Enhancing Pharmaceutical Supply Chain Resilience: A Study of Pharmaceutical Companies in Multiple Geographies

Background: Pharmaceutical businesses had enormous difficulties in product distribution during COVID-19, and the solution to this perpetual issue is a resilient supply chain. Aim(s): The study aims to understand the vulnerabilities to which it subjected the pharmaceutical product distribution supply chains during the COVID-19 pandemic and further develop an adaptive model through which the pharmaceutical product supply chain can enhance its resilience capabilities. Material(s) and Method(s): The conceptual model is developed for the supply chain of pharmaceutical companies based on the literature survey, and then the conceptual model is explored through factor analysis. Researchers have developed a validated model after a statistical analysis using Cronbach's alpha. Subjective analysis has concluded that the pharmaceutical supply chain's resilience is driven by factors such as "trade cost," which comprises transport cost, business practices, and raw material sourcing cost;"shock propagation," which comprises country-specific shocks, production shocks, and policy changes;and "technological infrastructure bottleneck," which relates to the availability of cold chain storage warehouses and refrigerated transport vehicle facilities. Result(s): An empirical model pertaining to supply chain resilience may be further studied with different geographies, like Pune, Hyderabad, and Delhi NCR, for the purpose of generalizing the study. Conclusion(s): The identified major factors were trade cost, shock propagation, and technological infrastructure bottlenecks. The sensitivity of the issue under investigation required a personal touch to the survey, as the COVID-19 pandemic had left these respondents emotionally vulnerable. As COVID-19 is the recent catastrophe that has hit humanity, it has made the pharmaceutical product distribution channel vulnerable during the pandemic. This difficult time of pandemic has really tested the pharmaceutical products' supply chain capabilities as well.Copyright © 2023, Association of Pharmaceutical Teachers of India. All rights reserved.

Application of Artificial Intelligence on Post Pandemic Situation and Lesson Learn for Future Prospects

Coronavirus disease (COVID-19) pandemic has intensively damaged human socio-economic lives and the growth of countries around the world. Many efforts have been made in the direction of artificial intelligence (AI) techniques to detect the corona at an early stage and take necessary precautions to stop it from spreading or recovery from the infection. However, the situation and solutions are still challenging. In this paper, we proposed various technological aspects, solutions using a supervised/unsupervised manner and continuous health monitoring with physiological parameters. Finally, the performance of COVID-19 detection with Gaussian mixture model-universal background model (GMM-UBM) technique using the voice signal has been demonstrated. The developed system achieves the COVID-19 detection performance in terms of areas under receiver operating characteristic (ROC) curves in the range 60–67%. Moreover, the various lessons learned from the current COVID-19 crisis are presented for future directions.

FlowTele: Remotely Shaping Traffic on Internet-Scale Networks

Internet content providers often deliver content through bandwidth bottlenecks that are out of their control. Thus, despite often having massively over-provisioned upstream servers, the content providers still cannot control the end-to-end user experience. This paper explores remote traffic shaping, allowing the content provider to allocate its share of a remote bottleneck link across its users using a metric other than TCP fairness, while remaining TCP-friendly to cross traffic on the bottleneck link. To evaluate this approach, we designed FlowTele, the first system that shapes outbound traffic on an Internet-scale network to optimize provider-selected metrics, using source control with neither in-network support nor special client support. Our extensive evaluations over the Internet show that by strategically reallocating bandwidth among provider-owned co-bottlenecked flows, FlowTele improves the provider's total revenue by roughly 20% - 30% in various network settings, compared with both (i) status quo TCP fairshare and (ii) recent practice by content providers that proactively throttles video quality during the COVID-19 pandemic, while being TCP-friendly to cross-traffic. Besides revenue, we also study other metrics, such as QoE fairness, that a content provider may wish to optimize using FlowTele. © 2022 Owner/Author.

E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network.

Background: Recently, the coronavirus disease 2019 (COVID-19) has caused mortality of many people globally. Thus, there existed a need to detect this disease to prevent its further spread. Hence, the study aims to predict COVID-19 infected patients based on deep learning (DL) and image processing. Objectives: The study intends to classify the normal and abnormal cases of COVID-19 by considering three different medical imaging modalities namely ultrasound imaging, X-ray images and CT scan images through introduced attention bottleneck residual network (AB-ResNet). It also aims to segment the abnormal infected area from normal images for localizing localising the disease infected area through the proposed edge based graph cut segmentation (E-GCS). Methodology: AB-ResNet is used for classifying images whereas E-GCS segment the abnormal images. The study possess various advantages as it rely on DL and possess capability for accelerating the training speed of deep networks. It also enhance the network depth leading to minimum parameters, minimising the impact of vanishing gradient issue and attaining effective network performance with respect to better accuracy. Results/Conclusion: Performance and comparative analysis is undertaken to evaluate the efficiency of the introduced system and results explores the efficiency of the proposed system in COVID-19 detection with high accuracy (99%).

Narrow transmission bottlenecks and limited within-host viral diversity during a SARS-CoV-2 outbreak on a fishing boat.

The long-term evolution of viruses is ultimately due to viral mutants that arise within infected individuals and transmit to other individuals. Here, we use deep sequencing to investigate the transmission of viral genetic variation among individuals during a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak that infected the vast majority of crew members on a fishing boat. We deep-sequenced nasal swabs to characterize the within-host viral population of infected crew members, using experimental duplicates and strict computational filters to ensure accurate variant calling. We find that within-host viral diversity is low in infected crew members. The mutations that did fix in some crew members during the outbreak are not observed at detectable frequencies in any of the sampled crew members in which they are not fixed, suggesting that viral evolution involves occasional fixation of low-frequency mutations during transmission rather than persistent maintenance of within-host viral diversity. Overall, our results show that strong transmission bottlenecks dominate viral evolution even during a superspreading event with a very high attack rate.

Evaluation of operating efficiency of the Baggage handling system, depending on the damage of identification labels

The authors investigated the impact of anti-pandemic measures on the function of the Baggage Handling System (BHS). Disinfection procedures can damage the bag tag (BT) used to identify and sort luggage. Data obtained from a standard literature search were used to construct a 3D simulation in the Tecnomatix Plant Simulation 15 software. The simulation showed that decreasing the success rate of BT automatic scanning produces increasing the need of manual scanning. It can eventually result in the formation of additional bottlenecks, identification problems, and the loss of luggage. This consequently leads to further delays in the handling of luggage (flights). It also considerable increases the possibility of transmission of the corona virus (covid disease) due to additional human contact, and in addition to increased costs due to the need to expand manual scanning workplaces. Considering all factors, it is recommended to look for suitable disinfection methods with no damage to the BT along with other BHS components. © 2021 IEEE.

Limited genomic reconstruction of SARS-CoV-2 transmission history within local epidemiological clusters.

A detailed understanding of how and when severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission occurs is crucial for designing effective prevention measures. Other than contact tracing, genome sequencing provides information to help infer who infected whom. However, the effectiveness of the genomic approach in this context depends on both (high enough) mutation and (low enough) transmission rates. Today, the level of resolution that we can obtain when describing SARS-CoV-2 outbreaks using just genomic information alone remains unclear. In order to answer this question, we sequenced forty-nine SARS-CoV-2 patient samples from ten local clusters in NW Spain for which partial epidemiological information was available and inferred transmission history using genomic variants. Importantly, we obtained high-quality genomic data, sequencing each sample twice and using unique barcodes to exclude cross-sample contamination. Phylogenetic and cluster analyses showed that consensus genomes were generally sufficient to discriminate among independent transmission clusters. However, levels of intrahost variation were low, which prevented in most cases the unambiguous identification of direct transmission events. After filtering out recurrent variants across clusters, the genomic data were generally compatible with the epidemiological information but did not support specific transmission events over possible alternatives. We estimated the effective transmission bottleneck size to be one to two viral particles for sample pairs whose donor-recipient relationship was likely. Our analyses suggest that intrahost genomic variation in SARS-CoV-2 might be generally limited and that homoplasy and recurrent errors complicate identifying shared intrahost variants. Reliable reconstruction of direct SARS-CoV-2 transmission based solely on genomic data seems hindered by a slow mutation rate, potential convergent events, and technical artifacts. Detailed contact tracing seems essential in most cases to study SARS-CoV-2 transmission at high resolution.

Real-Time Face Mask Detection and Analysis System

Due to COVID-19 situation, we need to wear face masks in public places. Reports say that wearing face mask at public places and at workspace reduces the transmission of virus as the SARS-CoV-2 spreads through atmosphere among people, at gathering in any environment. In this paper, a real-time face mask detection system is presented which will detect mask presence on the face using TensorFlow. We are using MobileNetV2 model to provide a greater accuracy in determining the mask presence. Accuracy obtained is 99%. Older systems do not provide a proper working system. A face mask detector has been designed with computer vision using Python, OpenCV, Keras, and TensorFlow. Video surveillance input can be given directly, and our primary purpose is to identify to check people are wearing masks on daily basis or not wearing masks and prepare a weekly and monthly report based on this observation and display the data on an interactive web application. System provides option to see the historical records, thereby reducing transmission. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features.

We present an experimental investigation into the effectiveness of transfer learning and bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath and speech. This type of screening is non-contact, does not require specialist medical expertise or laboratory facilities and can be deployed on inexpensive consumer hardware such as a smartphone. We use datasets that contain cough, sneeze, speech and other noises, but do not contain COVID-19 labels, to pre-train three deep neural networks: a CNN, an LSTM and a Resnet50. These pre-trained networks are subsequently either fine-tuned using smaller datasets of coughing with COVID-19 labels in the process of transfer learning, or are used as bottleneck feature extractors. Results show that a Resnet50 classifier trained by this transfer learning process delivers optimal or near-optimal performance across all datasets achieving areas under the receiver operating characteristic (ROC AUC) of 0.98, 0.94 and 0.92 respectively for all three sound classes: coughs, breaths and speech. This indicates that coughs carry the strongest COVID-19 signature, followed by breath and speech. Our results also show that applying transfer learning and extracting bottleneck features using the larger datasets without COVID-19 labels led not only to improved performance, but also to a marked reduction in the standard deviation of the classifier AUCs measured over the outer folds during nested cross-validation, indicating better generalisation. We conclude that deep transfer learning and bottleneck feature extraction can improve COVID-19 cough, breath and speech audio classification, yielding automatic COVID-19 detection with a better and more consistent overall performance.

Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis.

Visual explanation methods have an important role in the prognosis of the patients where the annotated data is limited or unavailable. There have been several attempts to use gradient-based attribution methods to localize pathology from medical scans without using segmentation labels. This research direction has been impeded by the lack of robustness and reliability. These methods are highly sensitive to the network parameters. In this study, we introduce a robust visual explanation method to address this problem for medical applications. We provide an innovative visual explanation algorithm for general purpose and as an example application we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels. This approach overcomes the drawbacks of commonly used Grad-CAM and its extended versions. The premise behind our proposed strategy is that the information flow is minimized while ensuring the classifier prediction stays similar. Our findings indicate that the bottleneck condition provides a more stable severity estimation than the similar attribution methods. The source code will be publicly available upon publication.

Transmission dynamics of SARS-CoV-2 within-host diversity in two major hospital outbreaks in South Africa.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes acute, highly transmissible respiratory infection in humans and a wide range of animal species. Its rapid global spread has resulted in a major public health emergency, necessitating commensurately rapid research to improve control strategies. In particular, the ability to effectively retrace transmission chains in outbreaks remains a major challenge, partly due to our limited understanding of the virus' underlying evolutionary dynamics within and between hosts. We used high-throughput sequencing whole-genome data coupled with bottleneck analysis to retrace the pathways of viral transmission in two nosocomial outbreaks that were previously characterised by epidemiological and phylogenetic methods. Additionally, we assessed the mutational landscape, selection pressures, and diversity at the within-host level for both outbreaks. Our findings show evidence of within-host selection and transmission of variants between samples. Both bottleneck and diversity analyses highlight within-host and consensus-level variants shared by putative source-recipient pairs in both outbreaks, suggesting that certain within-host variants in these outbreaks may have been transmitted upon infection rather than arising de novo independently within multiple hosts. Overall, our findings demonstrate the utility of combining within-host diversity and bottleneck estimations for elucidating transmission events in SARS-CoV-2 outbreaks, provide insight into the maintenance of viral genetic diversity, provide a list of candidate targets of positive selection for further investigation, and demonstrate that within-host variants can be transferred between patients. Together these results will help in developing strategies to understand the nature of transmission events and curtail the spread of SARS-CoV-2.

Population Bottlenecks and Intra-host Evolution During Human-to-Human Transmission of SARS-CoV-2.

The emergence of the novel human coronavirus, SARS-CoV-2, causes a global COVID-19 (coronavirus disease 2019) pandemic. Here, we have characterized and compared viral populations of SARS-CoV-2 among COVID-19 patients within and across households. Our work showed an active viral replication activity in the human respiratory tract and the co-existence of genetically distinct viruses within the same host. The inter-host comparison among viral populations further revealed a narrow transmission bottleneck between patients from the same households, suggesting a dominated role of stochastic dynamics in both inter-host and intra-host evolutions.

Covid-19 pandemic by the "real-time" monitoring: the Tunisian case and lessons for global epidemics in the context of 3PM strategies.

Covid-19 is neither the first nor the last viral epidemic which societies around the world are, were and will be affected by. Which lessons should be taken from the current pandemic situation? The Covid-19 disease is still not well characterised, and many research teams all over the world are working on prediction of the epidemic scenario, protective measures to populations and sub-populations, therapeutic and vaccination issues, amongst others. Contextually, countries with currently low numbers of Covid-19-infected individuals such as Tunisia are intended to take lessons from those countries which already reached the exponential phase of the infection distribution as well as from those which have the exponential phase behind them and record a minor number of new cases such as China. To this end, in Tunisia, the pandemic wave has started with a significant delay compared with Europe, the main economic partner of the country. In this paper, we do analyse the current pandemic situation in this country by studying the infection evolution and considering potential protective strategies to prevent a pandemic scenario. The model is predictive based on a large number of undetected Covid-19 cases that is particularly true for some country regions such as Sfax. Infection distribution and mortality rate analysis demonstrate a highly heterogeneous picture over the country. Qualitative and quantitative comparative analysis leads to a conclusion that the reliable "real-time" monitoring based on the randomised laboratory tests is the optimal predictive strategy to create the most effective evidence-based preventive measures. In contrast, lack of tests may lead to incorrect political decisions causing either unnecessary over-protection of the population that is risky for a long-term economic recession, or under-protection of the population leading to a post-containment pandemic rebound. Recommendations are provided in the context of advanced predictive, preventive and personalised (3P) medical approach.

Intra-Host Diversity of SARS-Cov-2 Should Not Be Neglected: Case of the State of Victoria, Australia.

Since the identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the etiological agent of the current COVID-19 pandemic, a rapid and massive effort has been made to obtain the genomic sequences of this virus to monitor (in near real time) the phylodynamic and diversity of this new pathogen. However, less attention has been given to the assessment of intra-host diversity. RNA viruses such as SARS-CoV-2 inhabit the host as a population of variants called quasispecies. We studied the quasispecies diversity in four of the main SARS-CoV-2 genes (ORF1a, ORF1b, S and N genes), using a dataset consisting of 210 next-generation sequencing (NGS) samples collected between January and early April of 2020 in the State of Victoria, Australia. We found evidence of quasispecies diversity in 68% of the samples, 76% of which was nonsynonymous variants with a higher density in the spike (S) glycoprotein and ORF1a genes. About one-third of the nonsynonymous intra-host variants were shared among the samples, suggesting host-to-host transmission. Quasispecies diversity changed over time. Phylogenetic analysis showed that some of the intra-host single-nucleotide variants (iSNVs) were restricted to specific lineages, highlighting their potential importance in the epidemiology of this virus. A greater effort must be made to determine the magnitude of the genetic bottleneck during transmission and the epidemiological and/or evolutionary factors that may play a role in the changes in the diversity of quasispecies over time.

SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2.

Face mask detection had seen significant progress in the domains of Image processing and Computer vision, since the rise of the Covid-19 pandemic. Many face detection models have been created using several algorithms and techniques. The proposed approach in this paper uses deep learning, TensorFlow, Keras, and OpenCV to detect face masks. This model can be used for safety purposes since it is very resource efficient to deploy. The SSDMNV2 approach uses Single Shot Multibox Detector as a face detector and MobilenetV2 architecture as a framework for the classifier, which is very lightweight and can even be used in embedded devices (like NVIDIA Jetson Nano, Raspberry pi) to perform real-time mask detection. The technique deployed in this paper gives us an accuracy score of 0.9264 and an F1 score of 0.93. The dataset provided in this paper, was collected from various sources, can be used by other researchers for further advanced models such as those of face recognition, facial landmarks, and facial part detection process.

Large-Scale, In-House Production of Viral Transport Media To Support SARS-CoV-2 PCR Testing in a Multihospital Health Care Network during the COVID-19 Pandemic.

The COVID-19 pandemic has severely disrupted worldwide supplies of viral transport media (VTM) due to widespread demand for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse transcription-PCR (RT-PCR) testing. In response to this ongoing shortage, we began production of VTM in-house in support of diagnostic testing in our hospital network. As our diagnostic laboratory was not equipped for reagent production, we took advantage of space and personnel that became available due to closure of the research division of our medical center. We utilized a formulation of VTM described by the CDC that was simple to produce, did not require filtration for sterilization, and used reagents that were available from commercial suppliers. Performance of VTM was evaluated by several quality assurance measures. Based on cycle threshold (CT ) values of spiking experiments, we found that our VTM supported highly consistent amplification of the SARS-CoV-2 target (coefficient of variation = 2.95%) using the Abbott RealTime SARS-CoV-2 Emergency Use Authorization (EUA) assay on the Abbott m2000 platform. VTM was also found to be compatible with multiple swab types and, based on accelerated stability studies, able to maintain functionality for at least 4 months at room temperature. We further discuss how we met logistical challenges associated with large-scale VTM production in a crisis setting, including use of a staged assembly line for VTM transport tube production.