The value of probabilistic forecasting in emergency medical resource planning under uncertainty

PurposeMost epidemic transmission forecasting methods can only provide deterministic outputs. This study aims to show that probabilistic forecasting, in contrast, is suitable for stochastic demand modeling and emergency medical resource planning under uncertainty.Design/methodology/approachTwo probabilistic forecasting methods, i.e. quantile regression convolutional neural network and kernel density estimation, are combined to provide the conditional quantiles and conditional densities of infected populations. The value of probabilistic forecasting in improving decision performances and controlling decision risks is investigated by an empirical study on the emergency medical resource planning for the COVID-19 pandemic.FindingsThe managerial implications obtained from the empirical results include (1) the optimization models using the conditional quantile or the point forecasting result obtain better results than those using the conditional density;(2) for sufficient resources, decision-makers' risk preferences can be incorporated to make tradeoffs between the possible surpluses and shortages of resources in the emergency medical resource planning at different quantile levels;and (3) for scarce resources, the differences in emergency medical resource planning at different quantile levels greatly decrease or disappear because of the existing of forecasting errors and supply quantity constraints.Originality/valueVery few studies concern probabilistic epidemic transmission forecasting methods, and this is the first attempt to incorporate deep learning methods into a two-phase framework for data-driven emergency medical resource planning under uncertainty. Moreover, the findings from the empirical results are valuable to select a suitable forecasting method and design an efficient emergency medical resource plan.

Videos and podcasts for delivering agricultural extension: achieving credibility, relevance, legitimacy and accessibility

PurposeTo explore the perceived credibility, relevance, legitimacy and accessibility of videos and podcasts in farm extension.MethodsA two-phase mixed methods approach consisting of a pre-COVID online survey of farmers (n = 221), farmer telephone interviews (n = 60) and in-person focus groups of farmers (n = 4) followed by an analysis of how viewers interact with Agricology videos and podcasts, a further online survey (n = 141) and online farmer focus groups (n = 4) during the COVID-19 pandemic.FindingsIf they are to be perceived as effective extension methods, videos should be short, concise, practical, advert-free and visualise how to implement a practice. Podcasts can be longer, more detailed, and allow multitasking. Both should use farmer-friendly language, be easily accessible, high quality, non-biased, and be created by someone whom farmers respect.Practical implicationsHelps policy-makers and extensionists understand the potential of videos and podcasts and the trade-offs in using them with other forms of extension. The findings are also of use to global advisory services seeking to offer hybridised advice as a result of the ongoing COVID pandemic.Theoretical implicationsElucidates the trade-offs of using videos and podcasts when face-to-face extension is not possible and develops the CRELE framework.OriginalityDiscusses the role of podcasts in farm extension and re-evaluates the role of videos when face-to-face extension is impossible.

Work-in-Progress: DIY Ventilator - A CoVID-19 Action

In the Covid-19 pandemic, ventilators became scarce, especially in countries with lower medical standards. A special project has managed to develop a complete ventilator as a kit using cheap parts and 3D printers. In the first two phases of the project a demonstrator has been developed which allows first tests. With hands-on experiments, one can already control the ventilator together with an artificial lung and learn how the ventilator works and what types of pumps, sensors, and electronic components are necessary for this. Due to its multidisciplinary character this project provides an excellent basis for the elaboration of educational and instructional material. Disciplines addressed comprise among others: informatics, physics, mathematics, biochemistry, medicine, material science, and electronics. In addition, the use of tools is demonstrated on a wide range covering 3D printing, IDEs for software development, wiring and soldering, etc. The knowledge gained through this project could be used for setting up a startup enterprise or as reference when starting research in the Edge Computing or IoT domain. The education and training will be carried out in small groups (four persons ideally) using a system for blended learning. It is envisaged to use the Mediathread Development [1] from the Columbia University which provides an excellent framework for the flipped classroom approach. Besides the academic domain also industries are the target for using the educational material which will be delivered by the project. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Stress-Coping Tweets Acquisition: A Two-phase Bootstrapping Method on Patterns and Semantic Features

Stress is integral to biological survival. However, without an appropriate coping response, high stress levels and long-term stressful situations may lead to negative mental health outcomes. Since the COVID-19 pandemic, remote assessment of mental health has become imperative. The majority of past studies focused on detecting users' stress levels rather than coping responses using social media. Because of the diversity of human expression and because people do not usually express stress and the corresponding coping response simultaneously, it is challenging to extract users' tweets about their coping responses to stressful events from their daily tweets. Consequently, there are two goals being pursued in this study: to anchor users' stress statuses and to detect their stress responses based on the existing stressful conditions. In order to accomplish these goals, we propose a framework that consists of two phases: the construction of stress dataset and the extraction of coping responses. Since the stressed users' data are lacking, the first phase is to construct a stress dataset based on stress-related hashtags, personal pronouns, and emotion recognition. In addition, to ensure the collection of enough tweets to observe the coping responses of stressed users, we broadened the survey's scope by collecting all tweets from the same user. In the second phase, stress-coping tweets were extracted by utilizing bootstrapping-based patterns and semantic features. The bootstrapping method was used to enrich word patterns for text expression and the semantic feature to assess the meaning of sentences. The collected data included the tweets of the stressed users identified in Phase 1 and the various coping responses from Phase 2 can contribute to developing a tool for the remote assessment of mental health. The experimental results show that our two-phase method outperforms the baseline and can help improve the efficiency of extracting stress-coping tweets. © 2022 IEEE.

Two-phase COVID-19 medical waste transport optimisation considering sustainability and infection probability.

A safe and effective medical waste transport network is beneficial to control the COVID-19 pandemic and at least decelerate the spread of novel coronavirus. Seldom studies concentrated on a two-phase COVID-19 medical waste transport in the presence of multi-type vehicle selection, sustainability, and infection probability, which is the focus of this paper. This paper aims to identify the priority of sustainable objectives and observe the impacts of multi-phase and infection probability on the results. Thus, such a problem is formulated as a mixed-integer programming model to minimise total potential infection risks, minimise total environmental risks, and maximise total economic benefits. Then, a hybrid solution strategy is designed, incorporating a lexicographic optimisation approach and a linear weighted sum method. A real-world case study from Chongqing is used to illustrate this methodology. Results indicate that the solution strategy guides a good COVID-19 medical waste transport scheme within 1 min. The priority of sustainable objectives is society, economy, and environment in the first and second phases because the total Gap of case No.35 is 3.20%. A decentralised decision mode is preferred to design a COVID-19 medical waste transport network at the province level. Whatever the infection probability is, infection risk is the most critical concern in the COVID-19 medical waste clean-up activities. Environmental and economic sustainability performance also should be considered when infection probability is more than a certain threshold.

STUDY of ONLINE SHOPPING BEHAVIOR DIFFERENCES between GOAL-DIRECTED SEARCH and EXPLORATORY BROWSING in INTERMEDIATE CHOICE LIST on E-COMMERCE

With the development of the internet and shipping, and also the appearance of COVID-19, the growth of E-commerce is rising in a fast way. People change their shopping behavior from offline to online. It means there is a significant change in customers' shopping process. Lots of scholars did research about it, such as from AIDMA [1] to AISAS [2], and also from Buyer Decision Process [3] to Dynamic Buyer Decision Process [4]. Both of them talk about how customers change their behavior and process of shopping in the digital generation. So it's necessary to know clearly how customers do and think to make the serve, user interface, and user experience in websites better. But the study found that most of those existing models are for "goal-directed search"shopping (shopping with purpose). But in fact, "exploratory browsing"shopping is rising these days, it's better to know more about it, so this study focuses on the process of how people do online shopping in "goal-directed search"shopping and "exploratory browsing"shopping. Besides, this study also found because there is too much information on the internet now, people will make the two phases of the decision in the process of online shopping [5], and they need to mark and record some product information in the middle of these two phases. Hence, they need a space to save this information is called "Intermediate Choice List(ICL)"[6]. Therefore, this study also explored how ICL takes a hand in the whole online shopping process. At the end of the study built a complete shopping process model for future use in E-commerce website design. © 2022 ACM.

Perceptions of and Experiences with Consumer Sleep Technologies That Use Artificial Intelligence

This study aims to assess the perspectives and usability of different consumer sleep technologies (CSTs) that leverage artificial intelligence (AI). We answer the following research questions: (1) what are user perceptions and ideations of CSTs (phase 1), (2) what are the users’ actual experiences with CSTs (phase 2), (3) and what are the design recommendations from participants (phases 1 and 2)? In this two-phase qualitative study, we conducted focus groups and usability testing to describe user ideations of desires and experiences with different AI sleep technologies and identify ways to improve the technologies. Results showed that focus group participants prioritized comfort, actionable feedback, and ease of use. Participants desired customized suggestions about their habitual sleeping environments and were interested in CSTs+AI that could integrate with tools and CSTs they already use. Usability study participants felt CSTs+AI provided an accurate picture of the quantity and quality of sleep. Participants identified room for improvement in usability, accuracy, and design of the technologies. We conclude that CSTs can be a valuable, affordable, and convenient tool for people who have issues or concerns with sleep and want more information. They provide objective data that can be discussed with clinicians.

A Facile Single-Phase-Fluid-Driven Bubble Microfluidic Generator for Potential Detection of Viruses Suspended in Air

Microfluidics devices have widely been employed to prepare monodispersed microbubbles/droplets, which have promising applications in biomedical engineering, biosensor detection, drug delivery, etc. However, the current reported microfluidic devices need to control at least two-phase fluids to make microbubbles/droplets. Additionally, it seems to be difficult to make monodispersed microbubbles from the ambient air using currently reported microfluidic structures. Here, we present a facile approach to making monodispersed microbubbles directly from the ambient air by driving single-phase fluid. The reported single-phase-fluid microfluidic (SPFM) device has a typical co-flow structure, while the adjacent space between the injection tube and the collection tube is open to the air. The flow condition inside the SPFM device was systematically studied. By adjusting the flow rate of the single-phase fluid, bubbles were generated, the sizes of which could be tuned precisely. This facile bubble generator may have significant potential as a detection sensor in detecting viruses in spread droplets or haze particles in ambient air.

Multiphase large-eddy simulations of human cough jet development and expiratory droplet dispersion

Violent respiratory events play critical roles in the transmission of respiratory diseases, such as coughing and sneezing, between infectious and susceptible individuals. In this work, large-scale multiphase flow large-eddy simulations have been performed to simulate the coughing jet from a human's mouth carrying pathogenic or virus-laden droplets by using a weakly compressible smoothed particle hydrodynamics method. We explicitly model the cough jet ejected from a human mouth in the form of a mixture of two-phase fluids based on the cough velocity profile of the exhalation flow obtained from experimental data and the statistics of the droplets’ sizes. The coupling and interaction between the two expiratory phases and ambient surrounding air are examined based on the interaction between the gas particles and droplet particles. First, the results reveal that the turbulence of the cough jet determines the dispersion of the virus-laden droplets, i.e. whether they fly up evolving into aerosols or fall down to the ground. Second, the droplet particles have significant effects on the evolution of the cough jet turbulence;for example, they increase the complexity and butterfly effect introduced by the turbulence disturbance. Our results show that the prediction of the spreading distance of droplet particles often goes beyond the social distancing rules recommended by the World Health Organization, which reminds us of the risks of exposure if we do not take any protecting protocol.

Challenges and Lessons Learnt Operating During Covid19 Pandemic in Remote Location: A Success Story of Mauritania Deepwater Abandonment and Decommissioning Campaign

Operator's Wells Abandonment & Decommissioning campaign consists of 15 Deepwater subsea wells in Field "C" offshore West Africa. Discovered in 2001, the field is located approximately 80 km west of coastline and about 90 km from Nouakchott, capital of Mauritania. Field "C" is a deepwater field in water depth ranging from 730m to 830m. The field was developed using subsea wells, Hinged Over Subsea Templates (HOST), manifolds, flexible flowlines, umbilicals, and risers tied back to a permanently moored FPSO. In total, the field consists of nine (9) oil producer wells and five (5) water injection wells. During the development stage, one (1) gas injection well was drilled and completed at adjacent Field "B" about 17 km Northeast of Field "C". The water depth at this gas injection well location is approximately 280m. Field "C" reached maturity in 2016. Due to high operating costs, declining production coupled with declining oil prices, the decision was made to cease production, plug and abandon (P&A) and decommission the field. Two phases strategy was engaged by the Operator in order to complete the decommissioning and abandonment of Field "C". In Phase 1, which was executed back in the year of 2017-2018, all the 15 deep water subsea wells were temporarily suspended with two (2) barriers in place. The Floating, Production, Storage and Offloading (FPSO) unit was decommissioned and disconnected. In line with the strategy of dividing the project into two phases, the information on well integrity and conditions acquired during the Phase 1 Temporary Wells Suspension (TWS) was used by the Operator in planning for Phase 2 - Wells Plug and Abandonment (P&A). The operator made full use of temporary well suspension period between Phase 1 and Phase 2 for engineering, procurement, and operations preparation. The same drillship was utilized for the project in both phases. Multiple optimizations and modifications were done on the drillship based on lessons learned in Phase 1 and to cater for the subsea Xmas Tree and subsea structures retrieval in Phase 2. Due to the nature of the remote location and no existing oil & gas operations support base, all equipment required in this project was sent to Mauritania early. Equipment inspection and acceptance were carried out in Mauritania as part of the strategy in ensuring the availability of good quality equipment for offshore operations. The operations on Wells Plug & Abandonment commenced in December 2019. In March 2020, upon declaration of the COVID-19 pandemic, operator was faced with difficulty of continuing operation as the Host Country activated border lockdown. The operator managed to continue operations for remaining well and demobilized drillship and personnel safely. Operator has successfully retrieved three (3) subsea Xmas Trees, P&A three (3) wells and intervened six (6) other wells for tubing cutting before operations was suspended due to COVID-19 pandemic. Operator used the suspension phase to devise a methodology to resume operation in the prevailing COVID-19 pandemic situation. The challenges faced during the COVID-19 pandemic as well as the steps taken for resumption are highlighted in this paper. It is expected that this paper will serve as guidance in highlighting challenges and efforts taken to resume operation in the event of unforeseen suspension due to any reasons. It is also hoped that the details shared in this paper can assist other Operators in better operation planning for remote locations. Copyright © 2021, Society of Petroleum Engineers

Design and Implementation of a Monitoring System for COVID-19-Free Working Environment

The proposed work aims to design a monitoring system that ensures COVID-free safety working environment. The proposed system has two phases. The first phase is used to monitor the vital parameters of the person entering the workplace relevant to COVID-19 test. The second phase monitors the indoor safety measures to create COVID-19-free working space. It is an inexpensive solution that aims increased COVID-19 indoor safety, with certain aspects covered like contactless sensing of temperature, heartrate monitoring, detection of mask, social distancing check, monitoring air quality, temperature, and humidity of the room. Contactless temperature sensing and pulse checking are carried out using IR sensor and heartrate sensor interfaced with Arduino Uno. The detection of mask and proper social distancing check is done by using Open CV techniques with Raspberry Pi which is equipped with a camera. The monitoring system ensures COVID-19-free safe working environment to the society. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Modelling Deprivation Level and Multimorbidity in a Health District

Deprivation is associated with an increased risk of developing chronic health conditions and with worse outcomes in multimorbidity. The goal of our study was to develop an integrated population index of deprivation (IPID) to observe the influence of deprivation on morbidity and the subsequent use of healthcare resources in one health district, using the socioeconomic, clinical and geographical data from its administrative health records. Eight socioeconomic indicators were identified and weighted using the methodology of two-phase principal component analysis, providing an index that allowed each census section to be classified into seven deprivation groups. Secondly, the possible relation between the IPID and the variables for multimorbidity and healthcare resources was analysed using the theory of multiple comparisons. It was observed that places with a greater proportion of healthy people presented lower values of deprivation and that, at lower levels of deprivation, there were fewer hospital admissions. The results show that living in an area with a higher deprivation index is associated with greater consumption of healthcare resources and disease burden. Identifying areas of sociosanitary vulnerability can help to identify health inequalities and allow intervention by clinical practices and healthcare management to reduce them.

'Everyone seems to be agreeing at the minute that face-to-face is the way forward': Practitioners' perspectives on post-pandemic mathematics and statistics support

Mathematics and statistics support (MSS) is now firmly embedded in the learning and teaching infra-structure of most UK universities and in many universities worldwide. In common with other higher education activities, in response to restrictions put in place to reduce the spread of Covid-19, MSS transitioned rapidly to online delivery in spring 2020. This paper reports on thinking within the practitioner community about good practice in the delivery of online MSS. A two-phase approach was used to collect this shared wisdom: an initial questionnaire in May 2020 (just after provision had moved online) and interviews with practitioners in January/February 2021 after colleagues had some experience in online provision and had had the opportunity to reflect on and modify the measures hastily put in place in spring 2020. The focus of the study is not only on what is currently being provided but also on what MSS will look like once all pandemic related restrictions have been ended. The overall feeling of the participants is that face-to-face MSS will return as the dominant form of delivery but that the benefits of online provision are such that a significant minority of provision will remain in this form. ©

A Two-Phase ASP Encoding for Solving Rehabilitation Scheduling

The rehabilitation scheduling process consists of planning rehabilitation physiotherapy sessions for patients, by assigning proper operators to them in a certain time slot of a given day, taking into account several requirements and optimizations, e.g., patient’s preferences and operator’s work balancing. Being able to efficiently solve such problem is of upmost importance, in particular after the COVID-19 pandemic that significantly increased rehabilitation’s needs. In this paper, we present a solution to rehabilitation scheduling based on Answer Set Programming (ASP), which proved to be an effective tool for solving practical scheduling problems. Results of experiments performed on both synthetic and real benchmarks, the latter provided by ICS Maugeri, show the effectiveness of our solution. © 2021, Springer Nature Switzerland AG.

Application of multiphase flow and droplet separation theory in modeling cough droplets contamination range to mitigate COVID-19 transmission: Do not stand too close to me!

On March 11, 2020, the World Health Organization (WHO) declared that COVID-19 is a pandemic, warning the world of a health catastrophe and social, economic, and political disruptions. According to WHO, COVID-19 is transmitted by the transport of respiratory droplets generated by a violent respiratory event such as sneeze and cough directly to susceptible persons, or indirectly through surfaces. The aim of this study is to propose simple physical and mathematical models based on two-phase flow dynamics and droplet separation theory. The proposed mathematical model predicts the contamination range of ejected cough droplets, estimating the safe person-to-person social distance. As a result, the proposed simple model predicted a contamination range of 2.3 m for a male adult. In addition, to understand the behavior of ejected cough droplets, a sensitivity analysis is carried out to investigate the effect on contamination range of cough air flowrate, i.e., body/lung size, droplet size, and droplet drag coefficient. It is found that as the body/lung size decreases, i.e., lower cough flow rate, contamination range decreases, resulting in 1.9 m for an adult female, and 1.4 m for a child. In addition, the model predictions show an appreciable effect of droplet size, and droplet drag coefficient on cough contamination range. In particular, the effect of droplet drag coefficient is of interest, because of its relationship to ambient conditions such as temperature and relative humidity, in which both affect ambient air viscosity, and thus drag coefficient. This is important in investigating the contamination range and person-to-person social-distance as climate changes. © 2021 University of Kuwait. All rights reserved.

Initial Screening of Poly(ethylene glycol) Amino Ligands for Affinity Purification of Plasmid DNA in Aqueous Two-Phase Systems.

Gene therapy and DNA vaccination are among the most expected biotechnological and medical advances for the coming years. However, the lack of cost-effective large-scale production and purification of pharmaceutical-grade plasmid DNA (pDNA) still hampers their wide application. Downstream processing, which is mainly chromatography-based, of pDNA remains the key manufacturing step. Despite its high resolution, the scaling-up of chromatography is usually difficult and presents low capacity, resulting in low yields. Alternative methods that are based on aqueous two-phase systems (ATPSs) have been studied. Although higher yields may be obtained, its selectivity is often low. In this work, modified polymers based on poly(ethylene glycol) (PEG) derivatisation with amino groups (PEG-amine) or conjugation with positively charged amino acids (PEG-lysine, PEG-arginine, and PEG-histidine) were studied to increase the selectivity of PEG-dextran systems towards the partition of a model plasmid. A two-step strategy was employed to obtain suitable pure formulations of pDNA. In the first step, a PEG-dextran system with the addition of the affinity ligand was used with the recovery of the pDNA in the PEG-rich phase. Then, the pDNA was re-extracted to an ammonium-sulphate-rich phase in the second step. After removing the salt, this method yielded a purified preparation of pDNA without RNA and protein contamination.

Rapid and inexpensive purification of adenovirus vectors using an optimised aqueous two-phase technology.

Adenoviruses (AdVs) are used as gene therapy vectors to treat human diseases and as vaccines against COVID-19. AdVs are produced by transfecting human embryonic kidney 239 (HEK293) or PER.C6 virus producer cells with AdV plasmid vectors or infecting these cells withcell lysates containing replication-defective AdV. Cell lysates can be purified further by caesium chloride or chromatographic protocols to research virus seed stocks (RVSS) for characterisation to high quality master virus seed stocks (MVSS) and working virus seed stocks (WVSS) before downstream production of pure, high titre AdV. Lysates are poorly infectious, block filtration columns and have limited storage capability. Aqueous two-phase systems (ATPS) are an alternative method for AdV purification that rapidly generates cleaner RVSS for characterisation to MVSS. After testing multiple ATPS formulations, an aqueous mixture of 20 % PEG 600 and 20 % (NH4)2SO4 (w/w) was found most effective for AdV partitioning, producing up to 97+3% yield of high-titre virus that was devoid of aggregates both effective in vitro and in vivo with no observable cytotoxicity. Importantly, AdV preparations stored at -20 °C or 4 °C show negligible loss of titre and are suitable for downstream processing to clinical grade to support the need for AdV vaccines.

Inferring statistical trends of the COVID19 pandemic from current data. Where probability meets fuzziness.

We introduce unprecedented tools to infer approximate evolution features of the COVID19 outbreak when these features are altered by containment measures. In this framework we present: (1) a basic tool to deal with samples that are both truncated and non independently drawn, and (2) a two-phase random variable to capture a game changer along a process evolution. To overcome these challenges we lie in an intermediate domain between probability models and fuzzy sets, still maintaining probabilistic features of the employed statistics as the reference KPI of the tools. This research uses as a benchmark the daily cumulative death numbers of COVID19 in two countries, with no any ancillary data. Numerical results show: (i) the model capability of capturing the inflection point and forecasting the end-of-infection time and related outbreak size, and (ii) the out-performance of the model inference method according to conventional indicators.

A two-phase dynamic contagion model for COVID-19.

In this paper, we propose a continuous-time stochastic intensity model, namely, two-phase dynamic contagion process (2P-DCP), for modelling the epidemic contagion of COVID-19 and investigating the lockdown effect based on the dynamic contagion model introduced by Dassios and Zhao [24]. It allows randomness to the infectivity of individuals rather than a constant reproduction number as assumed by standard models. Key epidemiological quantities, such as the distribution of final epidemic size and expected epidemic duration, are derived and estimated based on real data for various regions and countries. The associated time lag of the effect of intervention in each country or region is estimated. Our results are consistent with the incubation time of COVID-19 found by recent medical study. We demonstrate that our model could potentially be a valuable tool in the modeling of COVID-19. More importantly, the proposed model of 2P-DCP could also be used as an important tool in epidemiological modelling as this type of contagion models with very simple structures is adequate to describe the evolution of regional epidemic and worldwide pandemic.

Aerosol deposition and airflow dynamics in healthy and asthmatic human airways during inhalation.

Inhalation of aerosols such as pharmaceutical aerosols or virus aerosol uptake is of great concern to the human population. To elucidate the underlying aerosol dynamics, the deposition fractions (DFs) of aerosols in healthy and asthmatic human airways of generations 13-15 are predicted. The Navier-stokes equations governing the gaseous phase and the discrete phase model for particles' motion are solved using numerical methods. The main forces responsible for deposition are inertial impaction forces and complex secondary flow velocities. The curvatures and sinusoidal folds in the asthmatic geometry lead to the formation of complex secondary flows and hence higher DFs. The intensities of complex secondary flows are strongest at the generations affected by asthma. The DF in the healthy airways is 0%, and it ranges from 1.69% to 52.93% in the asthmatic ones. From this study, the effects of the pharmaceutical aerosol particle diameters in the treatment of asthma patients can be established, which is conducive to inhibiting the inflammation of asthma airways. Furthermore, with the recent development of COVID-19 which causes pneumonia, the predicted physics and effective simulation methods of bioaerosols delivery to asthma patients are vital to prevent the exacerbation of the chronic ailment and the epidemic.