Bullwhip effect: Modelling and simulation osinusoidal stimulus considering food waste

Background: The current coronavirus disease 2019 (COVID-19) pandemic has stressed why a change towards resilient, robust and sustainable supply chains is more imperative than ever. This is especially true for supply chains of perishable foods, where issues such as the bullwhip effect cause not only economic but also environmental damage.Objectives: The key objectives of this study are to gain a deeper insight into correlations regarding the causes of the bullwhip effect and to see how a sinusoidal stimulus is affecting the generation of food waste. Method: A simplified beef food chain was modelled in Tecnomatix Plant Simulation®. As the bullwhip effect consists of a simplified parameterisation of an excitation duration (period length) and its height (amplitude), these two variables were used to generate a sinusoidal stimulus. The simulation results were statistically verified and checked for commonalities and differences with the already established scientific knowledge. Results: While the expected higher sensitivity of the front links of the supply chain to waste generation can be confirmed, the results of a long stimulation period suggest that the negative effects of the bullwhip effect do not increase indefinitely. Conclusion: The analysis of the results has shown that previous theories can be transferred, but that the variation of the variables entails new insights for the interdependencies of the amplitude and period length and their influence on the output variable waste. Contribution: The study contributes to a more holistic understanding of the bullwhip effect and, in particular, its implications within a perishable food supply chain. © 2023. The Authors. Licensee: AOSIS. This work is licensed under the Creative Commons Attribution License.

Population pharmacokinetics of nadroparin for thromboprophylaxis in COVID-19 intensive care unit patients.

AIMS: Nadroparin is administered to COVID-19 intensive care unit (ICU) patients as thromboprophylaxis. Despite existing population pharmacokinetic (PK) models for nadroparin in literature, the population PK of nadroparin in COVID-19 ICU patients is unknown. Moreover, optimal dosing regimens achieving anti-Xa target levels (0.3-0.7 IU/mL) are unknown. Therefore, a population PK analysis was conducted to investigate different dosing regimens of nadroparin in COVID-19 ICU patients. METHODS: Anti-Xa levels (n = 280) from COVID-19 ICU patients (n = 65) receiving twice daily (BID) 5700 IU of subcutaneous nadroparin were collected to perform a population PK analysis with NONMEM v7.4.1. Using Monte Carlo simulations (n = 1000), predefined dosing regimens were evaluated. RESULTS: A 1-compartment model with an absorption compartment adequately described the measured anti-Xa levels with interindividual variability estimated for clearance (CL). Inflammation parameters C-reactive protein, D-dimer and estimated glomerular filtration rate based on the Chronic Kidney Disease Epidemiology Collaboration equation allowed to explain the interindividual variability of CL. Moreover, CL was decreased in patients receiving corticosteroids (22.5%) and vasopressors (25.1%). Monte Carlo simulations demonstrated that 5700 IU BID was the most optimal dosing regimen of the simulated regimens for achieving prespecified steady-state t = 4 h anti-Xa levels with 56.7% on target (0.3-0.7 IU/mL). CONCLUSION: In our study, clearance of nadroparin is associated with an increase in inflammation parameters, use of corticosteroids, vasopression and renal clearance in critically ill patients. Furthermore, of the simulated regimens, targeted anti-Xa levels were most adequately achieved with a dosing regimen of 5700 IU BID. Future studies are needed to elucidate the underlying mechanisms of found covariate relationships.

Accelerating model-informed decisions for COVID-19 vaccine candidates using a model-based meta-analysis approach.

BACKGROUND: The COVID-19 pandemic has increased the need for innovative quantitative decision tools to support rapid development of safe and efficacious vaccines against SARS-CoV-2. To meet that need, we developed and applied a model-based meta-analysis (MBMA) approach integrating non-clinical and clinical immunogenicity and protection data. METHODS: A systematic literature review identified studies of vaccines against SARS-CoV-2 in rhesus macaques (RM) and humans. Summary-level data of 13 RM and 8 clinical trials were used in the analysis. A RM MBMA model was developed to quantify the relationship between serum neutralizing (SN) titres after vaccination and peak viral load (VL) post-challenge in RM. The translation of the RM MBMA model to a clinical protection model was then carried out to predict clinical efficacies based on RM data alone. Subsequently, clinical SN and efficacy data were integrated to develop three predictive models of efficacy - a calibrated RM MBMA, a joint (RM-Clinical) MBMA, and the clinical MBMA model. The three models were leveraged to predict efficacies of vaccine candidates not included in the model and efficacies against newer strains of SARS-CoV-2. FINDINGS: Clinical efficacies predicted based on RM data alone were in reasonable agreement with the reported data. The SN titre predicted to provide 50% efficacy was estimated to be about 21% of the mean human convalescent titre level, and that value was consistent across the three models. Clinical efficacies predicted from the MBMA models agreed with reported efficacies for two vaccine candidates (BBV152 and CoronaVac) not included in the modelling and for efficacies against delta variant. INTERPRETATION: The three MBMA models are predictive of protection against SARS-CoV-2 and provide a translational framework to enable early Go/No-Go and study design decisions using non-clinical and/or limited clinical immunogenicity data in the development of novel SARS-CoV-2 vaccines. FUNDING: This study was funded by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA.

A Digital Twin Based Approach For Ensuring Business Continuity Plan And Safe Return To Workplace

Organizations are struggling to ensure business continuity without compromising on delivery excellence in the face of Covid19 pandemic related uncertainties. The uncertainty exists along multiple dimensions such as virus mutations, infectivity and severity of new mutants, efficacy of vaccines against new mutants, waning of vaccine induced immunity over time, and lockdown / opening-up policies effected by city authorities. Moreover, this uncertainty plays out in a non-uniform manner across nations, states, cities, and even within the cities thus leading to highly heterogeneous evolution of pandemic. While Work From Home (WFH) strategy has served well to meet ever-increasing business demands without compromising on individual health safety, there has been an undeniable reduction in social capital. With Covid19 pandemic showing definite waning trends, organizations are considering the possibility of safe transition from WFH to Work From Office (WFO) or a hybrid mode of operation. An effective strategy needs to score equally well on possibly interfering dimensions such as risk of infection, project delivery, and employee wellness. As large organizations will typically have a large number of offices spread across a geography, the problem of arriving at office-specific strategies becomes non-trivial. Moreover, the strategies need to adapt over time to changes that cannot be deduced upfront. This calls for an approach that is amenable to quick and easy adaptation. Our contribution in this regard is constructing a Digital Twin by leveraging various modelling techniques to realistically represent the above mentioned aspects of interest that can be subjected to what-if scenario analysis. We further demonstrate its efficacy using a case study from a large organization. © 2022 SCS.

Understanding Emergent Dynamism of Covid-19 Pandemic in a City.

Predicting the evolution of a pandemic requires precise understanding of the pathogen and disease progression, the susceptible population group, means of transmission, and possible control mechanisms. It has been a significant challenge as Covid-19 virus (SARS-CoV-2 family) is not well understood yet; the entire human population is susceptible, and the virus transmits easily through airborne particles. Given its size and connectedness, it is not feasible to test the entire population and to isolate the infected individuals. Moreover, rapid and continuous mutation of virus open up the possibility of reinfection. As a result, the evolution of pandemic is not uniform and in-step throughout the world but is significantly influenced by local characteristics pertaining to people, places, dominant virus strain, extent of vaccination, and adherence to pandemic control interventions. Traditional macro-modelling techniques, such as variations of SEIR models, provide only a coarse-grained, 'lumped up' understanding of the pandemic which is not enough for exploring and understanding possible fine-grained factors that are effective for controlling the Covid-19 pandemic. This paper explores the problem space from a system theoretic perspective and presents a fine-grained city digital twin as an in-silico experimentation aid to understand the complex interplay of factors that influence infection spread and also help in controlling the Covid-19 pandemic. Our focus is not to speculate the possibility of the next wave or how the next wave may look like. Instead, we systematically seek answers to questions such as: what are indicators should we consider for a future wave? What are the parameters that may influence those indicators? When and why should they be tweaked (in terms of interventions) to control unacceptable situations? We validate our approach on the second and third waves of Covid-19 pandemic in Pune city.

An algebraic framework for structured epidemic modelling.

Pandemic management requires that scientists rapidly formulate and analyse epidemiological models in order to forecast the spread of disease and the effects of mitigation strategies. Scientists must modify existing models and create novel ones in light of new biological data and policy changes such as social distancing and vaccination. Traditional scientific modelling workflows detach the structure of a model-its submodels and their interactions-from its implementation in software. Consequently, incorporating local changes to model components may require global edits to the code base through a manual, time-intensive and error-prone process. We propose a compositional modelling framework that uses high-level algebraic structures to capture domain-specific scientific knowledge and bridge the gap between how scientists think about models and the code that implements them. These algebraic structures, grounded in applied category theory, simplify and expedite modelling tasks such as model specification, stratification, analysis and calibration. With their structure made explicit, models also become easier to communicate, criticize and refine in light of stakeholder feedback. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Natural Ventilation and Aerosol Particles Dispersion Indoors

Aerosol pollutant particles indoors significantly affect public health. The conventional wisdom is that natural ventilation will alleviate the dispersion of airborne or aerosol particles. However, we show that the problem is far more complex and that natural ventilation should be applied under specific conditions to be effective. We performed several simulations of a simplified (and easily reproducible) room with a window opening and aerosol particles stratified layers. Opening a window can scatter particles present in stratified layers indoors and potentially contribute to the degradation of indoor air quality for a significant period of time. Moreover, we show that thermal instabilities arising from the temperature gradients due to temperature differences between the indoor and outdoor environment spread the particles randomly indoors, adversely affecting air quality and architectural design. Recommendations for more efficient natural ventilation minimizing aerosol pollutant particles dispersed indoors are provided.

EML webinar overview: Simulation-assisted discovery of membrane targeting nanomedicine.

The COVID-19 pandemic has brought infectious diseases again to the forefront of global public health concerns. In this EML webinar (Gao, 2020), we discuss some recent work on simulation-assisted discovery of membrane targeting nanomedicine to counter increasing antimicrobial resistance and potential application of similar ideas to the current pandemic. A recent report led by the world health organization (WHO) warned that 10 million people worldwide could die of bacterial infections each year by 2050. To avert the crisis, membrane targeting antibiotics are drawing increasing attention due to their intrinsic advantage of low resistance development. In collaboration with a number of experimental groups, we show examples of simulation-assisted discovery of molecular agents capable of selectively penetrating and aggregating in bacterial lipid membranes, causing membrane permeability/rupture. Through systematic all-atom molecular dynamics simulations and free energy analysis, we demonstrate that the membrane activity of the molecular agents correlates with their ability to enter, perturb and permeabilize the lipid bilayers. Further study on different cell membranes demonstrates that the selectivity results from the presence of cholesterol in mammalian but not in bacterial membranes, as the cholesterol can condense the hydrophobic region of membrane, preventing the penetration of the molecular agents. Following the molecular penetration, we establish a continuum theory and derive the energetic driving force for the domain aggregation and pore growth on lipid membrane. We show that the energy barrier to membrane pore formation can be significantly lowered through molecular aggregation on a large domain with intrinsic curvature and a sharp interface. The theory is consistent with experimental observations and validated with coarse-grained molecular dynamics simulations of molecular domain aggregation leading to pore formation in a lipid membrane. The mechanistic modelling and simulation provide some fundamental principles on how molecular antimicrobials interact with bacterial membranes and damage them through domain aggregation and pore formation. For treating viral infections and cancer therapy, we discuss potential size- and lipid-type-based selectivity principles for developing membrane active nanomedicine. These studies suggest a general simulation-assisted platform to accelerate discovery and innovation in nanomedicine against infectious diseases. EML Webinar speakers are updated at https://imechanica.org/node/24132.

Assessing via Simulation the Operating Characteristics of the WHO Scale for COVID-19 Endpoints.

Many clinical trials of treatments for patients hospitalised for COVID-19 use an ordinal scale recommended by the World Heath Organisation. The scale represents intensity of medical intervention, with higher scores for interventions more burdensome for the patient, and highest score for death. There is uncertainty about use of this ordinal scale in testing hypotheses. With the objective of assessing the power and Type I error of potential endpoints and analyses based on the ordinal scale, trajectories of the score over 28 days were simulated for scenarios based closely on results of two trials recently published. The simulation used transition probabilities for the ordinal scale over time. No one endpoint was optimal across scenarios, but a ranked measure of trajectory fared moderately well in all scenarios. Type I error was controlled at close to the nominal level for all endpoints. Because not tied to a particular population with regard to baseline severity, the use of transition probabilities allows plausible assessment of endpoints in populations with configurations of baseline score for which data is not yet published, provided some data on the relevant transition probabilities are available. The results could support experts in the choice of endpoint based on the ordinal scale.

COVID-19: Development of a robust mathematical model and simulation package with consideration for ageing population and time delay for control action and resusceptibility.

The current global health emergency triggered by the pandemic COVID-19 is one of the greatest challenges we face in this generation. Computational simulations have played an important role to predict the development of the current pandemic. Such simulations enable early indications on the future projections of the pandemic and is useful to estimate the efficiency of control action in the battle against the SARS-CoV-2 virus. The SEIR model is a well-known method used in computational simulations of infectious viral diseases and it has been widely used to model other epidemics such as Ebola, SARS, MERS, and influenza A. This paper presents a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibility, where we can tune the exit conditions in the model to extend prediction on the current projections of the pandemic into three possible outcomes; death, recovery, and recovery with a possibility of resusceptibility. The model also considers specific information such as ageing factor of the population, time delay on the development of the pandemic due to control action measures, as well as resusceptibility with temporal immune response. Owing to huge variations in clinical symptoms exhibited by COVID-19, the proposed model aims to reflect better on the current scenario and case data reported, such that the spread of the disease and the efficiency of the control action taken can be better understood. The model is verified using two case studies based on the real-world data in South Korea and Northern Ireland.

Insights into the dynamics and control of COVID-19 infection rates.

This work aims to model, simulate and provide insights into the dynamics and control of COVID-19 infection rates. Using an established epidemiological model augmented with a time-varying disease transmission rate allows daily model calibration using COVID-19 case data from countries around the world. This hybrid model provides predictive forecasts of the cumulative number of infected cases. It also reveals the dynamics associated with disease suppression, demonstrating the time to reduce the effective, time-dependent, reproduction number. Model simulations provide insights into the outcomes of disease suppression measures and the predicted duration of the pandemic. Visualisation of reported data provides up-to-date condition monitoring, while daily model calibration allows for a continued and updated forecast of the current state of the pandemic.

The Impact of Staying at Home on Controlling the Spread of COVID -19: Strategy of Control

In this paper, we present a new mathematical model to describe the evolution of the COVID-19 in countries under the state of emergency. Where the COVID-19 pandemic is sweeping country after country. The Italian and Moroccan authorities have declared a state of emergency in response to the growing threat of this novel coronavirus (COVID- 19) outbreak by March 09 and 20, respectively. In-state of emergency, citizens cannot go out to public spaces without special authorization from local authorities. But after all these efforts exerted by these authorities, the number of new cases of the COVID-19 continues to rise significantly, which confirms the lack of commitment of some citizens. First, we aim to investigate the cause of new infections despite all strategies of control followed in these countries including media reports, awareness, and treatment, self-distancing and quarantine, by estimating the number of these people who underestimate the lives and safety of citizens and put them at risk. To do this, we use real data of the COVID-19 in Italy and Morocco to estimate the parameters of the model, and then we predict the number of these populations. Second, we propose an optimal control strategy that could be the optimal and the efficient way for the Moroccan and Italian authorities and other countries to make the state of emergency more efficient and to control the spread of the COVID-19. The model is analyzed for both countries and then to compare the implications of the obtained results. Numerical examples are provided to illustrate the efficiency of the strategy of control that we propose and to show what would have been happened in Morocco and Italy if this strategy of control was applied early. [ABSTRACT FROM AUTHOR] Copyright of Revista Mexicana de Ingeniería Biomédica is the property of Sociedad Mexicana de Ingenieria Biomedica, A.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)