Three dimensional epidemic model with non-monotonic incidence and saturated treatment: A case study of SARS infection of Hong Kong 2003 scenario

In this paper, we have proposed a mathematical compartmental model with non-monotonic incidence and saturated treatment and we have validated the model with SARS infection in Hong Kong, 2003. We have analysed the stability of disease free and endemic equilibria as well as different bifurcations. We have shown that the epidemic disappears if the cure rate of treatment crosses a threshold value. We have obtained a necessary and sufficient condition for backward bifurcation, which shows the basic reproduction number less than unity is not sufficient to eradicate the disease completely. Saddle–node and Hopf bifurcation with respect to awareness factor have been investigated, which shows that the awareness factor is effective to change the disease dynamics. The model has been fitted to SARS cases in Hong Kong. The most effective parameters for controlling infections have been identified through sensitivity analysis. Moreover, we have investigated how the number of infected cases reduces if there was some vaccination polices in SARS infection. Finally, the model has been also used as an optimal control problem as vaccination and treatment controls are time dependent functions.

Development and Validation of RP-HPLC Method for the simultaneous determination of Nirmatrelvir and Ritonavir in bulk and pharmaceutical formulation

In this study, a rapid and sensitive stability indicating reversed phase HPLC method was developed for quantitation of Nirmatrelvir and Ritonavir simultaneously in bulk and tablet formulation. Nirmatrelvir and ritonavir were separated on a Thermo C18 column with mobile phase containing 0.01M potassium dihydrogen phosphate buffer and acetonitrile (45:55, v/v). The flow rate was 1 mL/min and detection wavelength was 272 nm. Method linearity was established over a range of 75-225 μg/mL for nirmatrelvir and 50-150 μg/mL for ritonavir. Limit of quantification was 0.694μg/mL for nirmatrelvir and 0.820μg/mL for ritonavir. The recovery (%) was 99.96 to 100.45 (Nirmatrelvir) and 100.25 to 101.35 (Ritonavir). The method precisions were 0.11% (Nirmatrelvir) and 0.33% (Ritonavir). Method was suitable to assay nirmatrelvir and ritonavir in tablet formulation (Paxlovid). Stress degradation studies have shown that this method can be implemented to assay nirmatrelvir and ritonavir in the presence of its degradants. © 2023 World Research Association. All rights reserved.

Emerging Virtual Communities of Practice during Crises: A Sustainable Model Validating the Levels of Peer Motivation and Support

The purpose of this study is to validate the role of social media among peers in a virtual community of practice, VCoP, by applying the Delphi technique of data collection and analysis. The study adopts the Grounded Theory methodology to identify the theoretical frame that is developed from the data analysis. The research design identified the areas of social and professional support from Maslow's hierarchy of human needs from the faculty members' responses that were chosen by "purposive sampling” rather than random sampling The qualitative data collection took three months, wherein 26 faculty members interacting on WhatsApp during COVID-19 were identified as experts: one of the corner elements of the Delphi technique. The thematic analysis of the results indicated that fulfilling Maslow's needs was an integral component of social media VCoP. However, unlike Maslow's model, expressions of the need for safety, belonging, self-esteem, and self-actualization took turns as the most important need depending on changing context, gender, and individual preferences. The answer to the research questions, thus, becomes embedded in the research methodology that involves instructors' perceptions as a validating element of the findings. The main recommendation is to replicate the study in various contexts to monitor faculty well-being to reach a sustainable educational environment. © 2023 by the authors.

Prospective validation of a dynamic prognostic model for identifying COVID-19 patients at high risk of rapid deterioration.

BACKGROUND: We sought to develop and prospectively validate a dynamic model that incorporates changes in biomarkers to predict rapid clinical deterioration in patients hospitalized for COVID-19. METHODS: We established a retrospective cohort of hospitalized patients aged ≥18 years with laboratory-confirmed COVID-19 using electronic health records (EHR) from a large integrated care delivery network in Massachusetts including > 40 facilities from March to November 2020. A total of 71 factors, including time-varying vital signs and laboratory findings during hospitalization were screened. We used elastic net regression and tree-based scan statistics for variable selection to predict rapid deterioration, defined as progression by two levels of a published severity scale in the next 24 hours. The development cohort included the first 70% of patients identified chronologically in calendar time; the latter 30% served as the validation cohort. A cut-off point was estimated to alert clinicians of high risk of imminent clinical deterioration. RESULTS: Overall, 3,706 patients (2,587 in the development and 1,119 in the validation cohort) met the eligibility criteria with a median of 6 days of follow-up. Twenty-four variables were selected in the final model, including 16 dynamic changes of laboratory results or vital signs. Area under the ROC curve was 0.81 (95% CI, 0.79 - 0.82) in the development set and 0.74 (95% CI, 0.71-0.78) in the validation set. The model was well calibrated (slope = 0.84 and intercept = -0.07 on the calibration plot in the validation set). The estimated cut-off point, with a positive predictive value of 83%, was 0.78. CONCLUSIONS: Our prospectively validated dynamic prognostic model demonstrated temporal generalizability in a rapidly evolving pandemic and can be used to inform day-to-day treatment and resource allocation decisions based on dynamic changes in biophysiological factors. This article is protected by copyright. All rights reserved.

How can banks and finance companies incorporate value chain factors in their risk management strategy? The case of agro-food firms

A value chain framework for guiding the financial firms in their credit decisions is urgent, as the current COVID-19 pandemic has highlighted, but missing in the extant literature, particularly for those that lend to industries sensitive to value and supply chain bottlenecks. This study creates knowledge in value chain finance, a big untapped and un-researched market. It constructs, confirms, and validates a value chain framework for assessing risks in lending to Agro and Food Processing firms in which value chain risks are major business concerns globally. To pursue the objectives of the study, we use a novel methodology that integrates the Modified Delphi technique, exploratory factor analysis, confirmatory factor analysis, and discriminant analysis. Based on testing and analysis of primary data, including loan data, a framework comprising six factors is proposed for use in conjunction with existing risk assessment models of finance companies to improve the quality of their credit decisions, contributing to their performance sustainability. © 2022 ERP Environment and John Wiley & Sons Ltd.

Quantile time-frequency connectedness between cryptocurrency volatility and renewable energy volatility during the COVID-19 pandemic and Ukraine-Russia conflicts

Our article employs a quantile vector autoregression (QVAR) to identify the connectedness of seven variables from April 1, 2019, to June 13, 2022, in order to examine the relationships between crypto volatility and energy volatility. Our findings reveal that the dynamic connectedness is approximately 25% in the short term and approximately 9% in the long term. The 50% quantile equates to the overall average connectedness of the entire period, according to dynamic net total directional connectedness over a quantile, which also indicates that connectedness is very intense for both highly positive changes (above the 80% quantile) and crypto and energy volatility (below the 20% quantile). With the exception of the early 2022 period when the Crypto Volatility Index transmits a net of shocks because of the Ukraine-Russia Conflict, dynamic net total directional connectedness implies that in the short term, the Crypto Volatility Index acts as a net shock receiver across time. While this indicator is a net shock receiver for long-term dynamics, wind energy is a net shock transmitter during the short term. Green bonds are a short-term net shock receiver. This role is valid in the long term. Clean energy and solar energy are the long-term net transmitters of shocks;nevertheless, the series is always and only momentarily a net receiver of shocks because of the short-term dynamics. Natural gas and crude oil play roles in both two quantiles. Dynamic net pairwise directional connectedness over a quantile suggests that uncertain events like the COVID-19 epidemic or Ukraine-Russia Conflict influence cryptocurrency volatility and renewable energy volatility. © 2022 Elsevier Ltd

General bounds on the area under the receiver operating characteristic curve and other performance measures when only a single sensitivity and specificity point is known

Receiver operating characteristic (ROC) curves are often used to quantify the performance of predictive models used in diagnosis, risk stratification and rating systems. The ROC area under the curve (AUC) summarizes the ROC in a single statistic, which also provides a probabilistic interpretation that is isomorphic to the Mann– Whitney–Wilcoxon test. In many settings, such as those involving diagnostic tests for diseases or antibodies, information about the ROC is not reported;instead the true positive. TP / and true negative. TN / rates are reported for a single threshold value. We demonstrate how to calculate the upper and lower bounds for the ROC AUC, given a single. TP;TN / pair. We use simple geometric arguments only, and we present two examples of real-world applications from medicine and finance, involving Covid-19 diagnosis and credit card fraud detection, respectively. In addition, we introduce formally the notion of “pathological” ROC curves and “well-behaved” ROC curves. In the case of well-behaved ROC curves, the bounds on the AUC may be made tighter. In certain special cases involving pathological ROC curves that result from what we term “George Costanza” classifiers, we may transform predictions to obtain well-behaved ROC curves with higher AUC than the original decision process. Our results also enable the calculation of other quantities of interest, such as Cohen’s d or the Pearson correlation between a diagnostic outcome and an actual outcome. These results facilitate the direct comparison of reported performance when model or diagnostic performance is reported for only a single score threshold. © 2022. Infopro Digital Risk (IP) Limited

Sequence-assignment validation in cryo-EM models with checkMySequence.

The availability of new artificial intelligence-based protein-structure-prediction tools has radically changed the way that cryo-EM maps are interpreted, but it has not eliminated the challenges of map interpretation faced by a microscopist. Models will continue to be locally rebuilt and refined using interactive tools. This inevitably results in occasional errors, among which register shifts remain one of the most difficult to identify and correct. Here, checkMySequence, a fast, fully automated and parameter-free method for detecting register shifts in protein models built into cryo-EM maps, is introduced. It is shown that the method can assist model building in cases where poorer map resolution hinders visual interpretation. It is also shown that checkMySequence could have helped to avoid a widely discussed sequence-register error in a model of SARS-CoV-2 RNA-dependent RNA polymerase that was originally detected thanks to a visual residue-by-residue inspection by members of the structural biology community. The software is freely available at https://gitlab.com/gchojnowski/checkmysequence.

Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19.

Rationale & Objective: Acute kidney injury (AKI) is common in patients hospitalized with COVID-19, but validated, predictive models for AKI are lacking. We aimed to develop the best predictive model for AKI in hospitalized patients with coronavirus disease 2019 and assess its performance over time with the emergence of vaccines and the Delta variant. Study Design: Longitudinal cohort study. Setting & Participants: Hospitalized patients with a positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction result between March 1, 2020, and August 20, 2021 at 19 hospitals in Texas. Exposures: Comorbid conditions, baseline laboratory data, inflammatory biomarkers. Outcomes: AKI defined by KDIGO (Kidney Disease: Improving Global Outcomes) creatinine criteria. Analytical Approach: Three nested models for AKI were built in a development cohort and validated in 2 out-of-time cohorts. Model discrimination and calibration measures were compared among cohorts to assess performance over time. Results: Of 10,034 patients, 5,676, 2,917, and 1,441 were in the development, validation 1, and validation 2 cohorts, respectively, of whom 776 (13.7%), 368 (12.6%), and 179 (12.4%) developed AKI, respectively (P = 0.26). Patients in the validation cohort 2 had fewer comorbid conditions and were younger than those in the development cohort or validation cohort 1 (mean age, 54 ± 16.8 years vs 61.4 ± 17.5 and 61.7 ± 17.3 years, respectively, P < 0.001). The validation cohort 2 had higher median high-sensitivity C-reactive protein level (81.7 mg/L) versus the development cohort (74.5 mg/L; P < 0.01) and higher median ferritin level (696 ng/mL) versus both the development cohort (444 ng/mL) and validation cohort 1 (496 ng/mL; P < 0.001). The final model, which added high-sensitivity C-reactive protein, ferritin, and D-dimer levels, had an area under the curve of 0.781 (95% CI, 0.763-0.799). Compared with the development cohort, discrimination by area under the curve (validation 1: 0.785 [0.760-0.810], P = 0.79, and validation 2: 0.754 [0.716-0.795], P = 0.53) and calibration by estimated calibration index (validation 1: 0.116 [0.041-0.281], P = 0.11, and validation 2: 0.081 [0.045-0.295], P = 0.11) showed stable performance over time. Limitations: Potential billing and coding bias. Conclusions: We developed and externally validated a model to accurately predict AKI in patients with coronavirus disease 2019. The performance of the model withstood changes in practice patterns and virus variants.

Dynamics of SEIR model: A case study of COVID-19 in Italy

COVID-19 takes a gigantic form worldwide in a short time from December, 2019. For this reason, World Health Organization (WHO) declared COVID-19 as a pandemic outbreak. In the early days when this outbreak began, the coronavirus spread rapidly in the community due to a lack of knowledge about the virus and the unavailability of medical facilities. Therefore it becomes a significant challenge to control the influence of the disease outbreak. In this situation, mathematical models are an important tool to employ an effective strategy in order to fight against this pandemic. To study the disease dynamics and their influence among the people, we propose a deterministic mathematical model for the COVID-19 outbreak and validate the model with real data of Italy from 15th Feb 2020 to 14th July 2020. We establish the positivity and boundedness of solutions, local stability of equilibria to examine its epidemiological relevance. Sensitivity analysis has been performed to identify the highly influential parameters which have the most impact on basic reproduction number (R0). We estimate the basic reproduction number (R0) from available data in Italy and also study effective reproduction numbers based on reported data per day from 15th Feb 2020 to 14th July 2020 in Italy. Finally, the disease control policy has been summarized in the conclusion section.

Analysis of epidemic spread dynamics using a PDE model and COVID-19 data from Hamilton County OH USA

We study the spatiotemporal dynamics of an epidemic spread using a compartmentalized PDE model. The model is validated using COVID-19 data from Hamilton County, Ohio, USA. The model parameters are estimated using a month of recorded data and then used to forecast the infection spread over the next ten days. The model is able to accurately estimate the key dynamic characteristics of COVID-19 spread in the county. Additionally, a stability analysis indicates that the model is robust to disturbances and perturbations which, for instance, could be used to represent the effects of super spreader events. We also use the modeling framework to analyse and discuss the impact of Non-pharmaceutical interventions (NPIs) for mitigation of infection. Our results suggest that such models can yield useful short and medium term predictive characterization of an epidemic spread in a restricted geographical region and also help formulate effective NPIs for mitigation. The results also signify the importance of further research into the accurate analytical representation of specific NPIs and hence their dampening effects on an infection spread. Copyright © 2021 The Authors.

Application of Deep Learning Techniques for Detection of COVID-19 Using Lung CT Scans: Model Development and Validation

Due to the worldwide spread of the COVID-19, efforts to combat the disease have intensified. Among these efforts, the only effective way to prevent further spread to communities and disease progression is to control the spread of the disease, which is done using public vaccination as well as repeated and rapid testing to diagnose and isolate sick people. In this regard, computer systems with the help of medical science can speed up the diagnosis of COVID-19 disease. This paper, proposed a review of the methods used in rapid and automatic detection of COVID-19 using CT scan images. Finally, by presenting a new method based on deep learning, the obtained results compared with the results of widely used algorithms such as VGG-16 and MobileNet. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Identifying the Factors Influencing Patients' Telehealth Visit Satisfaction: Survey Validation Through a Structural Equation Modeling Approach.

Introduction: The COVID-19 pandemic accelerated the adoption of telehealth as an alternative to in-person hospital visits. To understand the factors impacting the quality of telehealth services, there is a need for validated survey instruments and conceptual frameworks. The objective of this study is to validate a telehealth patient satisfaction survey by structural equation modeling (SEM) and determine the relationship between the factors in the proposed telehealth patient satisfaction model (TPSM). Methods: We conducted a cross-sectional survey of pediatric patients and families receiving care from a comprehensive pediatric hospital in the Midwest between September 2020 and January 2021. In total, 2,039 usable responses were collected. We used an SEM approach by performing confirmatory factor analysis with Diagonally Weighted Least Squares modeling and Partial Least Squares-Path Modeling to establish the structural validity and examined the relationships among the constructs of "Admission Process" (AP), "Perceived Quality of Service" (PQS), and "Telehealth Satisfaction" (TS). Results: Participants were predominantly White (75%) and English-speaking (95%) parents (85%) of patients (mean age of patients was 10.2 years old). The survey responses were collected from patients visiting 43 department specialties, whereas 50% were behavioral and occupational therapy patients. The structural model showed that the admission process (AP) had a strong positive impact on perceived quality of service (PQS) (p = 0.67, t = 36.1, p < 0.001). The PQS had a strong positive impact on telehealth satisfaction (TS) (p = 0.66, t = 31.8, p < 0.001). The AP had a low positive direct impact on TS (p = 0.16, t = 7.46, p < 0.05). Overall, AP and PQS explained 61% variances (R2) of TS. Conclusions: We validated a newly proposed TS assessment model by using SEM. The TPSM will inform researchers to better understand the influencing factors in TS and help health care systems to improve telehealth patient satisfaction through a validated model.

Mathematical modeling approach to predict covid-19 infected people in sri lanka

The novel corona virus (COVID-19) has badly affected many countries (more than 180 countries including China) in the world. More than 90% of the global COVID-19 cases are currently outside China. The large, unanticipated number of COVID-19 cases has interrupted the healthcare system in many countries and created shortages for bed space in hospitals. Consequently, better estimation of COVID-19 infected people in Sri Lanka is vital for government to take suitable action. This paper investigates predictions on both the number of the first and the second waves of COVID-19 cases in Sri Lanka. First, to estimate the number of first wave of future COVID-19 cases, we develop a stochastic forecasting model and present a solution technique for the model. Then, another solution method is proposed to the two existing models (SIR model and Logistic growth model) for the prediction on the second wave of COVID-19 cases. Finally, the proposed model and solution approaches are validated by secondary data obtained from the Epidemiology Unit, Ministry of Health, Sri Lanka. A comparative assessment on actual values of COVID-19 cases shows promising performance of our developed stochastic model and proposed solution techniques. So, our new finding would definitely be benefited to practitioners, academics and decision makers, especially the government of Sri Lanka that deals with such type of decision making. © 2022 the Author(s), licensee AIMS Press.

Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability.

Since China's launch of the COVID-19 vaccination, the situation of the public, especially the mobile population, has not been optimistic. We investigated 782 factory workers for whether they would get a COVID-19 vaccine within the next 6 months. The participants were divided into a training set and a testing set for external validation conformed to a ratio of 3:1 with R software. The variables were screened by the Lead Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Then, the prediction model, including important variables, used a multivariate logistic regression analysis and presented as a nomogram. The Receiver Operating Characteristic (ROC) curve, Kolmogorov-Smirnov (K-S) test, Lift test and Population Stability Index (PSI) were performed to test the validity and stability of the model and summarize the validation results. Only 45.54% of the participants had vaccination intentions, while 339 (43.35%) were unsure. Four of the 16 screened variables-self-efficacy, risk perception, perceived support and capability-were included in the prediction model. The results indicated that the model has a high predictive power and is highly stable. The government should be in the leading position, and the whole society should be mobilized and also make full use of peer education during vaccination initiatives.

A dynamic risk assessment model to assess the impact of the coronavirus (COVID-19) on the sustainability of the biomass supply chain: A case study of a U.S. biofuel industry.

The novel coronavirus (COVID-19) is highly detrimental, and its death distribution peculiarity has severely affected people's health and the operations of businesses. COVID-19 has wholly undermined the global economy, including inflicting significant damage to the ever-emerging biomass supply chain; its sustainability is disintegrating due to the coronavirus. The biomass supply chain must be sustainable and robust enough to adapt to the evolving and fluctuating risks of the market due to the coronavirus or any potential future pandemics. However, no such study has been performed so far. To address this issue, investigating how COVID-19 influences a biomass supply chain is vital. This paper presents a dynamic risk assessment methodological framework to model biomass supply chain risks due to COVID-19. Using a dynamic Bayesian network (DBN) formalism, the impacts of COVID-19 on the performance of biomass supply chain risks have been studied. The proposed model has been applied to the biomass supply chain of a U.S.-based Mahoney Environmental® company in Washington, USA. The case study results show that it would take one year to recover from the maximum damage to the biomass supply chain due to COVID-19, while full recovery would require five years. Results indicate that biomass feedstock gate availability (FGA) is 2%, due to pandemic and lockdown conditions. Due to the availability of vaccination and gradual business reopenings, this availability increases to 92% in the second year. Results also indicate that the price of fossil-based fuel will gradually increase after one year of the pandemic; however, the market prices of fossil-based fuel will not revert to pre-coronavirus conditions even after nine years. K-fold cross-validation is used to validate the DBN. Results of validation indicate a model accuracy of 95%. It is concluded that the pandemic has caused risks to the sustainability of biomass feedstock, and the current study can help develop risk mitigation strategies.

Development of a novel hybrid cognitive model validation framework for implementation under COVID-19 restrictions.

The purpose of this study was to develop a method for validation of cognitive models consistent with the remote working situation arising from COVID-19 restrictions in place in Spring 2020. We propose a framework for structuring validation tasks and applying a scoring system to determine initial model validity. We infer an objective validity level for cognitive models requiring no in-person observations, and minimal reliance on remote usability and observational studies. This approach has been derived from the necessity of the COVID-19 response, however, we believe this approach can lower costs and reduce timelines to initial validation in post-Covid-19 studies, enabling faster progress in the development of cognitive engineering systems. A three-stage hybrid validation framework was developed based on existing validation methods and was adapted to enable compliance with the specific limitations derived from COVID-19 response restrictions. This validation method includes elements of argument-based validation combined with a cognitive walkthrough analysis, and reflexivity assessments. We conducted a case study of the proposed framework on a developmental cognitive model of cardiovascular surgery to demonstrate application of a real-world validation task. This framework can be easily and quickly implemented by a small research team and provides a structured validation method to increase confidence in assumptions as well as to provide evidence to support validity claims in the early stages of model development.

Structure and dynamics of membrane protein in SARS-CoV-2.

SARS-CoV-2 membrane (M) protein performs a variety of critical functions in virus infection cycle. However, the expression and purification of membrane protein structure is difficult despite tremendous progress. In this study, the 3 D structure is modeled followed by intensive validation and molecular dynamics simulation. The lack of suitable homologous templates (>30% sequence identities) leads us to construct the membrane protein models using template-free modeling (de novo or ab initio) approach with Robetta and trRosetta servers. Comparing with other model structures, it is evident that trRosetta (TM-score: 0.64; TM region RMSD: 2 Å) can provide the best model than Robetta (TM-score: 0.61; TM region RMSD: 3.3 Å) and I-TASSER (TM-score: 0.45; TM region RMSD: 6.5 Å). 100 ns molecular dynamics simulations are performed on the model structures by incorporating membrane environment. Moreover, secondary structure elements and principal component analysis (PCA) have also been performed on MD simulation data. Finally, trRosetta model is utilized for interpretation and visualization of interacting residues during protein-protein interactions. The common interacting residues including Phe103, Arg107, Met109, Trp110, Arg131, and Glu135 in the C-terminal domain of M protein are identified in membrane-spike and membrane-nucleocapsid protein complexes. The active site residues are also predicted for potential drug and peptide binding. Overall, this study might be helpful to design drugs and peptides against the modeled membrane protein of SARS-CoV-2 to accelerate further investigation. Communicated by Ramaswamy H. Sarma.