Burden of Traumatic Brain Injuries in Children and Adolescents in Europe: Hospital Discharges, Deaths and Years of Life Lost

Children and adolescents are at high risk of traumatic brain injuries (TBI). To identify those most at risk across Europe, a comprehensive epidemiological study on the burden of TBI is needed. Our aim was to estimate the burden of TBI in the pediatric and adolescent population of Europe by calculating rates of hospital-based incidence, death and years of life lost (YLL) due to TBI in 33 countries of Europe in 2014 (most recent available data). We conducted a cross-sectional observational, population-based study. All cases with TBI in the age range 0 to 19, registered in the causes of death databases or hospital discharge databases of 33 European countries were included. Crude and age-standardized rates of hospital discharges, deaths and YLLs due to TBI; and pooled estimates for all countries combined were calculated. TBI caused 2303 deaths (71% in boys), 154,282 YLLs (68% in boys) and 441,368 hospital discharges (61% in boys) in the population of 0–19 year-olds. We estimated pooled age-standardized rates of death (2.8, 95% CI: 2.4–3.3), YLLs (184.4, 95% CI: 151.6–217.2) and hospital discharges (344.6, 95% CI: 250.3–438.9) for the analyzed countries in 2014. The population of 15–19 year-olds had the highest rates of deaths and YLLs, and the population of 0–4 year-olds had the highest rate of hospital discharges. Detailed estimates of hospital discharge, death and YLL rates based on high-quality, standardized data may be used to develop health policies, aid decision-making and plan prevention.

MI3: Machine-initiated Intelligent Interaction for Interactive Classification and Data Reconstruction

In many applications, while machine learning (ML) can be used to derive algorithmic models to aid decision processes, it is often difficult to learn a precise model when the number of similar data points is limited. One example of such applications is data reconstruction from historical visualizations, many of which encode precious data, but their numerical records are lost. On the one hand, there is not enough similar data for training an ML model. On the other hand, manual reconstruction of the data is both tedious and arduous. Hence, a desirable approach is to train an ML model dynamically using interactive classification, and hopefully, after some training, the model can complete the data reconstruction tasks with less human interference. For this approach to be effective, the number of annotated data objects used for training the ML model should be as small as possible, while the number of data objects to be reconstructed automatically should be as large as possible. In this article, we present a novel technique for the machine to initiate intelligent interactions to reduce the user’s interaction cost in interactive classification tasks. The technique of machine-initiated intelligent interaction (MI3) builds on a generic framework featuring active sampling and default labeling. To demonstrate the MI3 approach, we use the well-known cholera map visualization by John Snow as an example, as it features three instances of MI3 pipelines. The experiment has confirmed the merits of the MI3 approach.

The Contribution of MCDM to SUMP: The Case of Spanish Cities during 2006–2021

Sustainable Urban Mobility Plans (SUMP) are increasingly popular planning tools in cities with environmental issues where numerous actions are usually proposed to reduce pollution from urban transport. However, the diagnosis and implementation of these processes requires broad consensus from all stakeholders and the ability to fit them into urban planning in such a way that it allows the proposals to become realistic actions. In this study, a review of the sustainable urban mobility plans of 47 cities in Spain during the last 15 years has been carried out, analyzing both the diagnosis and proposal of solutions and their subsequent implementation. From the results obtained, a new framework based on a structured hybrid methodology is proposed to aid decision-making for the evaluation of alternatives in the implementation of proposals in SUMP. This hybrid methodology considers experts’ and stakeholders’ opinion and applies two different multi-criteria decision making (MCDM) methods in different phases to present two rankings of best alternatives. From that experience, an analysis based on the MCDM methods called ‘Sequential Interactive Modelling for Urban Systems (SIMUS)’ and weighted sum method (WSM) was applied to a case study of the city of Cartagena, a southeastern middle-size city in Spain. This analytic proposal has been transferred to the practical field in the SUMP of Cartagena, the first instrument of this nature developed after COVID-19 in Spain for a relevant city. The results show how this framework, based on a hybrid methodology, allows the development of complex decision mapping processes using these instruments without obviating the need to generate planning tools that can be transferred from the theoretical framework of urban reality.

Geographical Landscape and Transmission Dynamics of SARS-CoV-2 Variants Across India: A Longitudinal Perspective

Globally, SARS-CoV-2 has moved from one tide to another with ebbs in between. Genomic surveillance has greatly aided the detection and tracking of the virus and the identification of the variants of concern (VOC). The knowledge and understanding from genomic surveillance is important for a populous country like India for public health and healthcare officials for advance planning. An integrative analysis of the publicly available datasets in GISAID from India reveals the differential distribution of clades, lineages, gender, and age over a year (Apr 2020–Mar 2021). The significant insights include the early evidence towards B.1.617 and B.1.1.7 lineages in the specific states of India. Pan-India longitudinal data highlighted that B.1.36* was the predominant clade in India until January–February 2021 after which it has gradually been replaced by the B.1.617.1 lineage, from December 2020 onward. Regional analysis of the spread of SARS-CoV-2 indicated that B.1.617.3 was first seen in India in the month of October in the state of Maharashtra, while the now most prevalent strain B.1.617.2 was first seen in Bihar and subsequently spread to the states of Maharashtra, Gujarat, and West Bengal. To enable a real time understanding of the transmission and evolution of the SARS-CoV-2 genomes, we built a transmission map available on https://covid19-indiana.soic.iupui.edu/India/EmergingLineages/April2020/to/March2021. Based on our analysis, the rate estimate for divergence in our dataset was 9.48 e-4 substitutions per site/year for SARS-CoV-2. This would enable pandemic preparedness with the addition of future sequencing data from India available in the public repositories for tracking and monitoring the VOCs and variants of interest (VOI). This would help aid decision making from the public health perspective.

Uncertainty in Drought Identification Due to Data Choices, and The Value of Triangulation

Droughts are complex and gradually evolving conditions of extreme water deficits which can compromise livelihoods and ecological integrity, especially in fragile arid and semi-arid regions that depend on rainfed farming, such as Kitui West in south-eastern Kenya. Against the background of low ground-station density, 10 gridded rainfall products and four gridded temperature products were used to generate an ensemble of 40 calculations of the Standardized Precipitation Evapotranspiration Index (SPEI) to assess uncertainties in the onset, duration, and magnitude of past droughts. These uncertainties were driven more by variations between the rainfall products than variations between the temperature products. Remaining ambiguities in drought occurrence could be resolved by complementing the quantitative analysis with ground-based information from key informants engaged in disaster relief, effectively formulating an ensemble approach to SPEI-based drought identification to aid decision making. The reported trend towards drier conditions in Eastern Africa was confirmed for Kitui West by the majority of data products, whereby the rainfall effect on those increasingly dry conditions was subtler than just annual and seasonal declines and greater annual variation of rainfall, which requires further investigation. Nevertheless, the effects of increasing droughts are already felt on the ground and warrant decisive action.

The Demand for, and Avoidance of, Information

Management scientists recognize that decision making depends on the information people have but lack a unified behavioral theory of the demand for (and avoidance of) information. Drawing on an existing theoretical framework in which utility depends on beliefs and the attention paid to them, we develop and test a theory of the demand for information encompassing instrumental considerations, curiosity, and desire to direct attention to beliefs one feels good about. We decompose an individual’s demand for information into the desire to refine beliefs, holding attention constant, and the desire to focus attention on anticipated beliefs, holding these beliefs constant. Because the utility of resolving uncertainty (i.e., refining beliefs) depends on the attention paid to it and more important or salient questions capture more attention, demand for information depends on the importance and salience of the question(s) it addresses. In addition, because getting new information focuses attention on one’s beliefs and people want to savor good news and ignore bad news, the desire to obtain or avoid information depends on the valence (i.e., goodness or badness) of anticipated beliefs. Five experiments (n = 2,361) test and find support for these hypotheses, looking at neutrally valenced as well as ego-relevant information. People are indeed more inclined to acquire information (a) when it feels more important, even if it cannot aid decision making (Experiments 1A and 2A); (b) when a question is more salient, manipulated through time lag (Experiments 1B and 2B); and (c) when anticipated beliefs have higher valence (Experiment 2C). This paper was accepted by Yan Chen, behavioral economics and decision analysis.

Sentiment Prediction of Geotagged Tweets During Lockdown and Unlock Phases in India: A Deep Learning Based Approach

India is a hotspot of the COVID-19 crisis. During the first wave several lockdowns (L) and gradual unlock (UL) phases were implemented by the Government of India (GOI) to curb the virus spread. Twitter, a social media platform, was extensively used by citizens to react to various events and topics related to resource management and virus spread that varied geographically. This paper attempts to capture those variations by analyzing the sentiments of geotagged tweets during L and UL phases, which remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings can aid decision-makers in analyzing citizen reactions toward the resources and events during an ongoing pandemic, which can result in better resource planning.

Unpacking the WEF Nexus Index: A Regional and Sub-Regional Analysis of Northern Canada

The water–energy–food (WEF) nexus has emerged as a leading tool for assessing integrated resource management strategies and for monitoring progress towards the WEF-related Sustainable Development Goals. A notable outcome of WEF nexus research has been the calculation of the global WEF Nexus Index, which provides a quantitative ranking of country-level WEF security for 170 nations. As valuable as this ranking is, the aggregation of country-level WEF data obscures regional differences, particularly in remote regions that are sparsely populated and differ in geography, economy, and climate. This has proven to be the case for northern Canada, which despite representing 40% of Canada’s total land area, accounts for less than 1% of the Canadian population, most of whom are Indigenous. Whereas Canada ranks 5th globally in their WEF security, northern Canada, if treated independently, would rank 67th on the global WEF Nexus Index rankings. Evaluating each WEF sector independently, northern Canada would rank 22nd in water security, 90th in energy security, and 113th in food security. Our results further reveal that considerable inter-regional variability exists between northern territories and provinces, where Nunavik would rank 54th, Northwest Territories 67th, Yukon 69th, Labrador 80th, and Nunavut 107th on the global index. By highlighting these differences, we hope that this research can aid decision-makers in developing informed, regionally specific, and integrative resource policy responses that remedy rather than amplify existing WEF-related inequalities.

Calibration of two validated SARS-CoV-2 pseudovirus neutralization assays for COVID-19 vaccine evaluation

Vaccine-induced neutralizing antibodies (nAbs) are key biomarkers considered to be associated with vaccine efficacy. In United States government-sponsored phase 3 efficacy trials of COVID-19 vaccines, nAbs are measured by two different validated pseudovirus-based SARS-CoV-2 neutralization assays, with each trial using one of the two assays. Here we describe and compare the nAb titers obtained in the two assays. We observe that one assay consistently yielded higher nAb titers than the other when both assays were performed on the World Health Organization’s anti-SARS-CoV-2 immunoglobulin International Standard, COVID-19 convalescent sera, and mRNA-1273 vaccinee sera. To overcome the challenge this difference in readout poses in comparing/combining data from the two assays, we evaluate three calibration approaches and show that readouts from the two assays can be calibrated to a common scale. These results may aid decision-making based on data from these assays for the evaluation and licensure of new or adapted COVID-19 vaccines.

Challenges and Experiences Extending the cBioPortal for Cancer Genomics to a Molecular Tumor Board Platform

In Molecular Tumor Boards (MTBs), therapy recommendations for cancer patients are discussed. To aid decision-making based on the patient’s molecular profile, the research platform cBioPortal was extended based on users’ requirements. Additionally, a comprehensive dockerized workflow was developed to support the deployment of cBioPortal and connected services. In this work, we present the challenges and experiences of nearly two years of implementing and deploying an MTB platform based on cBioPortal and compare those to findings of a previous study.