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This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems;(ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis;(iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC;(iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences. © The Author(s) 2022.
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Background: COVID-19 pandemic originated in the city of Wuhan in Hubie province of China and within three months of its origin the disease extended to nearly 221 countries in the world. Objective(s): The objective is Study of hematological and various biochemical Markers in COVID-19 patients admitted in a tertiary care centre . Methodology: In this single-center study, records of 170 patients hospitalized with COVID-19 were studied for hematological profile and biochemical markers. Records of patients with laboratory-confirmed COVID-19 disease hospitalized between April 2020, to August 2020, were included in the analysis. Result(s): A total of 170 patients were enrolled of Age Group 20-80 year of which 80% (136/170) were asymptomatic and 20% (34/170) symptomatic. 17% patients had co-existing illnesses. Clinical spectrum among COVID-19 patients varied from being asymptomatic to having symptoms like fever, dry cough, breathlessness with few progressing to respiratory failure and multi-organ failure. In our study, 96.0% (163/170) recovered while 4.0% (7/170) died. Mean age, total leucocyte count (TLC), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and lactate dehydrogenase (LDH),Procalcitonin, CRP, D dimer of severely ill patients were significantly higher than those of patients with non-severe illness. Conclusion(s): The clinicians may consider the hematological and biochemical parameters in the patients with COVID-19 in future decision-making. Elevated NLR, TLC, LDH, C-Reactive Protein, Procalcitonin, D dimer and lymphopenia were seen in the symptomatic patients especially manifesting severe disease. Early intervention and periodic monitoring of these parameters in patients, especially with severe disease may help in improving disease outcome. Copyright © 2022 Ubiquity Press. All rights reserved.
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Introduction: The COVID-19 pandemic catalyzed growth of virtual medicine, challenging providers to adapt their standard protocols for telehealth. During this time, cardiologists were unable to gather numerical/graphical heart data to guide therapy. Many examination technologies exist in-person to obtain this data, but they are not engineered for patient use during virtual visits. Objective: The team sought to develop an inexpensive diagnostic point-of-care device designed to work with telehealth applications and provide patients with the ability to transmit data on cardiovascular function to physicians in real time. Methods: The AusculBand used clinician and patient input for final design format. The AusculBand is shaped like a wristband and encapsulates a custom bell, microphone, and novel circuitry to facilitate self-auscultation for live transmission to a virtual physician. Frequency response testing was designed to verify the AusculBand’s captured cardiac sound frequency range. A comparative study was designed to test audio quality against and the Eko Duo’s, a leading competitor. Repeated cardiac auscultation signals at different points and background noise using both devices were obtained to quantify signal-to-noise ratio (SNR) on one user. Results: During frequency response analysis, the AusculBand captured frequencies up to 1997Hz in response to a signal that swept through a range of 0-3kHz. These results were within 0.2% of the 2kHz upper-limit of the cardiac range. SNR testing showed 27.29dB for the AusculBand and 24.02dB for the Eko Duo. The AusculBand is nearly twice as loud as the Eko Duo, and maintains a projected price of $80 (Eko Duo $320). Conclusions: The AusculBand is an inexpensive, patient-oriented digital stethoscope that beats industry standards in SNR and is compatible with existing telehealth platforms. Future modifications will add a single-lead ECG to promote the device as an all-in-one telemedicine tool for virtual cardiac analysis. [Formula presented] [Formula presented]
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We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Using a realistic representation of a social contact network for the Commonwealth of Virginia, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals' degree (number of social contacts) and total social proximity time is significantly more effective than the usually used age-based allocation strategy in reducing the number of infections, hospitalizations and deaths. The overall strategy is robust even: (i) if the social contacts are not estimated correctly;(ii) if the vaccine efficacy is lower than expected or only a single dose is given;(iii) if there is a delay in vaccine production and deployment;and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed. © 2022 Owner/Author.
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Background: Intravascular thrombosis and pulmonary fibrosis in COVID-19 patients with pneumonia are significantly associated with the severity of the disease. Vitamin K is known to balance the coagulation mechanisms and also prevent calcification and fibrosis of the extrahe-patic soft tissues. This narrative review focuses on the role of vitamin K as a linking factor for thrombotic as well as pulmonary complications of COVID-19. Materials and Methods: Article search was performed in databases of WHO, PubMed, Scopus and Clinical Trial Registry using appropriate keywords. Original articles included very few ob-servational studies which showed a reduced level of vitamin K as well as activated extrahepatic vitamin K Dependent Proteins (VKDP) in COVID-19 patients when compared to healthy con-trols. Chronic treatment with vitamin K Antagonists did not reduce the risk of in-hospital death. Docking study was performed using Swiss dock, and it demonstrated a significant interaction between menaquinone and SARS-CoV-2 main protease (SARS-CoV-2 Mpro). Results and Discussion: Deficiency of vitamin K in COVID-19 can be due to excessive use of antagonists or defective ingestion or absorption. This triggers an imbalance in the normal coagu-lation-anticoagulation mechanism by channeling the available vitamin K to the liver, thereby causing a deficiency of the same in extrahepatic tissues, thus finally leading to thrombosis. This also prevents carboxylation and activation of extrahepatic VKDP required to prevent the calcification of soft tissues, thus leading to lung fibrosis. Conclusion: Supplementation of vitamin K should be considered as a potentially modifiable risk factor in severe COVID-19. Randomized control trials are highly recommended to provide clear-er evidence on the same.
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This research uses the COVID-19 Trends and Impact Survey provided by Carnegie Mellon University in partnership with Facebook to study predictors and drivers of COVID-19 vaccine hesitancy in Virginia’s adult population. It estimates vaccine hesitancy rates at a zip code level in Virginia by applying multilevel statistical models. Our analysis identifies the demographic features of zip codes that are associated with vaccine hesitancy. It also examines the drivers of COVID-19 vaccine hesitancy across Virginia. Results show the presence of a larger percentage of Black and White population and a lower percentage of Hispanic population are predictors of higher vaccine hesitancy within a zip code in Virginia. Among these drivers, the biggest is system distrust, where individuals either do not trust the government or believe that the vaccine is not efficacious. Finally, it provides policy insights and tailored outreach programs for improving COVID-19 vaccination acceptability in different regions in Virginia. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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We study the role of vaccine acceptance in controlling the spread of COVID-19 in the US using AI-driven agent-based models. Our study uses a 288 million node social contact network spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12.59 billion daily interactions. The highly-resolved agent-based models use realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Developing a national model at this resolution that is driven by realistic data requires a complex scalable workflow, model calibration, simulation, and analytics components. Our workflow optimizes the total execution time and helps in improving overall human productivity.This work develops a pipeline that can execute US-scale models and associated workflows that typically present significant big data challenges. Our results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4K to 28.2K nationwide. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. Improving vaccine acceptance by 10% in all states increases averted infections from 4.5M to 4.7M (a 4.4% improvement) and total deaths from 28.2K to 29.9K (a 6% increase) nationwide. The analysis also reveals interesting spatio-temporal differences in COVID-19 dynamics as a result of vaccine acceptance. To our knowledge, this is the first national-scale analysis of the effect of vaccine acceptance on the spread of COVID-19, using detailed and realistic agent-based models. © 2021 IEEE.
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Understanding the effects of a pandemic on the public sentiment is an important challenge in the study of social dynamics during a global pandemic. This paper puts forward a case study that throws light on the psychological impact of the COVID-19 pandemic on the people living in the Indian subcontinent. The study is based on a pipeline that involves preprocessing, sentiment analysis, topic modelling, natural language processing and statistical analysis of Twitter data extracted in the form of tweets. The results demonstrate the effectiveness of this pipeline in understanding the temporal impact of the different lockdowns implemented in the span of the pandemic on the public sentiment, which can be useful for healthcare workers, authorities, and researchers. ©2021 IEEE