Time-critical Decisions Research Articles

General Assembly of the Church of Scotland

In light of the COVID-19 situation, a decision was taken in March 2020 by the Assembly Business Committee to hold a Commission of the General Assembly to agree ‘straightforward, time-critical and non-controversial decisions’ – with which the Legal Questions Committee concurred under Act VI 1997.

Modeling Framework to Evaluate Vaccine Strategies against the COVID-19 Pandemic

SARS-CoV-2, with an infection fatality rate between 0.5 and 1%, has spread to all corners of the globe and infected millions of people. While vaccination is essential to protect against the virus and halt community transmission, rapidly making and delivering safe and efficacious vaccines presents unique development, manufacturing, supply chain, delivery, and post-market surveillance challenges. Despite the large number of vaccines in or entering the clinic, it is unclear how many candidates will meet regulatory requirements and which vaccine strategy will most effectively lead to sustained, population-wide immunity. Interviews with experts from biopharmaceutical companies, regulatory and multilateral organizations, non-profit foundations, and academic research groups, complemented with extensive literature review, informed the development of a framework for understanding the factors leading to population-wide immunity against SARS-CoV-2, in particular considering the role of vaccines. This paper presents a systems-level modeling framework to guide the development of analytical tools aimed at informing time-critical decisions to make vaccines globally and equitably accessible. Such a framework can be used for scenario planning and evaluating tradeoffs across access strategies. It highlights the diverse and powerful ways in which data can be used to evaluate future risks and strategically allocate limited resources.

Models to inform neutralizing antibody therapy strategies during pandemics: the case of SARS-CoV-2.

BackgroundNeutralizing antibodies (nAbs) against SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) can play an important role in reducing impacts of the COVID-19 pandemic, complementing ongoing public health efforts such as diagnostics and vaccination. Rapidly designing, manufacturing and distributing nAbs requires significant planning across the product value chain and an understanding of the opportunities, challenges and risks throughout.MethodsA systems framework comprised of four critical components is presented to aid in developing effective end-to-end nAbs strategies in the context of a pandemic: (1) product design and optimization, (2) epidemiology, (3) demand and (4) supply. Quantitative models are used to estimate product demand using available epidemiological data, simulate biomanufacturing operations from typical bioprocess parameters and calculate antibody production costs to meet clinical needs under various realistic scenarios.ResultsIn a US-based case study during the 9-month period from March 15 to December 15, 2020, the projected number of SARS-CoV-2 infections was 15.73 million. The estimated product volume needed to meet therapeutic demand for the maximum number of clinically eligible patients ranged between 6.3 and 31.5 tons for 0.5 and 2.5 g dose sizes, respectively. The relative production scale and cost needed to meet demand are calculated for different centralized and distributed manufacturing scenarios.ConclusionsMeeting demand for anti-SARS-CoV-2 nAbs requires significant manufacturing capacity and planning for appropriate administration in clinical settings. MIT Center for Biomedical Innovation’s data-driven tools presented can help inform time-critical decisions by providing insight into important operational and policy considerations for making nAbs broadly accessible, while considering time and resource constraints.

Live Tissue Training on Anesthetized Pigs for Air Ambulance Crews

ObjectivePatients with life- or limb-threatening severe injuries pose a challenge to prehospital services. Time-critical decision making and treatment are challenging because of occasional incomplete information, limited resources, adverse environments, and a range of basic and advanced technical skills available. To prepare for these infrequent critical situations, medical personnel from the helicopter emergency medical service at Oslo University Hospital developed a 1-day advanced trauma training course focusing on individual skills and teamwork during resuscitative procedures. MethodsParticipants were trained under supervision in teams on an established live tissue model with anesthetized pigs. A questionnaire-based evaluation was conducted before and after training to measure the feasibility of covering the allocated learning objectives in the time allotted and participants’ perception of any change in their skills as a result of the course. ResultsThe self-reported skill level in all learning objectives improved significantly. Combining all learning objectives, the median self-reported skill level was significantly increased from 4 to 6 points (P < .001). ConclusionExperienced prehospital physicians and other health staff reported an increased level of skill and competence in lifesaving and limb-saving procedures after completing a brief, intense 1-day course using living anesthetized pigs and cadaver models.

Analog ensemble (AE) systems for real time quantitative precipitation forecasts (QPFs) for different forecast lead times at local scale over the north-west Himalaya (NWH), India

Heavy snowfall during winter period (November to April) over the north-west Himalaya (NWH) generates many natural hazards. Time critical decisions primarily dependent on precipitation amount such as avalanche forecasting, flood forecasting, management of necessary services and supplies etc. demand real time weather forecasts, specially, quantitative precipitation forecasts (QPFs), at local scale during winter over the NWH. Two analog ensemble systems (AE1 system, AE2 system) for QPFs for varied forecast lead times are developed utilizing surface meteorological observations of 10 stations over the NWH. Performances of developed analog ensemble systems are evaluated and compared with performances of the climatological forecast models (CM1 model, CM2 model) for same forecast lead times for binary weather forecasts (precipitation day/no precipitation day) and QPFs at 10 stations over the NWH. The AE1 (AE2) system is found to perform better as compared to the CM1 (CM2) model for binary weather forecasts and QPFs at local scale over the NWH. Performance of the AE1 system for binary weather forecasts for shorter forecast lead times 0–15 h (0–15 h), 15–24 h, 24–39 h is found comparable to performance of the AE2 system for binary weather forecasts for longer forecast lead times 0–24 h, 24–48 h, 48–72 h. However, the Mean Absolute Errors (MAEs) and Root Mean Square Errors (RMSEs) for QPFs for shorter forecast lead times with the help of the AE1 system are found less as compared to the MAEs (RMSEs) for QPFs with the help of the AE2 system for longer forecast lead times. The AE1 system can provide real time QPFs based on recent surface meteorological observations for shorter lead times which can help in dynamic decision making for weather and avalanche forecasting over the NWH. However, QPFs with the help of the AE2 system can be useful for longer forecast lead times. Performances of the AE2 system and CM2 model are evaluated for binary weather forecasts and QPFs for a week period (7 days). The AE2 system is found to perform better as compared to the CM2 model for binary weather forecasts and QPFs for a week at local scale over the NWH. These results suggest that the AE2 system exhibits better consistency for QPFs as compared to the CM2 model for a week. The MAEs (RMSEs) for QPFs with the help of the AE1(AE2) system comparable to the MAEs (RMSEs) for QPFs with the help of other forecasting methods over the NWH (or elsewhere) suggest that the AE1(AE2) system exhibits good performance for real time QPFs at local scale over the NWH.

Effects of Uncertainty Visualization on Map-Based Decision Making Under Time Pressure

Most of our daily activities in a highly mobile digital society require timely spatial decision making. Much of such decision-making is supported by map displays on various devices with different modalities. Spatial information visualized on maps, however, is always subject to a multitude of uncertainties. If space-time decision-makers are not informed about potential uncertainties, misleading, or at worst, life-threatening outcomes might result from map-based decisions. Therefore, data uncertainties should be communicated to decision-makers, especially when they are made with limited time resources and when decision outcomes can have dramatic consequences. Thus, the current study investigates how data uncertainty visualized in maps might influence the process and outcomes of spatial decision making, especially when made under time pressure in risky situations. Although there is very little empirical evidence from prior uncertainty visualization research that considered decision time constraints, we hypothesized that uncertainty visualization would also have an effect on decision-making under time critical and complex decision contexts. Using a map-based helicopter landing scenario in mountainous terrain, we found that neither time pressure or uncertainty affected participants decision-making accuracy. However, uncertainty affected participants' decision strategies, and time pressure affected participants' response times. Specifically, when presented with two equally correct answers, participants avoided uncertainty more often at a cost of landing distance (an equally important decision criteria). We interpret our results as consistent with a loss-aversion heuristic and suggest implications for the study of decision-making with uncertainty visualizations.

Digital Evidence Object Model for Situation Awareness and Decision Making in Digital Forensics Investigation

The aim of a forensic investigation is to provide situation awareness in terms of identification and preservation of digital evidence, extraction of information, and analysis of extracted information to facilitate time-critical decision making. Digital forensic investigation is a process of collecting, examining, and analyzing digital data from various places such as digital devices, networks, and big data in the cloud. Here we propose a novel digital evidence object (DEO) model for the reduction of forensics data in digital forensic investigation and describe its application. The proposed DEO model is based on the synergy of category theory and integration of 5Ws (Who, What, When, Where, and Why) of digital investigation analysis techniques for digital evidence acquisition. We present a real-life case study to demonstrate its suitability for assisting computer forensics experts in the digital evidence investigation. Our results demonstrate that the application of the DEO model can noticeably decrease the number of false positive evidence objects submitted to a forensics expert, thus reducing his/her workload and improving decision making performance in a time-critical setting.

Online routing and scheduling of search-and-rescue teams

We study how to allocate and route search-and-rescue teams to areas with trapped victims in a coordinated manner after a disaster. We propose two online strategies for these time-critical decisions considering the uncertainty about the operation times required to rescue the victims and the condition of the roads that may delay the operations. First, we follow the theoretical competitive analysis approach that takes a worst-case perspective and prove lower bounds on the competitive ratio of the two variants of the defined online problem with makespan and weighted latency objectives. Then, we test the proposed online strategies and observe their good performance against the offline optimal solutions on randomly generated instances.

Bias reduction in the population size estimation of large data sets

Estimation of the population size of large data sets and hard to reach populations can be a significant problem. For example, in the military, manpower is limited and the manual processing of large data sets can be time consuming. In addition, accessing the full population of data may be restricted by factors such as cost, time, and safety. Four new population size estimators are proposed, as extensions of existing methods, and their performances are compared in terms of bias with two existing methods in the big data literature. These would be particularly beneficial in the context of time-critical decisions or actions. The comparison is based on a simulation study and the application to five real network data sets (Twitter, LiveJournal, Pokec, Youtube, Wikipedia Talk). Whilst no single estimator (out of the four proposed) generates the most accurate estimates overall, the proposed estimators are shown to produce more accurate population size estimates for small sample sizes, but in some cases show more variability than existing estimators in the literature.

Special Issue on Situation Awareness in Intelligent Human-Computer Interaction for Time Critical Decision Making

The articles in this special section focus on situation awareness in intelligent human-computer interaction for critical decision making (HCI). HCI is recognized as an ctive field that focuses on the various interactions of human with machines. The HCI has been widely applied in multiple domains, such as artificial intelligence, computer vision, image and multimedia analysis, and cognitive and behavioral sciences. The objective of the HCI is to make the computer smart via receiving enough knowledge about the environment where it is deployed and reduce the human intervention aspect toward decision making. This enables development of high-end computers that are context aware and smart in making decisions with reference to the context. Situation awareness of an intelligent HCI will decide the success and application of the solution across the real world environment. The aim of this special issue is to provide a platform on the topic of situation awareness in intelligent HCI for time critical decision making.

CYBELE – Fostering precision agriculture & livestock farming through secure access to large-scale HPC enabled virtual industrial experimentation environments fostering scalable big data analytics

According to McKinsey & Company, about a third of food produced is lost or wasted every year, amounting to a $940 billion economic hit. Inefficiencies in planting, harvesting, water use, reduced animal contributions, as well as uncertainty about weather, pests, consumer demand and other intangibles contribute to the loss. Precision Agriculture (PA) and Precision Livestock Farming (PLF) come to assist in optimizing agricultural and livestock production and minimizing the wastes and costs aforementioned. PA is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. PLF is also a technology-enabled, data-driven approach to livestock production management, which exploits technology to quantitatively measure the behavior, health and performance of animals. Big data delivered by a plethora of data sources related to these domains, has a multitude of payoffs including precision monitoring of fertilizer and fungicide levels to optimize crop yields, risk mitigation that results from monitoring when temperature and humidity levels reach dangerous levels for crops, increasing livestock production while minimizing the environmental footprint of livestock farming, ensuring high levels of welfare and health for animals, and more. By adding analytics to these sensor and image data, opportunities also exist to further optimize PA and PLF by having continuous data on how a field or the livestock is responding to a protocol. For these domains, two main challenges exist: 1) to exploit this multitude of data facilitating dedicated improvements in performance, and 2) to make available advanced infrastructure so as to harness the power of this information in order to benefit from the new insights, practices and products, efficiently time-wise, lowering responsiveness down to seconds so as to cater for time-critical decisions. The current paper aims to introduce CYBELE, a platform aspiring to safeguard that the stakeholders involved in the agri-food value chain (research community, SMEs, entrepreneurs, etc.) have integrated, unmediated access to a vast amount of very large scale datasets of diverse types and coming from a variety of sources, and that they are capable of actually generating value and extracting insights out of these data, by providing secure and unmediated access to large-scale High Performance Computing (HPC) infrastructures supporting advanced data discovery, processing, combination and visualization services, solving computationally-intensive challenges modelled as mathematical algorithms requiring very high computing power and capability.

AMRNN: attended multi-task recurrent neural networks for dynamic illness severity prediction

Illness severity prediction (ISP) is crucial for caregivers in the intensive care unit (ICU) while saving the life of patients. Existing ISP methods fail to provide sufficient evidence for the time-critical decision making in the dynamic changing environment. Moreover, the correlated temporal features in multivariate time-series are rarely be considered in existing machine learning-based ISP models. Therefore, in this paper, we propose a novel interpretable analysis framework which simultaneously analyses organ systems differentiated based on the pathological and physiological evidence to predict illness severity of patients in ICU. It not only timely but also intuitively reflects the critical conditions of patients for caregivers. In particular, we develop a deep interpretable learning model, namely AMRNN, which is based on the Multi-task RNNs and Attention Mechanism. Physiological features of each organ system in multivariate time series are learned by a single Long-Short Term Memory unit as a dedicated task. To utilize the functional and temporal relationships among organ systems, we use a shared LSTM task to exploit correlations between different learning tasks for further performance improvement. Real-world clinical datasets (MIMIC-III) are used for conducting extensive experiments, and our method is compared with the existing state-of-the-art methods. The experimental results demonstrated that our proposed approach outperforms those methods and suggests a promising way of evidence-based decision support.

How are map-based decisions influenced by uncertainty visualization in risky and time-critical situations?

How are map-based decisions influenced by uncertainty visualization in risky and time-critical situations?

Map Use in Transit Control Centers: Mapping geographic information flows

Map Use in Transit Control Centers: Mapping geographic information flows

‘Do not attempt CPR’ in the community: the experience of ambulance clinicians

Background: Ambulance clinicians must make time-critical decisions concerning treatment and resuscitation. Little is known about the impact of the presence (or absence) of do-not-attempt cardiopulmonary resuscitation (DNACPR) decision forms in the community. Aims: To investigate ambulance clinicians' experiences of DNACPR documentation and views concerning potential future changes. Methods: This multi-methods study used semi-structured face-to-face interviews (n=10) and an online questionnaire (n=123) Findings: Ambulance clinicians report that a statistically significant increase in numbers of community DNACPR forms has occurred in recent years. Most state they have not had formal DNACPR education and experience difficulties in making clinical judgments about patients at the end of life, reporting inappropriate CPR attempts and poor communication among stakeholders. Conclusion: Assessment of patients near the end of life with (and especially without) a DNACPR is challenging for ambulance clinicians. Education about resuscitation recommendations needs to be integrated into training and a national approach should be taken to decisions and their documentation.

Nested Gaussian process modeling and imputation of high-dimensional incomplete data under uncertainty

Modern healthcare systems are increasingly investing in advanced sensing and information technology, leading to data-rich environments in hospitals. For example, when patients are admitted into an intensive care unit (ICU), it is a common practice to monitor a number of clinical variables, such as blood pressure, heart rate, gas exchange, pulse oximetry and metabolic panel. However, heterogeneous sensing and measurement methods often lead to data uncertainty and incompleteness. Missing values exist pervasively for ICU clinical variables pertinent to a patient’s health condition. This adversely affects time-critical decision making in patient care. Hence, there is an urgent need to develop advanced analytical methods that address the challenges of ICU data uncertainty, provide a robust estimate of health conditions and derive in-depth knowledge for decision making from heterogeneous healthcare recordings. This article presents a novel nested Gaussian process (NGP) model that is tailored to represent multi-dimensional covariance structure of time, variable and patient for high-dimensional data imputation. We evaluate and validate the proposed NGP method on both simulation and real tensor-form ICU data with high-level missing information. Experimental results show that the proposed methodology effectively handles the data uncertainty in ICU settings, which helps further improve the biomarker extraction, patient monitoring and decision making. The proposed NGP model can also be generally applicable to a variety of engineering and medical domains that entail high-dimension data imputation and analytics.

A data-driven method for future Internet route decision modeling

Simulating the BGP routing system of Internet is crucial to the analysis of Internet backbone network routing behavior, locating network failure and, evaluating network performance for future Internet. However, the existing BGP routing model lacks in the coarse modeling granularity and the priori knowledge based model. The analysis of BGP routing data that reflects the routing behaviors, directly impacts the BGP routing decision and forward strategy. The efficiency of such analysis dictates the time it takes to come up with such a time-critical decision and strategy. Under the existing model, BGP routing data analysis does not scale up.In this paper, we analyze the inter-domain routing decision making process, then present a prefix level route decision prediction model. More specifically, we apply deep learning methods to build a high-precision BGP route decision process model. Our model handles as much available routing data as possible to promote the prediction accuracy. It analyzes the routing behaviors without any prior knowledge. Beyond discussing the characteristics of the model, we also evaluate the proposed model using experiments explained in detailed cases. For the research community, our method could help in detecting routing dynamics and route anomalies for routing behavior analysis.

Situation awareness in intelligent human computer interaction for time critical decision making

Situation awareness in intelligent human computer interaction for time critical decision making

Comparative analysis of real-time messages in big data pipeline architecture

Nowadays, real-time messaging system is the essential thing in enabling time-critical decision making in many applications where it is important to deal with real-time requirements and reliability requirements simultaneously. For dependability reasons, we intend to maximise the reliability requirement of the real-time messaging system. To develop a real-time messaging system, we create real-time big data pipeline by using Apache Kafka and Apache Storm. This paper focuses on analysing the performance of producer and consumer in Apache Kafka processing. Apache Kafka is the most popular framework used to ingest the data streams into the processing platforms. The comparative analysis of Kafka processing is more efficient to get reliable data on the pipeline architecture. Then, the experiment will be conducted the processing time in the performance of the producer and consumer on various partitions and many servers. The performance analysis of Kafka can impact on messaging systems in real-time big data pipeline architecture.

Comparative analysis of real-time messages in big data pipeline architecture

Nowadays, real-time messaging system is the essential thing in enabling time-critical decision making in many applications where it is important to deal with real-time requirements and reliability requirements simultaneously. For dependability reasons, we intend to maximise the reliability requirement of the real-time messaging system. To develop a real-time messaging system, we create real-time big data pipeline by using Apache Kafka and Apache Storm. This paper focuses on analysing the performance of producer and consumer in Apache Kafka processing. Apache Kafka is the most popular framework used to ingest the data streams into the processing platforms. The comparative analysis of Kafka processing is more efficient to get reliable data on the pipeline architecture. Then, the experiment will be conducted the processing time in the performance of the producer and consumer on various partitions and many servers. The performance analysis of Kafka can impact on messaging systems in real-time big data pipeline architecture.