AI and Dynamic Prediction of Deterioration in Covid-19

During the COVID-19 pandemic it became evident that outcome prediction of patients is crucial for triaging, when resources are limited and enable early start or increase of available therapeutic support. COVID-19 demographic risk factors for severe disease and death were rapidly established, including age and sex. Common Clinical Decision Support Systems (CDSS) and Early Warning Systems (EWS) have been used to triage based on demographics, vital signs and laboratory results. However, all of these have limitations, such as dependency of laboratory investigations or set threshold values, were derived from more or less specific cohort studies. Instead, individual illness dynamics and patterns of recovery might be essential characteristics in understanding the critical course of illness.The pandemic has been a game changer for data, and the concept of real-time massive health data has emerged as one of the important tools in battling the pandemic. We here describe the advantages and limitations of established risk scoring systems and show how artificial intelligence applied on dynamic vital parameter changes, may help to predict critical illness, adverse events and death in patients hospitalized with COVID-19.Machine learning assisted dynamic analysis can improve and give patient-specific prediction in Clinical Decision Support systems that have the potential of reducing both morbidity and mortality. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Crisis resilience ‘critical' to stem rising hunger

Speed read Crises, shocks and volatility to food systems becoming the new norm Policy focus should shift to ‘permanent crisis resilience' – report Local groups could be key to gender inclusion, data collection [SYDNEY] A shift towards permanent "crisis resilience” from short-term aid is crucial to mitigate increasingly frequent shocks to the global food system and tackle rising global hunger, say food policy researchers. Rob Vos, IFPRI's director of markets, trade and institutions, says a closer connection is needed between early warning systems that monitor global food market volatility and those that monitor food insecurity at the local level. "Both systems further need to pay more attention to monitoring the risk factors that underly food supply or price shocks as that cause food crises,” Vos tells SciDev.Net.

Capturing the SARS-CoV-2 infection pyramid within the municipality of Rotterdam using longitudinal sewage surveillance.

Despite high vaccination rates in the Netherlands, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to circulate. Longitudinal sewage surveillance was implemented along with the notification of cases as two parts of the surveillance pyramid to validate the use of sewage for surveillance, as an early warning tool, and to measure the effect of interventions. Sewage samples were collected from nine neighborhoods between September 2020 and November 2021. Comparative analysis and modeling were performed to understand the correlation between wastewater and case trends. Using high resolution sampling, normalization of wastewater SARS-CoV-2 concentrations, and 'normalization' of reported positive tests for testing delay and intensity, the incidence of reported positive tests could be modeled based on sewage data, and trends in both surveillance systems coincided. The high collinearity implied that high levels of viral shedding around the onset of disease largely determined SARS-CoV-2 levels in wastewater, and that the observed relationship was independent of variants of concern and vaccination levels. Sewage surveillance alongside a large-scale testing effort where 58 % of a municipality was tested, indicated a five-fold difference in the number of SARS-CoV-2-positive individuals and reported cases through standard testing. Where trends in reported positive cases were biased due to testing delay and testing behavior, wastewater surveillance can objectively display SARS-CoV-2 dynamics for both small and large locations and is sensitive enough to measure small variations in the number of infected individuals within or between neighborhoods. With the transition to a post-acute phase of the pandemic, sewage surveillance can help to keep track of re-emergence, but continued validation studies are needed to assess the predictive value of sewage surveillance with new variants. Our findings and model aid in interpreting SARS-CoV-2 surveillance data for public health decision-making and show its potential as one of the pillars of future surveillance of (re)emerging viruses.

Challenges to sustainability of pediatric early warning systems (PEWS) in low-resource hospitals in Latin America.

Background: Sustainability, or continued use of evidence-based interventions for long-term patient benefit, is the least studied aspect of implementation science. In this study, we evaluate sustainability of a Pediatric Early Warning System (PEWS), an evidence-based intervention to improve early identification of clinical deterioration in hospitalized children, in low-resource settings using the Clinical Capacity for Sustainability Framework (CCS). Methods: We conducted a secondary analysis of a qualitative study to identify barriers and enablers to PEWS implementation. Semi-structured interviews with PEWS implementation leaders and hospital directors at 5 Latin American pediatric oncology centers sustaining PEWS were conducted virtually in Spanish from June to August 2020. Interviews were recorded, professionally transcribed, and translated into English. Exploratory thematic content analysis yielded staff perceptions on PEWS sustainability. Coded segments were analyzed to identify participant perception about the current state and importance of sustaining PEWS, as well as sustainability successes and challenges. Identified sustainability determinants were mapped to the CCS to evaluate its applicability. Results: We interviewed 71 staff including physicians (45%), nurses (45%), and administrators (10%). Participants emphasized the importance of sustaining PEWS for continued patient benefits. Identified sustainability determinants included supportive leadership encouraging ongoing interest in PEWS, beneficial patient outcomes enhancing perceived value of PEWS, integrating PEWS into the routine of patient care, ongoing staff turnover creating training challenges, adequate material resources to promote PEWS use, and the COVID-19 pandemic. While most identified factors mapped to the CCS, COVID-19 emerged as an additional external sustainability challenge. Together, these challenges resulted in multiple impacts on PEWS sustainment, ranging from a small reduction in PEWS quality to complete disruption of PEWS use and subsequent loss of benefits to patients. Participants described several innovative strategies to address identified challenges and promote PEWS sustainability. Conclusion: This study describes clinician perspectives on sustainable implementation of evidence-based interventions in low-resource settings, including sustainability determinants and potential sustainability strategies. Identified factors mapped well to the CCS, however, external factors, such as the COVID pandemic, may additionally impact sustainability. This work highlights an urgent need for theoretically-driven, empirically-informed strategies to support sustainable implementation of evidence-based interventions in settings of all resource-levels.

Consumer Confidence and Real Economic Growth in the Eurozone

Over the past 15 years, the world economic system has experienced two global crises: the financial and economic crisis of 2008 and the pandemic crisis of 2020. The financial crisis of 2008 had a significant impact on the development of the world economy, including the eurozone. Although some sectors of the economy are not recovering and have not reached pre-crisis levels of efficiency, overall economies are characterized by predictable and positive economic trends. The pandemic crisis poses new challenges to the economy in terms of business closures, disrupted supply chains, and high and accelerating inflation. All this brought to the fore the need to analyze the correlations between various indicators and the dynamics of economic growth, so that when unforeseen crises occur, decisions can be made quickly. The aim of the study is to analyze the degree of correlation between the indicator of consumer confidence and real GDP growth by quarter in the euro area. The tested hypothesis is that for the last three years there has been a strong correlation between quarterly data on real economic growth and consumers' direct assessments, as expressed by the consumer confidence indicator. The regression analysis and hypothesis testing are performed using seasonally adjusted monthly data on consumer confidence indicator and seasonally adjusted annual data by quarters on real annual GDP growth in Q2 2019 - Q1 2022. The in-depth regression analysis shows that there is a statistically significant linear relationship between the indicator of consumer confidence and real annual GDP growth by quarters for the period under study. The results of the Granger causality test confirm the conclusions drawn from the dynamic, correlation, and regression analyses. The results of the test prove not only the presence of causality, but also the ability of the consumer confidence indicator to predict real annual growth by quarter during periods of crisis. All this allows to conclude that in periods of import crises, the indicator of consumer confidence can also be used as an early signal of the presence of systemic problems and to determine the dynamics of GDP, as well as to implement specific economic measures and policies.

Communicating landslide risks during the COVID-19 pandemic: A baseline study for PHIVOLCS-Dynaslope stakeholders

Dynaslope Project, a government-funded program implemented by the Philippine Institute of Volcanology and Seismology (PHIVOLCS), develops and deploys an early warning system (EWS) for deep-seated landslides. It uses landslide sensor technology and community involvement in its EWS implementation in 50 sites all over the country. As the pandemic altered people’s modes of communication, it is important to understand how these changes relate to communicating landslide early warning information (LEWI). This research answered: How can the Dynaslope Project effectively communicate landslide risk during the COVID-19 pandemic? Specifically, it a) identified their preferred communication channels and tools during the pandemic, b) enumerated the stakeholders’ perceived participation and communication barriers, and c) measured the degree of trust of the stakeholders in the Project, the LEWI it released, and other key messages. This research was guided by the Actor-Network Theory and the concept of co-orientation which posited that, instead of a single entity defining a network or a system, it was the interactions between people, objects, and institutions that created a collective network and continuously negotiated a coherent understanding. The study employed a descriptive quantitative methodology. It used stratified random sampling to select the participants. Among the major findings include the stakeholders’ preference for offline communication like SMS and calls, the presence of communication and participation barriers including weak or no phone reception and limited access to up-to-date communication devices and the internet, and a high level of trust in the Dynaslope Project because it was implemented by a science-based national agency PHIVOLCS.

Influent Fluctuation Analysis and Operation Strategy of Wastewater Treatment Plants during Shanghai's City-wide Lockdown Period

The city-wide lockdown management was implemented in Shanghai from the end of March to the beginning of June in response to the prevention and control of COVID-19 epidemic. The sudden change of the way of work, lifestyle and industrial structure in the megacity would inevitably influence the characteristics of wastewater production, the composition of wastewater, the influent conditions, and operations of wastewater treatment plants（WWTPs）. The main countermeasures of operation in typical WWTPs were analyzed, which could provide a reference for early-warning and precaution when similar situations occur in WWTPs in the future. In order to eliminate the interference caused by the hysteresis effect after the rain stops in the combined drainage system, the corresponding relationship between the rainfall conditions and the influent quantity and quality of WWTPs was studied.The new statistical standard was established for distinguishing"rainy days"or"count dry days". The contemporaneous data of WWTPs in Shanghai central area during dry season in the past three years was analyzed. It was found that the quantity and quality of the influent showed a reverse trend during Shanghai's city-wide lockdown period. The quantity of wastewater decreased by 6% in the corresponding period, and the concentration of COD and NH3-N decreased by 33% and 17%, respectively. The addition of a large amount of chlorine-containing disinfectants led to an increase in the concentration of residual chlorine in the influent of some WWTPs. Based on the analysis of treatment process and design parameters of the existing WWTPs, the operation mode of exploiting potentialities was finally adopted.WWTPs operated stably and the effluent quality could meet the discharge standards through measures such as strengthening pretreatment, prolonging sludge retention time, controlling the concentration of dissolved oxygen in the biological reaction section, and increasing the dosage of phosphorus removal agents.

The Whistleblower’s Dilemma: An Evolutionary Game Analysis of the Public Health Early Warning System

Although the ability to manage public health emergencies in China has improved significantly, there are still many challenges to the existing information transmission mechanism in pandemic early warning systems. In this context, a tripartite evolutionary game model composed of the local government, the whistleblower, and the public is formulated. By using Matlab, the dynamic evolution path of the game model is stimulated under different conditions. Stable strategies for an early warning system for public health emergencies are also explored. The results indicate that the cost of whistleblowing, the cost of response, and the benefit of attention significantly influence strategic decisions among three parties. This study highlights the importance of whistleblowing in managing public health emergencies. Yet, our findings provide theoretical support for policy recommendations for promoting public health emergency preparedness.

Home Telemonitoring and a Diagnostic Algorithm in the Management of Heart Failure in the Netherlands: Cost-effectiveness Analysis.

BACKGROUND: Heart failure is a major health concern associated with significant morbidity, mortality, and reduced quality of life in patients. Home telemonitoring (HTM) facilitates frequent or continuous assessment of disease signs and symptoms, and it has shown to improve compliance by involving patients in their own care and prevent emergency admissions by facilitating early detection of clinically significant changes. Diagnostic algorithms (DAs) are predictive mathematical relationships that make use of a wide range of collected data for calculating the likelihood of a particular event and use this output for prioritizing patients with regard to their treatment. OBJECTIVE: This study aims to assess the cost-effectiveness of HTM and a DA in the management of heart failure in the Netherlands. Three interventions were analyzed: usual care, HTM, and HTM plus a DA. METHODS: A previously published discrete event simulation model was used. The base-case analysis was performed according to the Dutch guidelines for economic evaluation. Sensitivity, scenario, and value of information analyses were performed. Particular attention was given to the cost-effectiveness of the DA at various levels of diagnostic accuracy of event prediction and to different patient subgroups. RESULTS: HTM plus the DA extendedly dominates HTM alone, and it has a deterministic incremental cost-effectiveness ratio compared with usual care of €27,712 (currency conversion rate in purchasing power parity at the time of study: €1=US $1.29; further conversions are not applicable in cost-effectiveness terms) per quality-adjusted life year. The model showed robustness in the sensitivity and scenario analyses. HTM plus the DA had a 96.0% probability of being cost-effective at the appropriate €80,000 per quality-adjusted life year threshold. An optimal point for the threshold value for the alarm of the DA in terms of its cost-effectiveness was estimated. New York Heart Association class IV patients were the subgroup with the worst cost-effectiveness results versus usual care, while HTM plus the DA was found to be the most cost-effective for patients aged <65 years and for patients in New York Heart Association class I. CONCLUSIONS: Although the increased costs of adopting HTM plus the DA in the management of heart failure may seemingly be an additional strain on scarce health care resources, the results of this study demonstrate that, by increasing patient life expectancy by 1.28 years and reducing their hospitalization rate by 23% when compared with usual care, the use of this technology may be seen as an investment, as HTM plus the DA in its current form extendedly dominates HTM alone and is cost-effective compared with usual care at normally accepted thresholds in the Netherlands.

Early warning system to predict energy prices: the role of artificial intelligence and machine learning.

The COVID-19 pandemic has inflicted the global economy and caused substantial financial losses. The energy sector was heavily affected and resulted in energy prices massively tumbling. The Russian invasion of Ukraine has fueled the energy maker more volatile. In such uncertain contexts, an Early Warning System (EWS) would efficiently contribute to stabilizing market swings. It will leverage the ability to control operating costs and pave the way for smooth economic recovery. Within this framework, we deploy Machine Learning (ML) models to forecast energy equity prices by employing uncertainty indices as a proxy for predicting energy market volatility. We empirically examine the comparative effectiveness of prevalent ML models and conventional approaches (regression) to forecast the energy equity prices by utilizing the daily data from 1/6/2011 to 18/1/2022 for four US uncertainty and eight energy equity indices. Results show that the Nonlinear Autoregressive with External (Exogenous) parameters (NARX) of Neural Networks (NN) scored significantly better accuracy than all other (25) ML models and conventional approaches. The study outcomes are beneficial for policymakers, governments, market regulators, investors, hedge and mutual funds, and corporations. They improve stakeholders' resilience to exogenous shocks, blaze the recovery path, and provide evidence-based for assets allocation strategies.

Satellites to protect the world, if data is open to all

Speed read Benefits of satellite tech for world’s poorest and the planet could be boundless – if data is open to all Demand for resources to build satellites could put vulnerable communities, indigenous land rights at risk Future challenges remain, such as need for infrastructure, uneven distribution between countries Satellite technologies are a new frontier in global development. Satellites are being used for everything from monitoring illegal fishing to tracking malaria, from supporting early warning systems in flood-prone nations to measuring crop yields and giving farmers advice on fertilisers. Nomadic communities in Africa are using space technology and mobile phone networks to go where the water is and avoid violent conflicts in the Sahel related to climate change and food insecurity.

Secure meat-hungry food systems to prevent next pandemic

Speed read Intensive agriculture for animal protein source of emerging diseases, experts say But, understanding of animal pathogen spillover incomplete, making pandemic prediction difficult Investment needed in early warning systems, One Health preparedness Sustainable agriculture and better health monitoring needed to break chain of disease transmission in food systems. Environmental hotspots Agriculture and meat production are significant contributors of greenhouse gases, both directly and through land-use change — Andersen notes that the drivers of pandemics are often the same drivers of climate change and biodiversity loss. "Climate change results in changing environmental conditions, which impacts on the ecosystem characteristics and as a result, it changes the distribution of animal species, and therefore also of any microorganisms which they carry,” Dirk Pfeiffer, a professor of veterinary epidemiology at City University of Hong Kong and the Royal Veterinary College in London, tells SciDev.Net.

Future perspectives of emerging infectious diseases control: A One Health approach.

Since the beginning of the COVID-19 pandemic in early 2020, global efforts to respond to and control COVID-19 have varied widely with some countries, including Australia, successfully containing local transmission, and minimising negative impacts to health and economies. Over this time, global awareness of climate variability due to climate change and the risk factors for emerging infectious diseases transmission has increased alongside an understanding of the inextricable relationship between the health of the environment, humans, and animals. Overall, the global response to the current pandemic suggests there is an urgent need for a One Health approach in controlling and preventing future pandemics, through developing integrated, dynamic, spatiotemporal early warning systems based on a One Health approach for emerging infectious diseases.

News from BEL

Some of the major orders acquired during the year were Avionics Pack for Light Combat Aircraft (LCA), Advanced Electronic Warfare Suite for Fighter Aircraft, Instrumented Electronic Warfare Range (IEWR), Electronic Voting Machine (EVM) and Voter Verifiable Paper Audit Trail (VVPAT), Cdr TIT90 Tank, COMINT System, Radar Warning Receiver (RWR) and Missile Approach Warning System (MAWS) for C-295 Programme, Electronic Gun, IoT Gateway, etc. Some of the flagship projects executed during FY 2021-22 were Missile Systems (Air Defence Weapon System and LRSAM), Command and Control Systems, Communication and Encryption products, various Sonars, Electro-optic Systems, Fire Control Systems, Gun Upgrades, various Radars, Electronic Warfare Systems, Coastal Surveillance System, Un-manned Systems, Home Land Security Systems, Smart City projects, K-FON, Medical Electronics, etc. Major products exported included Coastal Surveillance System, Trans-Receive (TR) Modules, EO-IR Payload System, Compact Multi-Purpose Advanced Stabilization System (EOS CoMPASS), Solar Hybrid Power Plant, Data Link, Electro-Mechanical parts, Low Band Receivers (LBREC), Medical Electronics, Spares for Radars, etc.

A Protection-Motivation Perspective to Explain Intention to Use and Continue to Use Mobile Warning Systems

Mobile emergency warning apps are essential for effective emergency communication – of course, provided the population intends to use them. Drawing on protection motivation theory, the study validated a psychometric model to explain what motivates individuals to install a warning app for the first time and to keep using it over time. Multi-group covariance-based structural equation modeling was used to model the answers to a survey that measured the drivers of intention to begin using or intention to continue using a warning app. The model shows that, for both non-users and users, trust, social influence, and response efficacy positively and maladaptive rewards negatively affect intention to use and intention to continue use warning apps. However, perceived vulnerability influences only intention to use, whereas response cost and self-efficacy affect continued use intention. Hence, this study enhances the theoretical understanding of technology-enabled protection behaviors and provides practitioners with a list of factors to consider for pushing the adoption and continued use of emergency warning applications.

AVIATION & DEFENCE In India

MoD and Make in India scheme Many significant projects including 155mm Artillery Gun system 'Dhanush', Bridge Laying Tank, Light Combat Aircraft 'Tejas', 'Akash' Surface to Air Missile system, Submarine 'INS Kalvari', Inshore Patrol Vessel, Offshore Surveillance Ship, 'INS Chennai', Anti-Submarine Warfare Corvette (ASWC), Arjun Armoured Repair and Recovery Vehicle, Landing Craft Utility, Bridge Laying Tank, Bi-Modular Charge System (BMCS) for 155mm Ammunition, Thermal Imaging Sight Mark-II for T-72 tank, 25 ? Airbus signs contract with BEL for C295 programme As part of its offset commitments under the prestigious C295 aircraft programme of the Government of India, and in line with the 'Make in India' policy, Airbus Defence and Space has signed a contract with Bharat Electronics Limited (BEL) for the manufacture and supply of Radar Warning Receiver (RWR) and Missile Approach Warning System (MAWS). The order spanning five years from 2023 to 2028 involves supply of critical avionic Line Replaceable Units (LRUs) related to Digital Flight Control Computers, Air Data Computers, Weapon Computers, LRUs related to Radar Warning Receiver (RWR) and Head Up Display. First sea sortie of fifth scorpene submarine 'Vagir' The fifth submarine of Project 75, Yard 11879, Indian Navy's Kalvari class commenced her sea trials on 1 February 2022.

Legal classification and international systems for monitoring and responding to novel psychoactive substances

Novel psychoactive substances make up a broad range of drugs that are not controlled by the United Nations international drug laws. They include synthetic cannabinoids, stimulants, benzodiazepines, opioids, hallucinogens, and dissociatives. Many of these substances are intended to mimic the effects of controlled drugs and are traded as ‘legal’ replacements for them. While the emergence of novel psychoactive substances is not a new phenomenon, driven by globalisation there has been a large increase in the availability, and, subsequently, harms caused by these substances since around 2008. At least until relatively recently, as the list of substances controlled at national level were largely based on those substances controlled under the international drug control system, few novel psychoactive substances were subject to control measures in many countries. However, in response to the growth in the market, increasingly some novel substances have been controlled or otherwise regulated at national level, irrespective of whether or not they are controlled internationally. Invariably, despite this, novel substances continue to appear on the drug market, albeit at a slower pace. This chapter: examines the legal classification of novel psychoactive substances;provides an overview of some of the early warning systems for monitoring and responding to these substances;discusses the current situation in Europe;and highlights the possible future of these substances and how our responses made need to adapt to ever-changing globalized drug markets. This includes a discussion of the work of the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) as part of a unique regional three-step legal framework of early warning, risk assessment, and control measures that allows the European Union to rapidly detect, assess, and respond to public health and social threats caused by these substances. © 2021 Elsevier Inc. All rights reserved.

Missile Defense: Assessment of Testing Approach Needed as Delays and Changes Persist

In fiscal year 2019, the Missile Defense Agency (MDA) delivered many of the Ballistic Missile Defense System (BMDS) assets it planned and conducted key flight tests, but did not meet all of its goals for the year. For example, MDA successfully delivered interceptors for use by warfighters and conducted a salvo test (which involves launching two interceptors at an incoming target) for the Ground-based Midcourse Defense program. However, MDA did not meet all of its goals for delivering assets or testing. For example, MDA completed only two of seven planned flight tests, plus eight additional flight tests that were later added for fiscal year 2019. MDA did not fully execute its fiscal year 2019 flight testing, continuing a decade-long trend in which MDA has been unable to achieve its fiscal year flight testing as scheduled. Although MDA revised its approach to developing its annual test plan in 2009 to ensure the test plan was executable, over the past decade MDA has only been able to conduct 37 percent of its baseline fiscal year testing as originally planned due to various reasons including developmental delays, range and target availability, or changing test objectives. In addition, MDA has not conducted an assessment to determine whether its current process for developing and executing its annual test plan could be improved to help ensure its executability. Without an independent assessment, MDA will continue down the same path, increasing the risk of the same outcomes from the past decadeless testing than originally planned, resulting in less data to demonstrate and validate capabilities.

U.S. Army Short-Range Air Defense Force Structure and Selected Programs: Backgroundand Issues for Congress

The U.S. Army defines Short-Range Air Defense (SHORAD) as dedicated air defense artillery (ADA) and non-dedicated air defense capabilities that enable movement and maneuver by destroying, neutralizing or deterring low altitude air threats to defend critical fixed and semi-fixed assets and maneuver forces. SHORAD units were historically embedded in Army divisions, providing them with an organic capability to protect their critical assets against fixed-wing and rotary-wing aircraft. However, in the early 2000s, these ADA units were divested from the Army to meet force demands deemed more critical at that time.

Automatic Mobile Warning System against People with Elevated Body Temperature

The paper proposes a system that allows for the automatic detection of people with elevated body temperature and estimates distance from them using a smartphone-type device and a single mobile thermal camera. The algorithm automatically finds and selects humans with the highest temperature, and tracks changes in their position in an image sequence. On the basis of the change in the position of the human head in the image, in subsequent frames, the algorithm estimates the distance between camera and human. Owing to the use of fast machine-learning methods, the proposed system can immediately alert the user about the presence of a people with an elevated temperature at a distance of 1–3 m as soon as it appears in the field of view of the camera. The effectiveness of the algorithm was assessed as the ratio of correct distance classifications in the test image set to the total number of test images. Values ranging from 73% to 100% were obtained for over 4000 images of humans at different distances. The proposed method allows for the quick and completely automatic warning aboutt people with elevated temperature, and can be used in popular Android mobile devices.