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.

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.

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.

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.

A Transdisciplinary Approach to Address Climate Change Adaptation for Human Health and Well-Being in Africa.

The health sector response to dealing with the impacts of climate change on human health, whether mitigative or adaptive, is influenced by multiple factors and necessitates creative approaches drawing on resources across multiple sectors. This short communication presents the context in which adaptation to protect human health has been addressed to date and argues for a holistic, transdisciplinary, multisectoral and systems approach going forward. Such a novel health-climate approach requires broad thinking regarding geographies, ecologies and socio-economic policies, and demands that one prioritises services for vulnerable populations at higher risk. Actions to engage more sectors and systems in comprehensive health-climate governance are identified. Much like the World Health Organization's 'Health in All Policies' approach, one should think health governance and climate change together in a transnational framework as a matter not only of health promotion and disease prevention, but of population security. In an African context, there is a need for continued cross-border efforts, through partnerships, blending climate change adaptation and disaster risk reduction, and long-term international financing, to contribute towards meeting sustainable development imperatives.

The effectiveness of continuous respiratory rate monitoring in predicting hypoxic and pyrexic events: a retrospective cohort study.

Respiratory rate (RR) is routinely used to monitor patients with infectious, cardiac and respiratory diseases and is a component of early warning scores used to predict patient deterioration. However, it is often measured visually with considerable bias and inaccuracy.Objectives. Firstly, to compare distribution and accuracy of electronically measured RR (EMRR) and visually measured RR (VMRR). Secondly, to determine whether, and how far in advance, continuous electronic RR monitoring can predict oncoming hypoxic and pyrexic episodes in infectious respiratory disease.Approach.A retrospective cohort study analysing the difference between EMRR and VMRR was conducted using patient data from a large tertiary hospital. Cox proportional hazards models were used to determine whether continuous, EMRR measurements could predict oncoming hypoxic (SpO2 < 92%) and pyrexic (temperature >38 °C) episodes.Main results.Data were gathered from 34 COVID-19 patients, from which a total of 3445 observations of VMRR (independent of Hawthorne effect), peripheral oxygen saturation and temperature and 729 117 observations of EMRR were collected. VMRR had peaks in distribution at 18 and 20 breaths per minute. 70.9% of patients would have had a change of treatment during their admission based on the UK's National Early Warning System if EMRR was used in place of VMRR. An elevated EMRR was predictive of hypoxic (hazard ratio: 1.8 (1.05-3.07)) and pyrexic (hazard ratio: 9.7 (3.8-25)) episodes over the following 12 h.Significance.Continuous EMRR values are systematically different to VMRR values, and results suggest it is a better indicator of true RR as it has lower kurtosis, higher variance, a lack of peaks at expected values (18 and 20) and it measures a physiological component of breathing directly (abdominal movement). Results suggest EMRR is a strong marker of oncoming hypoxia and is highly predictive of oncoming pyrexic events in the following 12 h. In many diseases, this could provide an early window to escalate care prior to deterioration, potentially preventing morbidity and mortality.

People-centered early warning systems in China: A bibliometric analysis of policy documents.

People-Centered Early Warning Systems (PCEWSs) is thought to be low-cost but effective, however, existing studies fail to discuss the basic characteristics of PCEWSs, how a PCEWSs should be built, and the extensible applications of PCEWSs. This study aims for making a significant contribution to the literature through the analysis of the PCEWSs trajectory of and fundamental shifts in policy pertaining to PCEWSs in the disaster domain in China. By using bibliometric analysis of policy documents, this study presents a comprehensive review of China's PCEWS policy system from 1977 to March 2020, which focuses on various types of disasters. The characteristics of policies and the contributing factors of the policy changes in each of the four phases are discussed in depth. Four main tendencies of PCEWSs are identified. This study provides a quantitative foundation for understanding the dynamic policy changes in China's PCEWSs and certain experience includes the disaster characteristics that PCEWSs are suitable to get involved, the orientation that experience and technology should be combined and multi agent participation which calls for more emphasis may serve as a basis for exploring the potential pathways to the effective PCWSs in other countries and regions.