Coherent forecasting of NoGeAR(1) model (preprint)

This article focuses on the coherent forecasting of the recently introduced novel geometric AR(1) (NoGeAR(1)) model - an INAR model based on inflated - parameter binomial thinning approach. Various techniques are available to achieve h - step ahead coherent forecasts of count time series, like median and mode forecasting. However, there needs to be more body of literature addressing coherent forecasting in the context of overdispersed count time series. Here, we study the forecasting distribution corresponding to NoGeAR(1) process using the Monte Carlo (MC) approximation method. Accordingly, several forecasting measures are employed in the simulation study to facilitate a thorough comparison of the forecasting capability of NoGeAR(1) with other models. The methodology is also demonstrated using real-life data, specifically the data on CW{\ss} TeXpert downloads and Barbados COVID-19 data.

Non-Consensual Sex among Japanese Women in the COVID-19 Pandemic: A Large-Scale Nationwide Survey-Based Study (preprint)

Background: Non-consensual sex including rape and sexual assault has been a global concern and may have been influenced by the COVID-19 pandemic, however the information on this topic is limited. Therefore, our objective was to survey the incidence rate of non-consensual sex among Japanese women aged 15-79 years between April to September 2020, following the COVID-19 pandemic in Japan.   Materials and Methods: We utilized the data obtained from a nationwide, cross-sectional internet survey conducted in Japan between August and September 2020. Sampling weights were applied to calculate national estimates, and multivariable logistic regression was performed to identify factors associated with non-consensual sex. Data was extracted from a cross-sectional, web-based, self-administered survey of approximately 2.2 million individuals from the general public, including in men and women.   Results: Excluding men and responses with inconsistencies, the final analysis included 12,809 women participants, with 138 (1.1%) reporting experiencing non-consensual sex within a five-month period. Being aged 15–29 years and having a worsened mental or economic status were associated with experiencing non-consensual sex.   Conclusions: Early intervention to prevent individuals from becoming victims of sexual harm should be extended to economically vulnerable and young women, especially during times of societal upheaval such as the COVID-19 pandemic. Additionally, Japan should prioritize the implementation of comprehensive education on the concept of sexual consent.

COVID-19 and the Interplay with Antibacterial Drug Resistance (preprint)

Antimicrobial resistance (AMR) is a growing public health issue worldwide that affects the world economy. The concern associated with this issue increased during the COVID-19 pandemic. The misuse of antibiotics during the COVID-19 pandemic will have catastrophic implications for the control of AMR. In 2019, the coronavirus illness COVID-19 first appeared in China. COVID-19 infection is a surface-to-surface communicable disease. It is considered the most vital global health disaster of the century. It has rapidly spread around the world and is the greatest challenge for humankind. This chapter discusses the origin, symptoms, transmission, treatment, and recommendations of the COVID-19 disease.

Intracardiac Thrombus in COVID-19 Inpatients: A Nationwide Study of Incidence, Predictors and Outcomes (preprint)

Background: COronaVIrus Disease 2019 (COVID-19) has been observed to be associated with a hypercoagulable state. Intracardiac thrombosis is a serious complication but has seldom been evaluated in COVID-19 patients. We assessed the incidence, associated factors, and outcomes of COVID-19 patients with intracardiac thrombosis. Methods: COVID-19 inpatients during 2020 were retrospectively identified from the national inpatient sample (NIS) database, and data retrieved regarding clinical characteristics, intracardiac thrombosis, and adverse outcomes. Multivariable logistic regression was performed to identify the clinical factors associated with intracardiac thrombosis and in-hospital mortality and morbidities. Results: A total of 1,683,785 COVID-19 inpatients were identified in 2020 from NIS, with a mean age of 63.8 {+/-} 1.6 years, and 32.2% females. Intracardiac thrombosis was present in 0.001% (1,830) patients. Overall, in-hospital outcomes include all-cause mortality 13.2% (222,695/1,683,785), cardiovascular mortality 3.5%, cardiac arrest 2.6%, acute coronary syndrome (ACS) 4.4%, heart failure 16.1%, stroke 1.3% and acute kidney injury (AKI) 28.3%. The main factors associated with intracardiac thrombosis were a history of congestive heart failure and coagulopathy. Intracardiac thrombosis was independently associated with a higher risk of in-hospital all-cause mortality (OR: 3.32, 95% CI: 2.42-4.54, p<0.001), cardiovascular mortality (OR: 2.95, 95% CI: 1.96-4.44, p<0.001), cardiac arrest (OR: 2.04, 95% CI: 1.22-3.43, p=0.006), ACS (OR: 1.62, 95% CI: 1.17-2.22, p=0.003), stroke (OR: 3.10, 95% CI: 2.11-4.56, p<0.001), and AKI (OR: 2.13 95% CI: 1.68-2.69, p<0.001), but not incident heart failure (p=0.27). Conclusion: Although intracardiac thrombosis is rare in COVID-19 inpatients, its presence was independently associated with higher risks of in-hospital mortality and most morbidities. Prompt investigations and treatments for intracardiac thrombosis are warranted when there is a high index of suspicion and a confirmed diagnosis respectively.

Face Mask And Body Temperature Detection In Sensor Enabled Automated Door Control System

Developing an automatic door-opening system that can recognize masks and gauge body temperature is the aim of this project. The new Corona Virus (COVID-19) is an unimaginable pandemic that presents the medical industry with a serious worldwide issue in the twenty-first century. How individuals conduct their lives has substantially changed as a result. Individuals are reluctant to seek out even the most basic healthcare services because of the rising number of sick people who pass away, instilling an unshakable terror in their thoughts.This paper is about the Automatic Health Machine (AHM). In this dire situation, the government provided the people with a lot of directions and information. Apart from the government, everyone is accountable for his or her own health. The most common symptom of corona infection is an uncontrollable rise in body temperature. In this project, we create a novel device to monitor people's body temperatures using components such as an IR sensor and temperature sensor. © 2023 IEEE.

COVID-19: Prediction, Prevalence, and the Operations of Vaccine Allocation

Problem definition: Mitigating the COVID-19 pandemic poses a series of unprecedented challenges, including predicting new cases and deaths, understanding true prevalence beyond what tests are able to detect, and allocating different vaccines across various regions. In this paper, we describe our efforts to tackle these issues and explore the impact on combating the pandemic in terms of case and death prediction, true prevalence, and fair vaccine distribution. Methodology/results: We present the methods we developed for predicting cases and deaths using a novel machine-learning-based aggregation method to create a single prediction that we call MIT-Cassandra. We further incorporate COVID-19 case prediction to determine true prevalence and incorporate this prevalence into an optimization model for efficiently and fairly managing the operations of vaccine allocation. We study the trade-offs of vaccine allocation between different regions and age groups, as well as first- and second-dose distribution of different vaccines. This also allows us to provide insights into how prevalence and exposure of the disease in different parts of the population can affect the distribution of different vaccine doses in a fair way. Managerial implications: MIT-Cassandra is currently being used by the Centers for Disease Control and Prevention and is consistently among the best-performing methods in terms of accuracy, often ranking at the top. In addition, our work has been helping decision makers by predicting how cases and true prevalence of COVID-19 will progress over the next few months in different regions and utilizing the knowledge for vaccine distribution under various operational constraints. Finally, and very importantly, our work has specifically been used as part of a collaboration with the Massachusetts Institute of Technology's (MIT's) Quest for Intelligence and as part of MIT's process to reopen the institute.

The journey of a lifetime - development of Pfizer's COVID-19 vaccine.

It would be apt to say that one of the greatest accomplishments in modern medicine has been the development of vaccines against COVID-19, which had paralyzed the entire world for more than a year. Pfizer and BioNTech codeveloped the first COVID-19 vaccine that was granted emergency-use authorization or conditional approval in several regions globally. This article is an attempt to go 'behind-the-scenes' of this development process and highlight key factors that allowed us to move with this unprecedented speed, while adhering to normal vaccine-development requirements to generate the information the regulatory authorities needed to assess the safety and effectiveness of a vaccine to prevent an infectious disease, including quality and manufacturing standards. This is also a story of how Pfizer and BioNTech leveraged our combined skill sets and experience to respond to the global health crisis to progress this program swiftly while ensuring the compliance with our high-quality standards and keeping patient safety at the forefront. We will also highlight multiple other factors that were instrumental in our success.

A tale of two waves: Delineating diverse genomic and transmission landscapes driving the COVID-19 pandemic in Pune, India.

BACKGROUND: Modern response to pandemics, critical for effective public health measures, is shaped by the availability and integration of diverse epidemiological outbreak data. Tracking variants of concern (VOC) is integral to understanding the evolution of SARS-CoV-2 in space and time, both at the local level and global context. This potentially generates actionable information when integrated with epidemiological outbreak data. METHODS: A city-wide network of researchers, clinicians, and pathology diagnostic laboratories was formed for genome surveillance of COVID-19 in Pune, India. The genomic landscapes of 10,496 sequenced samples of SARS-CoV-2 driving peaks of infection in Pune between December-2020 to March-2022, were determined. As a modern response to the pandemic, a "band of five" outbreak data analytics approach was used. This integrated the genomic data (Band 1) of the virus through molecular phylogenetics with key outbreak data including sample collection dates and case numbers (Band 2), demographics like age and gender (Band 3-4), and geospatial mapping (Band 5). RESULTS: The transmission dynamics of VOCs in 10,496 sequenced samples identified B.1.617.2 (Delta) and BA(x) (Omicron formerly known as B.1.1.529) variants as drivers of the second and third peaks of infection in Pune. Spike Protein mutational profiling during pre and post-Omicron VOCs indicated differential rank ordering of high-frequency mutations in specific domains that increased the charge and binding properties of the protein. Time-resolved phylogenetic analysis of Omicron sub-lineages identified a highly divergent BA.1 from Pune in addition to recombinant X lineages, XZ, XQ, and XM. CONCLUSIONS: The band of five outbreak data analytics approach, which integrates five different types of data, highlights the importance of a strong surveillance system with high-quality meta-data for understanding the spatiotemporal evolution of the SARS-CoV-2 genome in Pune. These findings have important implications for pandemic preparedness and could be critical tools for understanding and responding to future outbreaks.

Mucormycosis: An Epidemic Complicating the COVID-19 Pandemic.

COVID-19 is a relatively new disease whose complete pathogenesis and complications have not been elucidated. Apart from the morbidity and mortality caused by the virus itself, it is noted that patients affected with this virus have a higher susceptibility to bacterial and fungal co-infections. Mucormycosis is a rare and life-threatening fungal infection generally associated with uncontrolled diabetes mellitus and immunosuppression. It tends to rapid disease progression and poor prognosis if not diagnosed and managed promptly. There has been a sudden increase in the number of mucormycosis cases in patients with moderate to severe COVID-19 infection in the past few months. Herein, we present a series of 10 mucormycosis cases diagnosed over one week.

Neurophenotypes of COVID-19: Risk factors and recovery outcomes.

Coronavirus disease 2019 (COVID-19) infection is associated with risk of persistent neurocognitive and neuropsychiatric complications. It is unclear whether the neuropsychological manifestations of COVID-19 present as a uniform syndrome or as distinct neurophenotypes with differing risk factors and recovery outcomes. We examined post-acute neuropsychological profiles following SARS-CoV-2 infection in 205 patients recruited from inpatient and outpatient populations, using an unsupervised machine learning cluster analysis, with objective and subjective measures as input features. This resulted in three distinct post-COVID clusters. In the largest cluster (69%), cognitive functions were within normal limits, although mild subjective attention and memory complaints were reported. Vaccination was associated with membership in this "normal cognition" phenotype. Cognitive impairment was present in the remaining 31% of the sample but clustered into two differentially impaired groups. In 16% of participants, memory deficits, slowed processing speed, and fatigue were predominant. Risk factors for membership in the "memory-speed impaired" neurophenotype included anosmia and more severe COVID-19 infection. In the remaining 15% of participants, executive dysfunction was predominant. Risk factors for membership in this milder "dysexecutive" neurophenotype included disease-nonspecific factors such as neighborhood deprivation and obesity. Recovery outcomes at 6-month follow-up differed across neurophenotypes, with the normal cognition group showing improvement in verbal memory and psychomotor speed, the dysexecutive group showing improvement in cognitive flexibility, and the memory-speed impaired group showing no objective improvement and relatively worse functional outcomes compared to the other two clusters. These results indicate that there are multiple post-acute neurophenotypes of COVID-19, with different etiological pathways and recovery outcomes. This information may inform phenotype-specific approaches to treatment.

Exploring pharmacy student experiences with student debt and perspectives on future burnout and loan relief

Objective Pharmacy students with substantial educational debt are at risk for excessive workloads, burnout, and clinical errors. During the COVID-19 pandemic, policies addressing economic hardships for all student debt borrowers included temporary suspension of monthly payments and 0% interest during the pause. This study aimed to understand student-level factors regarding student debt from the lived experiences of current pharmacy students and aimed to understand how current pharmacy students view temporary loan relief. Methods We used semi-structured interviews of pharmacy students across four years of progression in their pharmacy program to better understand student experiences with debt, different factors that may influence the impact of student debt on short-term and long-term outcomes for students, and student perspectives on debt relief policies and potential solutions. Our thematic analysis was grounded in existing evidence and a conceptual framework, while also allowing codes to emerge directly from the data. Results A total of 20 pharmacy students were interviewed with a median student debt of $77,000, with debt amounts ranging from $0 to $209,000. Students described what mediating factors influenced their experiences, the influence of student debt on clinician burnout, and other outcomes impacted by student debt. Six overarching themes emerged relevant to current students: student debt influences education and career decisions, debt is risky given the saturated pharmacy market, debt is an accepted burden, debt will inhibit starting a life, the COVID-19 loan relief is revealing, and early financial education is needed. Conclusion Pharmacy students burdened with debt described a variety of different experiences and attitudes towards that debt and provided their perspectives on how student debt influences short-term education and career decisions. While students accept the tradeoff of debt for their education as an inevitable burden, reported coping mechanisms and strategies shared suggest some solutions may be available to ameliorate this burden.

Impact of Covid-19 lockdown on sleep quality, physical activity, and body weight among university students: A retrospective cross-sectional study.

BACKGROUND: Covid-19 lockdown had caused lifestyle changes especially in sleep, physical activity, and body weight. Thus, this study aimed to determine the weight changes before and after the lockdown period and further assessed the association between sleep quality, physical activity, and body mass index (BMI). MATERIAL AND METHODS: This was a retrospective cross-sectional study involving 107 undergraduate students in Universiti Sains Malaysia. Subjects recalled information during the first lockdown implemented in Malaysia from early March 2020 to July 2020. The questionnaire consisted of socio-demography, anthropometry, and physical activity using International Physical Activity Questionnaire and sleep quality using Pittsburgh Sleep Quality Index. Chi-square analysis was used to determine the association between the variables using Statistical Package for Social Sciences software version 26.0. RESULTS: There was a significant increase of 1.8 kg in weight before and after the lockdown period. The majority of respondents had poor sleep quality (80.4%) and low physical activity (60.2%), respectively. Almost 29% of the subjects had sleep latency of more than 30 min while 69.1% of them had sleep duration of <7 h. There was no significant association between sleep quality and BMI as well as physical activity and BMI. CONCLUSION: Our study demonstrated that the prevalence of poor sleep quality and low physical activity among university students was high during Covid-19 confinement. Moreover, youths have a significant increase in body weight during the lockdown period. Thus, university students may adopt exciting leisure activities to keep themselves active such as doing meditation or joining online exercise classes.

Unveiling Spatial Associations Between COVID-19 Severe Health Index, Racial/Ethnic Composition, And Community Factors (preprint)

Limited efforts have been made to incorporate various predisposing factors, including racial/ethnic composition, into prediction models exploring the spatial distribution of COVID-19 Severe Health Risk Index (SHRI). This study examines county-level data from 3,107 US counties, utilizing publicly available datasets. Spatial and non-spatial regression models were constructed, adjusting for rurality, socio-demographic factors, physical health, smoking, sleep, health insurance, healthcare providers, hospitalizations, and environmental risks. Findings reveal spatial models effectively explain geospatial disparities of COVID-19 SHRI. White, Hispanic, and other racial/ethnic majority counties exhibit lower burdens compared to majority Black counties. Older population, lower income, smoking, insufficient sleep, and preventable hospitalizations are associated with higher burdens. Counties with better health access and internet coverage experience lower burdens. This study provides insights into at-risk populations, guiding resource allocation. Racial/ethnic inequalities play a significant role in driving disparities. Addressing these factors reduces health outcome disparities. This work establishes a baseline typology for exploring social, health, economic, and political factors contributing to different health outcomes.

Correction: Suzuki et al. Cross-Country Student Perceptions about Online Medical Education during the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2022, 19, 2840.

In the published publication, there was an error regarding the affiliation for "Tomoya Suzuki" [...].

Longitudinal host transcriptional responses to SARS-CoV-2 infection in adults with extremely high viral load (preprint)

Current understanding of viral dynamics of SARS-CoV-2 and host responses driving the pathogenic mechanisms in COVID-19 is rapidly evolving. Here, we conducted a longitudinal study to investigate gene expression patterns during acute SARS-CoV-2 illness. Cases included SARS-CoV-2 infected individuals with extremely high viral loads early in their illness, individuals having low SARS-CoV-2 viral loads early in their infection, and individuals testing negative for SARS-CoV-2. We could identify widespread transcriptional host responses to SARS-CoV-2 infection that were initially most strongly manifested in patients with extremely high initial viral loads, then attenuating within the patient over time as viral loads decreased. Genes correlated with SARS-CoV-2 viral load over time were similarly differentially expressed across independent datasets of SARS-CoV-2 infected lung and upper airway cells, from both in vitro systems and patient samples. We also generated expression data on the human nose organoid model during SARS-CoV-2 infection. The human nose organoid-generated host transcriptional response captured many aspects of responses observed in the above patient samples while suggesting the existence of distinct host responses to SARS-CoV-2 depending on the cellular context, involving both epithelial and cellular immune responses. Our findings provide a catalog of SARS-CoV-2 host response genes changing over time.

Longitudinal host transcriptional responses to SARS-CoV-2 infection in adults with extremely high viral load (preprint)

Current understanding of viral dynamics of SARS-CoV-2 and host responses driving the pathogenic mechanisms in COVID-19 is rapidly evolving. Here, we conducted a longitudinal study to investigate gene expression patterns during acute SARS-CoV-2 illness. Cases included SARS-CoV-2 infected individuals with extremely high viral loads early in their illness, individuals having low SARS-CoV-2 viral loads early in their infection, and individuals testing negative for SARS-CoV-2. We could identify widespread transcriptional host responses to SARS-CoV-2 infection that were initially most strongly manifested in patients with extremely high initial viral loads, then attenuating within the patient over time as viral loads decreased. Genes correlated with SARS-CoV-2 viral load over time were similarly differentially expressed across independent datasets of SARS-CoV-2 infected lung and upper airway cells, from both in vitro systems and patient samples. We also generated expression data on the human nose organoid model during SARS-CoV-2 infection. The human nose organoid-generated host transcriptional response captured many aspects of responses observed in the above patient samples, while suggesting the existence of distinct host responses to SARS-CoV-2 depending on the cellular context, involving both epithelial and cellular immune responses. Our findings provide a catalog of SARS-CoV-2 host response genes changing over time.

COVID‐19: A multiwave SIR‐based model for learning waves

One of the greatest challenges of the COVID‐19 pandemic has been the way evolving regulation, information, and sentiment have driven waves of the disease. Traditional epidemiology models, such as the SIR model, are not equipped to handle these behavioral‐based changes. We propose a novel multiwave susceptible–infected–recovered (SIR) model, which can detect and model the waves of the disease. We bring together the SIR model's compartmental structure with a change‐point detection martingale process to identify new waves. We create a dynamic process where new waves can be flagged and learned in real time. We use this approach to extend the traditional susceptible–exposed–infected–recovered–dead (SEIRD) model into a multiwave SEIRD model and test it on forecasting COVID‐19 cases from the John Hopkins University data set for states in the United States. We find that compared to the traditional SEIRD model, the multiwave SEIRD model improves mean absolute percentage error (MAPE) by 15%–25% for the United States. We benchmark the multiwave SEIRD model against top performing Center for Disease Control (CDC) models for COVID‐19 and find that the multiwave SERID model is able to outperform the majority of CDC models in long‐term predictions.

Patient Utilization of Premium Intraocular Lenses Before and During the COVID-19 Pandemic.

PURPOSE: To compare the choice of intraocular lens (IOL) and sociodemographic characteristics between patients who underwent elective cataract surgery before the COVID-19 pandemic and during the pandemic at the Wilmer Eye Institute. METHODS: A retrospective chart review of patients who underwent cataract surgery before the COVID-19 pandemic (June 1 to November 30, 2019) and during the pandemic (June 1 to November 30, 2020) was conducted. Sociodemographic information, including age, sex, race, and insurance, and choice of IOL (premium or standard) were analyzed. The association between timing of surgery and choice of IOL was analyzed using multivariable logistic regression. RESULTS: The study included 2,877 patients (3,946 eyes) before COVID-19 and 2,564 patients (3,605 eyes) during COVID-19. However, 9.0% (357/3,946) of surgeries before COVID-19 used premium IOLs compared with 11.1% (399/3,605) during COVID-19 ( P =0.004). There was no difference in the racial characteristics of patients between before and during COVID-19. After adjusting for time of surgery and demographics, the odds of choosing premium IOLs for black patients was 0.32 times the odds for white patients ( P <0.001). There was an increase in private-insured patients but a decrease in Medicare-insured patients during COVID-19. After adjusting for time of surgery and demographics, private-insured patients had higher odds of choosing premium IOLs ( P <0.001), whereas Medicaid-insured patients had lower odds ( P =0.007) when compared with Medicare-insured patients. CONCLUSION: More patients chose premium IOLs during COVID-19 than before COVID-19, concurrent with change in insurance status. White patients were more likely to choose premium IOLs than black patients, as were private-insured patients compared with Medicare-insured patients.

Respiratory disorders and their association with clinical outcomes in COVID-19: a narrative review of current literature.

BACKGROUND AND OBJECTIVE: The coronavirus disease 2019, also known as COVID-19, has caused significant worldwide morbidity and mortality. Given the direct effect of severe acute respiratory syndrome virus-2 (SARS-CoV-2) on the respiratory system, it is important that clinicians who manage chronic respiratory conditions are familiar with the pathophysiology and impact of COVID-19 on pre-existing respiratory disease. METHODS: Literature review relating to COVID-19 and respiratory disorders from PubMed and Google Scholar was conducted, with aim to encompass all publications relating to the most commonly encountered respiratory diseases in clinical practice, namely chronic obstructive lung disease (COPD), asthma, interstitial lung disease (ILD), obstructive sleep apnea (OSA), as well as obesity given it's known effect on both gas exchange and mechanistic aspects of respiration. The publications were analyzed for relevance to clinical implications and pathophysiologic mechanisms. Additional manual literature review was conducted based on citations from large review articles and society guidelines/statement papers. KEY CONTENT AND FINDINGS: Certain respiratory disorders such as COPD, ILD, OSA, and obesity carry higher burden of morbidity and mortality associated with COVID-19. Surprisingly, and in contrast to previously studied viral epidemics, asthma does not carry increased associated risk of contracting the virus or worse clinical outcomes. CONCLUSIONS: A thorough understanding of the mechanisms responsible for control of breathing and the effect of COVID-19 on pulmonary pathophysiology will allow clinicians who manage chronic respiratory disease to effectively predict associated clinical outcomes as well as improve management strategies.