Centers visits, Emergency Departments' visits, and hospitalizations. RESULTS: 776,828 and 777,729 children from 2019 and 2020, respectively, were included. We found a lower infection rate in 2020 vs 2019. We did not find a difference in infection rate between Periods 1-2, while a significant difference was found between Periods 3- 4. We found a significant difference between Periods 2-4, with a higher RR than in Periods 1-3. A modest decrease in Ambulatory Emergency Care Center visits, and lower increase in emergency department visits and hospital admissions was found in 2020. We found decreases in antibiotic purchasing between Periods 1-3 and Periods 2-4, more pronounced in 2020 than in 2019. CONCLUSIONS AND RELEVANCE: Analysis of before and after social distancing and masking showed reduced prevalence of non-COVID pediatric infections, consumption of health care services, and antibiotics consumption.

COVID-19 Detection Using CNN-ResNet-50 Model

COVID-19 is a deadly virus that originated in 2019 and could be easily transmitted from one geographical area to another. It affected the integral world, resulting in severe mortality due to its contagious effect on human life. The infection rate is continuously growing and it is becoming unmanageable since the virus moves easily from one human to another. Once we detect the COVID-19 virus in its early stages, we can easily reduce the death rate. The most common and widely used method of diagnosing COVID is through reverse transcription polymerase chain (RT-PCR). But the RT-PCR test is time consuming, inaccurate, and expensive. In this situation, the time period for the detection of viruses is valuable. Keeping these limitations in mind, we use an X-ray image of the chest to identify the COVID-19 infected patient. This procedure is achieved by using convolution neural network (CNN) in deep learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l'Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of three-clinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-term-memory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset. © 2023 Tech Science Press. All rights reserved.

Effectiveness and policies analysis of pool testing method for COVID-19

Purpose: This research aims to figure out whether the pool testing method of SARS-CoV-2 for COVID-19 is effective and the optimal sample size is in one bunch. Additionally, since the infection rate was unknown at the beginning, this research aims to propose a multiple sampling approach that enables the pool testing method to be utilized successfully. Design/methodology/approach: The authors verify that the pool testing method of SARS-CoV-2 for COVID-19 is effective under the situation of the shortage of nucleic acid detection kits based on probabilistic modeling. In this method, the testing is performed on several samples of the cases together as a bunch. If the test result of the bunch is negative, then it is shown that none of the cases in the bunch has been infected with the novel coronavirus. On the contrary, if the test result of the bunch is positive, then the samples are tested one by one to confirm which cases are infected. Findings: If the infection rate is extremely low, while the same number of detection kits is used, the expected number of cases that can be tested by the pool testing method is far more than that by the one-by-one testing method. The pool testing method is effective only when the infection rate is less than 0.3078. The higher the infection rate, the smaller the optimal sample size in one bunch. If N samples are tested by the pool testing method, while the sample size in one bunch is G, the number of detection kits required is in the interval (N/G, N). Originality/value: This research proves that the pool testing method is not only suitable for the situation of the shortage of detection kits but also the situation of the overall or sampling detection for a large population. More importantly, it calculates the optimal sample size in one bunch corresponding to different infection rates. Additionally, a multiple sampling approach is proposed. In this approach, the whole testing process is divided into several rounds in which the sample sizes in one bunch are different. The actual infection rate is estimated gradually precisely by sampling inspection in each round. © 2021, Emerald Publishing Limited.

Seating System in Indian Public Transport

Public transportation is a crucial part of our everyday lives, also, moving from one place to another is fraught with problems. The number of people using public transportation has increased, increasing transportation demand among the general population. Because of the COVID-19 outbreak, it is difficult for people to travel around the city without fear of contracting the disease;in this situation, people always wish for a better hygiene system and a low infection rate in the city. As a result, the primary goal of the study is to research and develop a viewpoint on the public transportation seating system, that is relevant to the Indian setting and attitude of following rules and regulations. Many things have been tried in the past to ensure the safety of travelers, and they have not proven as beneficial as they should be after following COVID-19 recommendations. The goal is to enhance the comfort and safety of passengers as well as give people easy access to the public transportation that is most widely used, like auto-rickshaws and buses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.