Temporal assessment of SARS-CoV-2 detection in wastewater and its epidemiological implications in COVID-19 case dynamics

This research evaluated the relationship between daily new Coronavirus Disease 2019 (COVID-19) cases and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) concentrations in wastewater, followed by effects of differential SARS-CoV-2 shedding loads across various COVID-19 outbreaks. Linear regression analyses were utilized to examine the lead time of the SARS-CoV-2 signal in wastewater relative to new COVID-19 clinical cases. During the Delta wave, no lead time was evident, highlighting limited predictive capability of wastewater monitoring during this phase. However, significant lead times were observed during the Omicron wave, potentially attributed to testing capacity overload and subsequent case reporting delays or changes in shedding patterns. During the Post-Omicron wave (Febuary 23 to May 19, 2022), no lead time was discernible, whereas following the lifting of the COVID-19 state of emergency (May 30, 2022 to May 30, 2023), the correlation coefficient increased and demonstrated the potential of wastewater surveillance as an early warning system. Subsequently, we explored the virus shedding in wastewater through feces, operationalized as the ratio of SARS-CoV-2 concentrations to daily new COVID-19 cases. This ratio varied significantly across the Delta, Omicron, other variants and post-state-emergency phases, with the Kruskal-Wallis H test confirming a significant difference in medians across these stages (P < 0.0001). Despite its promise, wastewater surveillance of COVID-19 disease prevalence presents several challenges, including virus shedding variability, data interpretation complexity, the impact of environmental factors on viral degradation, and the lack of standardized testing procedures. Overall, our findings offer insights into the correlation between COVID-19 cases and wastewater viral concentrations, potential variation in SARS-CoV-2 shedding in wastewater across different pandemic phases, and underscore the promise and limitations of wastewater surveillance as an early warning system for disease prevalence trends.

Field and laboratory studies of fluorescence-based technologies for real-time tracking of cyanobacterial cell lysis and potential microcystins release

Elevated levels of dissolved microcystins (MCs) in source water due to rapid cell lysis of harmful cyanobacterial blooms may pose serious challenges for drinking water treatment. Catastrophic cell lysis can result from outbreaks of naturally-occurring cyanophages - as documented in Lake Erie during the Toledo water crisis of 2014 and in 2019, or through the application of algaecides or water treatment chemicals. Real-time detection of cyanobacterial cell lysis in source water would provide a valuable tool for drinking water plant and reservoir managers. In this study we explored two real-time fluorescence-based devices, PhycoSens and PhycoLA, that can detect unbound phycocyanin (uPC) as a potential indication of cell lysis and MCs release. The PhycoSens was deployed at the Low Service pump station of the City of Toledo Lake Erie drinking water treatment plant from July 15 to October 19, 2022 during the annual cyanobacteria bloom season. It measured major algal groups and uPC in incoming lake water at 15-min intervals during cyanobacteria dominant and senescence periods. Intermittent uPC detections from the PhycoSens over a three-month period coincided with periods of increasing proportions of extracellular MCs relative to total (intracellular and extracellular) MCs, indicating potential for uPC use as an indicator of cyanobacterial cell integrity. Following exposures of laboratory-cultured MCs-producing Microcystis aeruginosa NIES-298 (120 μg chlorophyll/L) to cyanophage Ma-LMM01, copper sulfate (0.5 and 1 mg Cu/L), sodium carbonate peroxyhydrate (PAK® 27, 6.7 and 10 mg H2O2/L), and potassium permanganate (2.5 and 4 mg/L), appearance of uPC coincided with elevated fractions of extracellular MCs. The PhycoLA was used to monitor batch samples collected daily from Lake Erie water exposed to algaecides in the laboratory. Concurrence of uPC signal and surge of dissolved MCs was observed following 24-h exposures to copper sulfate and PAK 27. Overall results indicate the appearance of uPC is a useful indicator of the onset of cyanobacterial cell lysis and the release of MCs when MCs are present.

Analyzing retraction responses of Tubifex tubifex (Oligochaeta: Naididae) colonies to copper treatments through a digital image analysis approach for developing early biological warning systems

Biological monitoring and assessments are commonly used for sustainable ecosystem management. Oligochaetes are found in various freshwater ecosystems and have been used as indicators of water quality and for the biological assessment of aquatic ecosystems. Among aquatic oligochaetes, the sludge worm Tubifex tubifex (Oligochaeta, Naididae) is tolerant to organic pollution and has been used as a biomonitoring indicator of toxicity and organic pollution. In this study, we investigated the response of worm colonies to copper (CuSO4) treatments (0.01, 0.05, 0.1, 0.5, and 1.0 mg/L) in an observation cage (100 mL beaker) for 30 min. Using a digital image analysis approach, we measured the changes in the colony image area between pre- and post-copper treatment. After copper treatment, the colony image area tended to decrease, even at low copper concentrations. In addition, the colony areas did not recover to their original levels at high concentrations, although those at low concentrations did. Area decreased proportional to the logarithm of the copper concentration. Finally, our results present the possible use of the retraction responses of Tubifex tubifex colonies to chemical disturbances as early biological warning systems.

Strengthening global health security through health early warning systems: A literature review and case study

Disease transmission is dependent on a variety of factors, including the characteristics of an event, such as crowding and shared accommodations, the potential of participants having prolonged exposure and close contact with infectious individuals, the type of activities, and the characteristics of the participants, such as their age and immunity to infectious agents [1-3]. Effective control of outbreaks of infectious diseases requires rapid diagnosis and intervention in high-risk settings. As a result, syndromic and event-based surveillance may be used to enhance the responsiveness of the surveillance system [1]. In public health, surveillance is collecting, analyzing, and interpreting data across time to inform decision-making and aid policy implementation [1]. In this review article we aimed to provide an overview of the principles, types, uses, advantages, and limitations of surveillance systems and to highlight the importance of early warning systems in response to the information received by disease surveillance. The study conducted a comprehensive literature search using several databases, selecting, and reviewing 78 articles that covered different types of surveillance systems, their applications, and their impact on controlling infectious diseases. The article also presents a case study from the Hajj gathering, which highlighted the development, evaluation, and impact of early warning systems on response to the information received by disease surveillance. The study concludes that ongoing disease surveillance should be accompanied by well-designed early warning and response systems, and continuous efforts should be invested in evaluating and validating these systems to minimize the risk of reporting delays and reducing the risk of outbreaks.

Towards an early warning system on the Basque coast

This study compares the performance of different wave overtopping estimation models at urban beaches. The models selected for comparison are the Mase et al. (2013) and EurOtop parametric models and the XBeach process-based model in surfbeat and non-hydrostatic mode. Seven energetic storms are selected between 2015 and 2022 with offshore significant wave height ranging between 3 m and 8 m and peak period between 12 s and 20 s to perform the model comparison. The information required to run and validate the models (beach slope, shoreface shape, absence/presence of overtopping) was collected for each storm from coastal videometry. To account for the uncertainties derived from the incident waves randomness and the bathymetry shape when using the process-based model, a series of simulations with random seed boundary conditions were run over two different realistic profile shapes for each storm. The present study is a pilot study on the beach of Zarautz; however, it can be extended to other beaches of the Basque coast. Results indicate that while Mase et al. (2013) and EurOtop tend to reasonably predict the absence or presence of overtopping events, they tend to underestimate the hazard level at the beach of Zarautz. Additionally, the beach underwater profile shape can affect the process-based model performance at intermediate intensity storms and to a lesser extend during moderate storms. Finally, the hazard level at the beach of Zarautz varies significantly alongshore due to the configuration of the seawall, highlighting the need for local adaptation measures. Considering that there is no model that systematically performs better than others, it might be reasonable to use model assemble techniques to draw conclusions from a probabilistic perspective.

Novel machine learning model to improve performance of an early warning system in hospitalized patients: a retrospective multisite cross-validation study

Background

Threshold-based early warning systems (EWS) are used to predict adverse events (Aes). Machine learning (ML) algorithms that incorporate all EWS scores prior to an event may perform better in hospitalized patients.

Methods

The deterioration index (DI) is a proprietary EWS. A threshold of DI >60 is used to predict a composite AE: all-cause mortality, cardiac arrest, transfer to intensive care, and evaluation by the rapid response team in practice. The DI scores were collected for adult patients (≥18 y-o) hospitalized on medical or surgical services during 8-23-2021 to 3-31-2022 from four different Mayo Clinic sites in the United States. A novel ML model was developed and trained on a retrospective cohort of hospital encounters. DI scores were represented in a high-dimensional space using random convolution kernels to facilitate training of a classifier and the area under the receiver operator characteristics curve (AUC) was calculated. Multiple time intervals prior to an AE were analyzed. A leave-one-out cross-validation protocol was used to evaluate performance across separate clinic sites.

Findings

Three different classifiers were trained on 59,617 encounter-derived DI scores in high-dimensional feature space and the AUCs were compared to two threshold models. All three tested classifiers improved the AUC over the threshold approaches from 0.56 and 0.57 to 0.76, 0.85 and 0.94. Time interval analysis of the top performing classifier showed best accuracy in the hour before an event occurred (AUC 0.91), but prediction held up even in the 12 h before an AE (AUC 0.80 at minus 12 h, 0.81 at minus 9 h, 0.85 at minus 6 h, and 0.88 at minus 3 h before an AE). Multisite cross-validation using leave-one-out approach on data from four different clinical sites showed broad generalization performance of the top performing ML model with AUC of 0.91, 0.91, 0.95, and 0.91.

Interpretation

A novel ML model that incorporates all the longitudinal DI scores prior to an AE in a hospitalized patient performs better at outcome prediction than the currently used threshold model. The use of clinical data, a generalized ML technique, and successful multisite cross-validation demonstrate the feasibility of our model in clinical implementation.

Funding

No funding to report.

Characteristics and time course of benzodiazepine-type new psychoactive substance detections in Australia: results from the Emerging Drugs Network of Australia - Victoria project 2020-2022

INTRODUCTION

The emergence of benzodiazepine-type new psychoactive substances (NPSs) are a growing international public health concern, with increasing detections in drug seizures and clinical and coronial casework. This study describes the patterns and nature of benzodiazepine-type NPS detections extracted from the Emerging Drugs Network of Australia - Victoria (EDNAV) project, to better characterise benzodiazepine-type NPS exposures within an Australian context.

METHODS

EDNAV is a state-wide illicit drug toxicosurveillance project collecting data from patients presenting to an emergency department with illicit drug-related toxicity. Patient blood samples were screened for illicit, pharmaceutical and NPSs utilising liquid chromatography-tandem mass spectrometry. Demographic, clinical, and analytical data was extracted from the centralised registry for cases with an analytical confirmation of a benzodiazepine-type NPS(s) between September 2020-August 2022.

RESULTS

A benzodiazepine-type NPS was detected in 16.5 % of the EDNAV cohort (n = 183/1112). Benzodiazepine-type NPS positive patients were predominately male (69.4 %, n = 127), with a median age of 24 (range 16-68) years. Twelve different benzodiazepine-type NPSs were detected over the two-year period, most commonly clonazolam (n = 82, 44.8 %), etizolam (n = 62, 33.9 %), clobromazolam (n = 43, 23.5 %), flualprazolam (n = 42, 23.0 %), and phenazepam (n = 31, 16.9 %). Two or more benzodiazepine-type NPSs were detected in 47.0 % of benzodiazepine-type NPS positive patients. No patient referenced the use of a benzodiazepine-type NPS by name or reported the possibility of heterogenous product content.

CONCLUSION

Non-prescription benzodiazepine use may be an emerging concern in Australia, particularly amongst young males. The large variety of benzodiazepine-type NPS combinations suggest that consumers may not be aware of product heterogeneity upon purchase or use. Continued monitoring efforts are paramount to inform harm reduction opportunities.

A risk-based approach to community illicit drug toxicosurveillance: operationalisation of the Emerging Drugs Network of Australia - Victoria (EDNAV) project

INTRODUCTION

The Emerging Drugs Network of Australia - Victoria (EDNAV) project is a newly established toxicosurveillance network that collates clinical and toxicological data from patients presenting to emergency departments with illicit drug related toxicity in a centralised clinical registry. Data are obtained from a network of sixteen public hospital emergency departments across Victoria, Australia (13 metropolitan and three regional). Comprehensive toxicological analysis of a purposive sample of 22 patients is conducted each week, with reporting of results to key alcohol and other drug stakeholders. This paper describes the overarching framework and risk-based approach developed within Victoria to assess drug intelligence from EDNAV toxicosurveillance.

METHODS

Risk management principles from other spheres of public health surveillance and healthcare clinical governance have been adapted to the EDNAV framework with the aim of facilitating a consistent and evidence-based approach to assessing weekly drug intelligence. The EDNAV Risk Register was reviewed over the first two years of EDNAV project operation (September 2020 - August 2022), with examples of eight risk assessments detailed to demonstrate the process from signal detection to public health intervention.

RESULTS

A total of 1112 patient presentations were documented in the EDNAV Clinical Registry, with 95 signals of concern entered into the EDNAV Risk Register over the two-year study period. The eight examples examined in further detail included suspected drug adulteration (novel opioid adulterated heroin, para-methoxymethamphetamine adulterated 3,4-methylenedioxymethamphetamine (MDMA)), drug substitution (25B-NBOH sold as lysergic acid diethylamide, five benzodiazepine-type new psychoactive substances in a single tablet, protonitazene sold as ketamine), new drug detection (N,N-dimethylpentylone), contamination (unreported acetylfentanyl) and a fatality subsequent to MDMA use. A total of four public Drug Alerts were issued over this period.

CONCLUSIONS

Continued toxicosurveillance efforts are paramount to characterising the changing landscape of illicit drug use. This work demonstrates a functional model for risk assessment of illicit drug toxicosurveillance, underpinned by analytical confirmation and evidence-based decision-making.

Assessment of rapid wastewater surveillance for determination of communicable disease spread in municipalities

Wastewater surveillance (WS) helps to improve the understanding of the spread of communicable diseases in communities. WS can assist public health decision-makers in the design and implementation of timely mitigation measures. There is an increased need to use reliable, cost-effective, simple, and rapid WS systems, given traditional analytical (or 'gold-standard') programs are instrument/time-intensive, and dependent on highly skilled personnel. This study investigated the application of the portable GeneXpert platform for WS of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A virus (IAV), influenza B virus (IBV), and respiratory syncytial virus (RSV). The GeneXpert system with the Xpert Xpress-SARS-CoV-2/Flu/RSV test kit uses reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to analyze wastewater samples. From September 2022 through January 2023, wastewater samples were collected from the influents of municipal wastewater treatment plants (MWTPs) of Saskatoon, Prince Albert, and North Battleford in the province of Saskatchewan, Canada. Both raw and concentrated wastewater samples were subjected to the GeneXpert analysis. Results showed that the Saskatoon wastewater viral loads were significantly correlated to Saskatchewan's influenza and COVID-19 clinical cases, with a lead time of 10 days for IAV and a lag time of 4 days for SARS-CoV-2. Additionally, the GeneXpert analysis of the three cities' wastewater samples showed that the raw WS could capture the dynamics of SARS-CoV-2 and IAV due to their correlation with concentrated WS. Interestingly, IBV loads were not detected in any wastewater samples, while the Saskatoon and Prince Albert wastewater samples collected following the 2023 holiday season (end of December and beginning of January) were positive for RSV. This study indicates that the GeneXpert has excellent potential for use in the development of an early warning system for transmissible disease in municipalities and limited-resource communities while simultaneously providing stakeholders with an efficient WS methodology.

Response of wastewater-based epidemiology predictor for the second wave of COVID-19 in Ahmedabad, India: A long-term data Perspective

In this work, we present an eight-month longitudinal study of wastewater-based epidemiology (WBE) in Ahmedabad, India, where wastewater surveillance was introduced in September 2020 after the successful containment of the first wave of COVID-19 to predict the resurge of the infection during the second wave of the pandemic. The study aims to elucidate the weekly resolution of the SARS-CoV-2 RNA data for eight months in wastewater samples to predict the COVID-19 situation and identify hotspots in Ahmedabad. A total of 287 samples were analyzed for SARS-CoV-2 RNA using RT-PCR, and Spearman's rank correlation was applied to depict the early warning potential of WBE. During September 2020 to April 2021, the increasing number of positive wastewater influent samples correlated with the growing number of confirmed clinical cases. It also showed clear evidence of early detection of the second wave of COVID-19 in Ahmedabad (March 2021). 258 out of a total 287 samples were detected positive with at least two out of three SARS-CoV-2 genes (N, ORF- 1 ab, and S). Monthly variation represented a significant decline in all three gene copies in October compared to September 2020, followed by an abrupt increase in November 2020. A similar increment in the gene copies was observed in March and April 2021, which would be an indicator of the second wave of COVID-19. A lead time of 1-2 weeks was observed in the change of gene concentrations compared with clinically confirmed cases. Measured wastewater ORF- 1 ab gene copies ranged from 6.1 x 102 (October 2020) to 1.4 x 104 (November 2020) copies/mL, and wastewater gene levels typically lead to confirmed cases by one to two weeks. The study highlights the value of WBE as a monitoring tool to predict waves within a pandemic, identify local disease hotspots within a city, and guide rapid management interventions.

China's public health initiatives for climate change adaptation

China's health gains over the past decades face potential reversals if climate change adaptation is not prioritized. China's temperature rise surpasses the global average due to urban heat islands and ecological changes, and demands urgent actions to safeguard public health. Effective adaptation need to consider China's urbanization trends, underlying non-communicable diseases, an aging population, and future pandemic threats. Climate change adaptation initiatives and strategies include urban green space, healthy indoor environments, spatial planning for cities, advance location-specific early warning systems for extreme weather events, and a holistic approach for linking carbon neutrality to health co-benefits. Innovation and technology uptake is a crucial opportunity. China's successful climate adaptation can foster international collaboration regionally and beyond.

Forecasting China's stock market risk under the background of the Stock Connect programs

With the opening of the Stock Connect programs, the mainland China and Hong Kong stock markets are becoming more closely linked. In this paper, we develop a China's stock market risk early warning system. The proposed early warning system consists of three components. First, we use value at risk (VaR) to identify the stock market risk in which stock market risk is divided into multiple categories instead of two categories. Second, we construct a comprehensive indicator system in which basic indicators, technical indicators, overseas return rate indicators, and macroeconomic indicators are considered simultaneously. Third, we use four machine learning models, namely long short-term memory (LSTM), gate recurrent unit (GRU), multilayer perceptron (MLP), and EXtreme Gradient Boosting algorithm (XGBoost), to predict China's stock market risk. Experimental results show that: (1) Considering the macroeconomic indicators and basic indicators of Shanghai Composite Index (SSEC), ShenZhen Component Index (SZCZ) and Hang Seng Index (HSI) can significantly improve the performance of predicting China's stock market risk. (2) The opening of SH-HK Stock Connect program improves the predictive performance, but the opening of SZ-HK Stock Connect program decreases the predictive performance. (3) The indicators related to Hong Kong become more important after the SZ-HK Stock Connect program.

20-Month monitoring of SARS-CoV-2 in wastewater of Curitiba, in Southern Brazil

The COVID-19 pandemic resulted in the collapse of healthcare systems and led to the development and application of several approaches of wastewater-based epidemiology to monitor infected populations. The main objective of this study was to carry out a SARS-CoV-2 wastewater based surveillance in Curitiba, Southern Brazil Sewage samples were collected weekly for 20 months at the entrance of five treatment plants representing the entire city and quantified by qPCR using the N1 marker. The viral loads were correlated with epidemiological data. The correlation by sampling points showed that the relationship between the viral loads and the number of reported cases was best described by a cross-correlation function, indicating a lag between 7 and 14 days amidst the variables, whereas the data for the entire city presented a higher correlation (0.84) with the number of positive tests at lag 0 (sampling day). The results also suggest that the Omicron VOC resulted in higher titers than the Delta VOC. Overall, our results showed that the approach used was robust as an early warning system, even with the use of different epidemiological indicators or changes in the virus variants in circulation. Therefore, it can contribute to public decision-makers and health interventions, especially in vulnerable and low-income regions with limited clinical testing capacity. Looking toward the future, this approach will contribute to a new look at environmental sanitation and should even induce an increase in sewage coverage rates in emerging countries.

A comprehensive early warning system for karst water sources contamination risk, case study of the Unica springs, SW Slovenia

Water suppliers should provide safe drinking water following preventive measures. This is especially important for karst water sources, as they are among the most vulnerable. Recently, there has been a strong focus on the early warning system, which mainly involves monitoring proxy parameters, but does not consider drainage area conditions and other monitoring recommendations. Here, we present an innovative strategy for assessing contamination risk of karst water sources that covers spatio-temporal dimensions and can be integrated into management practices. It is based on event-based monitoring and risk mapping and has been tested in a well-known study area. The holistic early warning system provides accurate spatial hazard and risk assessment and operational monitoring guidelines, including locations, indicator parameters, and temporal resolution and duration. In the study area, the high contamination risk, representing 0.5 % of the area, was spatially delineated. The highest probability of source contamination occurs during recharge events when proxy parameters such as bacteria, ATP, Cl, and Ca/Mg ratio should be monitored in addition to continuous monitoring of turbidity, EC, and T. Monitoring of sinking streams should serve as a preventive measure, since water transfer from ponors to springs has been shown to take about one day, and poor quality water is present for at least another day. Therefore, intensive monitoring should be conducted at intervals of a few hours for at least a week. Although hydrologic systems vary, the proposed strategy is particularly useful in systems where water flows rapidly and where remediation is not feasible.

'We don't live in a harm reduction world, we live in a prohibition world': tensions arising in the design of drug alerts

BACKGROUND

Drug alerts designed for health and community workforces have potential to avert acute harms associated with unpredictable illicit drug markets, by preparing workers to respond to unusual drug-related events, and distribute information to service users. However, the design of such alerts is complicated by diverse needs of individuals, and broader socio-political contexts. Here, we discuss the tensions that arose in the process of co-designing drug alert templates with health and community workers.

METHODS

We conducted five in-depth digital co-design workshops with 31 workers employed in alcohol and other drug and urgent care settings. Our approach to analysis was informed by Iterative Categorisation and reflexive thematic analysis methods.

RESULTS

We identified five key tensions. First, there is a need to provide comprehensive information to meet the information needs of a diverse group of workers with varying knowledge levels, while also designing alerts to be clear, concise, and relevant to the work of individuals. Second, it is important that alerts do not create 'information overload'; however, it is also important that information should be available to those who want it. Third, alert design and dissemination must be perceived to be credible, to avoid 'alert scepticism'; however, credibility is challenging to develop in a broader context of criminalisation, stigmatisation, and sensationalism. Fourth, alerts must be carefully designed to achieve 'intended effects' and avoid unintended effects, while acknowledging that it is impossible to control all potential effects. Finally, while alerts may be intended for an audience of health and community workers, people who use drugs are the end-users and must be kept front of mind in the design process.

CONCLUSIONS

The co-design process revealed complexities in designing drug alerts, particularly in the context of stigmatised illicit drug use, workforce diversity, and dissemination strategies. This study has highlighted the value of developing these important risk communication tools with their target audiences to ensure that they are relevant, useful, and impactful. The findings have informed the development of our drug alert prototypes and provide local context to complement existing best-practice risk-communications literature.

Usability and acceptance of crowd-based early warning of harmful algal blooms

Crowdsensing has become an alternative solution to physical sensors and apparatuses. Utilizing citizen science communities is undoubtedly a much cheaper solution. However, similar to other participatory-based applications, the willingness of community members to be actively involved is paramount to the success of implementation. This research investigated factors that affect the continual use intention of a crowd-based early warning system (CBEWS) to mitigate harmful algal blooms (HABs). This study applied the partial least square-structural equation modeling (PLS-SEM) using an augmented technology acceptance model (TAM). In addition to the native TAM variables, such as perceived ease of use and usefulness as well as attitude, other factors, including awareness, social influence, and reward, were also studied. Furthermore, the usability factor was examined, specifically using the System Usability Scale (SUS) score as a determinant. Results showed that usability positively affected the perceived ease of use. Moreover, perceived usefulness and awareness influenced users' attitudes toward using CBEWS. Meanwhile, the reward had no significant effects on continual use intention.

Willingness to pay for health gains from an international integrated early warning system for infectious disease outbreaks

Recently, due to the corona virus outbreak, pandemics and their effects have been at the forefront of the research agenda. However, estimates of the perceived value of early warning systems (EWSs) for identifying, containing, and mitigating outbreaks remain scarce. This paper aims to show how potential health gains due to an international EWS might be valued. This paper reports on a study into willingness to pay (WTP) in six European countries for health gains due to an EWS. The context in which health is gained, those affected, and the reduction in risk of contracting the disease generated by the EWS are varied across seven scenarios. Using linear regression, we analyse this 'augmented' willingness to pay for a QALY (WTP-Q) for each of the scenarios, where 'augmented' refers to the possible inclusion of context specific elements of value, such as feelings of safety. An initial WTP-Q estimate for the basic scenario is €17,400. This can be interpreted as a threshold for investment per QALY into an EWS. Overall, WTP estimates move in the expected directions (e.g. higher risk reduction leads to higher WTP). However, changes in respondents' WTP for reductions in risk were not proportional to the magnitude of the change in risk reduction. This study provided estimates of the monetary value of health gains in the context of a pandemic under seven scenarios which differ in terms of outcome, risk reduction and those affected. It also highlights the importance of future research into optimal ways of eliciting thresholds for investments in public health interventions.

An intelligent early warning system of analyzing Twitter data using machine learning on COVID-19 surveillance in the US

The World Health Organization (WHO) declared on 11th March 2020 the spread of the coronavirus disease 2019 (COVID-19) a pandemic. The traditional infectious disease surveillance had failed to alert public health authorities to intervene in time and mitigate and control the COVID-19 before it became a pandemic. Compared with traditional public health surveillance, harnessing the rich data from social media, including Twitter, has been considered a useful tool and can overcome the limitations of the traditional surveillance system. This paper proposes an intelligent COVID-19 early warning system using Twitter data with novel machine learning methods. We use the natural language processing (NLP) pre-training technique, i.e., fine-tuning BERT as a Twitter classification method. Moreover, we implement a COVID-19 forecasting model through a Twitter-based linear regression model to detect early signs of the COVID-19 outbreak. Furthermore, we develop an expert system, an early warning web application based on the proposed methods. The experimental results suggest that it is feasible to use Twitter data to provide COVID-19 surveillance and prediction in the US to support health departments' decision-making.

Model-based assessment of COVID-19 epidemic dynamics by wastewater analysis

Continuous surveillance of COVID-19 diffusion remains crucial to control its diffusion and to anticipate infection waves. Detecting viral RNA load in wastewater samples has been suggested as an effective approach for epidemic monitoring and the development of an effective warning system. However, its quantitative link to the epidemic status and the stages of outbreak is still elusive. Modelling is thus crucial to address these challenges. In this study, we present a novel mechanistic model-based approach to reconstruct the complete epidemic dynamics from SARS-CoV-2 viral load in wastewater. Our approach integrates noisy wastewater data and daily case numbers into a dynamical epidemiological model. As demonstrated for various regions and sampling protocols, it quantifies the case numbers, provides epidemic indicators and accurately infers future epidemic trends. Following its quantitative analysis, we also provide recommendations for wastewater data standards and for their use as warning indicators against new infection waves. In situations of reduced testing capacity, our modelling approach can enhance the surveillance of wastewater for early epidemic prediction and robust and cost-effective real-time monitoring of local COVID-19 dynamics.

Multi-step short-term [Formula: see text] forecasting for enactment of proactive environmental regulation strategies

Particulate matter is one of the key contributors of air pollution and climate change. Long-term exposure to constituents of air pollutants has exerted serious health implications in both humans and plants leading to a detrimental impact on economy. Among the pollutants contributing to air quality determination, particulate matter has been linked to serious health implications causing pulmonary complications, cardiovascular diseases, growth retardation and ultimately death. In agriculture, crop yield is also negatively impacted by the deposition of particulate matter on stomata of the plant which is alarming and can cause food security concerns. The deleterious impact of air pollutants on human health, agricultural and economic well-being highlights the importance of quantifying and forecasting particulate matter. Several deterministic and deep learning models have been employed in the recent years to forecast the concentration of particulate matter. Among them, deep learning models have shown promising results when it comes to modeling time series data and forecasting it. We have explored recurrent neural networks with LSTM model which shows potential to predict the particulate matter ([Formula: see text]) based on multi-step multi-variate data of two of the most polluted regions of South Asia, Beijing, China and Punjab, Pakistan effectively. The LSTM model is tuned using Bayesian optimization technique to employ the appropriate hyper-parameters and weight initialization strategies based on the dataset. The model was able to predict [Formula: see text] for the next hour with root-mean-square error (RMSE) of 0.1913 (91.5% accuracy) and this error gradually increases with the number of time steps with next 24 hours steps prediction having RMSE of 0.7290. While in case of Punjab dataset with data recorded once a day, the RMSE for the next day forecast is 0.2192. These multi-step short-term forecasts would play a pivotal role in establishing an early warning system based on the air quality index (AQI) calculated and enable the government in enacting policies to contain it.

From warning messages to preparedness behavior: The role of risk perception and information interaction in the Covid-19 pandemic

During infectious disease outbreaks, early warning is crucial to prevent and control the further spread of the disease. While the different waves of the Covid-19 pandemic have demonstrated the need for continued compliance, little is known about the impact of warning messages and risk perception on individual behavior in public health emergencies. To address this gap, this paper uses data from the second wave of Covid-19 in China to analyse how warning information influences preventive behavior through four categories risk perception and information interaction. Drawing on the protective action decision model (PADM) and the social amplification of risk framework (SARF), risk warning information (content, channel, and type), risk perception (threat perception, hazard- and resource-related preparedness behavior perception and stakeholder perception), information interaction, and preparedness behavior intention are integrated into a comprehensive model. To test our model, we run a survey with 724 residents in Northern China. The results show that hazard-related preparedness behavior perception and stakeholder perception act as mediators between warning and preventive action. Stakeholder perception had much stronger mediating effects than the hazard-related attributes. In addition, information interaction is effective in increasing all categories risk perception, stimulating public response, while functioning as a mediator for warning. The risk warning information content, channel, and type are identified as key drivers of risk perception. The research found that information channel was more related to different risk perception than other characteristics. Overall, these associations in our model explain core mechanisms behind compliance and allow policy-makers to gain new insights into preventive risk communication in public health emergencies.

Process, mechanisms, and early warning of glacier collapse-induced river blocking disasters in the Yarlung Tsangpo Grand Canyon, southeastern Tibetan Plateau

Glacier collapse is a fairly new type of glacier-related disasters on the Asian Water Towers (AWTs) in the warming climate. On 16 October and 29 October 2018, two glacier collapses occurred in the Sedongpu Basin, 7 km downstream from Gyala Village, Paizhen Town, Miling County, on the Yarlung Tsangpo River (YTR). The ice and entrained debris flows caused by the glacier collapses blocked the YTR, resulting in a potential threat to residents and transport lines upstream and downstream. Through post-event field investigations with a helicopter and an unmanned aerial vehicle (UAV), remote sensing interpretation, and seismic, hydrological, and meteorological observations, the process and potential mechanisms of the glacier collapse-induced river blocking (GCRB) disasters were investigated. We confirmed that the first glacier collapse event occurred at 22:48 (Beijing time) on 16 October 2018 and the second began at 08:03 on 29 October 2018. Approximately 130 × 10⁶ m³ of ice and debris detached from the glacier during the glacier collapse, and we calculated that the river blocking fans caused by the first and second glacier collapse event covered ~1.36 km² and ~ 1.29 km² on the main watercourse of the YTR, respectively. We determined that the GCRB incidents represent a disaster chain of glacier collapse → glacial debris flow → river blockage → dammed lake → outburst flood. These incidents arise due to a combination of factors, including glacier activity, climate warming, heavy precipitation, pre-seismic activity, and high topographic relief. In the context of climate warming on the Tibetan Plateau, such glacier collapse induced disaster chains will continue or even intensify in the future. To protect against glacier collapse disasters in the Grand Canyon on the YTR, we established a monitoring and early warning system (EWS), which has already successfully sounded alerts for GCRB incidents. As a major element of an integrated risk management strategy, the EWS represents a viable and promising tool for mitigating climate change-related risks.

Wastewater-based epidemiology in hazard forecasting and early-warning systems for global health risks

With the advent of the SARS-CoV-2 pandemic, Wastewater-Based Epidemiology (WBE) has been applied to track community infection in cities worldwide and has proven succesful as an early warning system for identification of hotspots and changingprevalence of infections (both symptomatic and asymptomatic) at a city or sub-city level. Wastewater is only one of environmental compartments that requires consideration. In this manuscript, we have critically evaluated the knowledge-base and preparedness for building early warning systems in a rapidly urbanising world, with particular attention to Africa, which experiences rapid population growth and urbanisation. We have proposed a Digital Urban Environment Fingerprinting Platform (DUEF) - a new approach in hazard forecasting and early-warning systems for global health risks and an extension to the existing concept of smart cities. The urban environment (especially wastewater) contains a complex mixture of substances including toxic chemicals, infectious biological agents and human excretion products. DUEF assumes that these specific endo- and exogenous residues, anonymously pooled by communities' wastewater, are indicative of community-wide exposure and the resulting effects. DUEF postulates that the measurement of the substances continuously and anonymously pooled by the receiving environment (sewage, surface water, soils and air), can provide near real-time dynamic information about the quantity and type of physical, biological or chemical stressors to which the surveyed systems are exposed, and can create a risk profile on the potential effects of these exposures. Successful development and utilisation of a DUEF globally requires a tiered approach including: Stage I: network building, capacity building, stakeholder engagement as well as a conceptual model, followed by Stage II: DUEF development, Stage III: implementation, and Stage IV: management and utilization. We have identified four key pillars required for the establishment of a DUEF framework: (1) Environmental fingerprints, (2) Socioeconomic fingerprints, (3) Statistics and modelling and (4) Information systems. This manuscript critically evaluates the current knowledge base within each pillar and provides recommendations for further developments with an aim of laying grounds for successful development of global DUEF platforms.

Did the COVID-19 pandemic change the willingness to pay for an early warning system for infectious diseases in Europe?

The COVID-19 pandemic highlights the need for effective infectious disease outbreak prevention. This could entail installing an integrated, international early warning system, aiming to contain and mitigate infectious diseases outbreaks. The amount of resources governments should spend on such preventive measures can be informed by the value citizens attach to such a system. This was already recognized in 2018, when a contingent valuation willingness to pay (WTP) experiment was fielded, eliciting the WTP for such a system in six European countries. We replicated that experiment in the spring of 2020 to test whether and how WTP had changed during an actual pandemic (COVID-19), taking into account differences in infection rates and stringency of measures by government between countries. Overall, we found significant increases in WTP between the two time points, with mean WTP for an early warning system increasing by about 50% (median 30%), from around €20 to €30 per month. However, there were marked differences between countries and subpopulations, and changes were only partially explained by COVID-19 burden. We discuss possible explanations for and implication of our findings.

Introducing artificial intelligence to the radiation early warning system

Although radiation level is a serious concern which requires continuous monitoring, many existing systems are designed to perform this task. Radiation early warning system (REWS) is one of these systems which monitor the gamma radiation level in air. Such system requires high manual intervention, depends totally on experts' analysis, and has some shortcomings that can be risky sometimes. In this paper, the approach called RIMI (refining incoming monitored incidents) will be introduced which aims to improve this system while becoming more autonomous with keeping the final decision to the experts. A new method is presented which will help in changing this system to become more intelligent while learning from past incidents of each specific system.

Development of an evidence-based ESCALATION system for recognition and response to paediatric clinical deterioration

AIM

The aim of this study was to develop an evidence-based paediatric early warning system for infants and children that takes into consideration a variety of paediatric healthcare contexts and addresses barriers to escalation of care.

METHODS

A three-stage intervention development framework consisted of Stage 1: evidence review, benchmarking, stakeholder (health professionals, decision-makers, and health consumers) engagement, and consultation; Stage 2: planning and coproduction by the researchers and stakeholders using action research cycles; and Stage 3: prototyping and testing.

RESULTS

A prototype evidence-based system incorporated human factor principles, used a structured approach to patient assessment, promoted situational awareness, and included family as well as clinician concern. Family involvement in detecting changes in their child's condition was supported by posters and flyers codesigned with health consumers. Five age-specific observation and response charts included 10 weighted variables and one unweighted variable (temperature) to convey a composite early warning score. The escalation pathway was supported by a targeted communication framework (iSoBAR NOW).

CONCLUSION

The development process resulted in an agreed uniform ESCALATION system incorporating a whole-system approach to promote critical thinking, situational awareness for the early recognition of paediatric clinical deterioration as well as timely and effective escalation of care. Incorporating family involvement was a novel component of the system.

Extraction-free detection of amphibian pathogens from water baths

Detecting and quantifying pathogens with quick, cost-efficient and sensitive methods is needed across disease systems for addressing pertinent epidemiological questions. Typical methods rely on extracting DNA from collected samples. Here we develop and test an extraction-free method from water bath samples that is both sensitive and efficient for 2 major amphibian pathogens-Batrachochytrium dendrobatidis and B. salamandrivorans. We tested mock samples with known pathogen quantities as well as comparatively assessed detection from skin swabs and water baths from field sampled amphibians. Quantitative PCR (qPCR) directly on lyophilized water baths was able to reliably detect low loads of 10 and 1 zoospores for both pathogens, and detection rates were greater than those of swabs from field samples. Further concentration of samples did not improve detection, and collection container type did not influence pathogen load estimates. This method of lyophilization (i.e. freeze-drying) followed by direct qPCR offers an effective and efficient tool from detecting amphibian pathogens, which is crucial for surveillance efforts and estimating shedding rates for robust epidemiological understanding of transmission dynamics. Furthermore, water bath samples have multiple functions and can be used to evaluate mucosal function against pathogens and characterize mucosal components. The multifunctionality of water bath samples and reduced monetary costs and time expenditures make this method an optimal tool for amphibian disease research and may also prove to be useful in other wildlife disease systems.

Analysis of Google Trends to monitor new psychoactive substance. Is there an added value?

The past decade has seen an increase in the development and availability of a broad category of drugs, known as new psychoactive substances (NPS). NPS are challenging for public health authorities, therefore the two major drug monitoring bodies - the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) and the United Nations Office on Drugs and Crime (UNODC) - have implemented the EU Early Warning System (EWS) and Early Warning Advisory (EWA), respectively. While these monitoring systems are informative, it is difficult to keep up with the constant and rapid developmental rate of NPS. The EMCDDA has recognised the need for an alternative and technologically derived early warning system. The aim of this research is to determine whether Google Trends and drug discussion forum data can be used to complement early warning systems for NPS. Forty-eight substances were used in this study and classed into groups based on their chemical structure, following the UNODC classification system. Google Trends data (time range: 2004-2019) and drug forum data (time range: 2003-2018) were extracted for each substance and visual trend profiles were created for class groups as well as individual substances. Analysis was conducted to determine when a substance first appeared on Google Trends and a drug discussion forum as well as their trends over time. This date of first appearance was then compared to the date the substance was first reported to UNODC. Of the three data sources utilised, substances were most likely to appear on Google Trends first. Amongst the different classes of NPS, discernible trends ('block', 'successive', and 'generational' trends) were observed. These trends reflect the evolution of the manufacture of substances or generations of substances that has been observed in the literature. For example, in the synthetic cannabinoids' category, a generational trend is observed that corresponds to the different generations of synthetic cannabinoids. When comparing Google Trends and Drugs-Forum directly, the order of appearance and duration of presence for substances aligns accurately for most classes. Google Trends showed the emergence, persistence, or transient nature of substances, which could direct the focus of law enforcement, health organisation and laboratory resources towards a limited number of substances. When one considers the reliance of individual information seeking on the Web as well as the prominence of NPS on the Web, it becomes clear that Google Trends and drug discussion forums could be used as a complement to current early warning systems.

Unravelling the early warning capability of wastewater surveillance for COVID-19: A temporal study on SARS-CoV-2 RNA detection and need for the escalation

Wastewater-based Epidemiological (WBE) surveillance offers a promising approach to assess the pandemic situation covering pre-symptomatic and asymptomatic cases in highly populated area under limited clinical tests. In the present study, we analyzed SARS-CoV-2 RNA in the influent wastewater samples (n = 43) from four wastewater treatment plants (WWTPs) in Gandhinagar, India, during August 7th to September 30th, 2020. A total of 40 samples out of 43 were found positive i.e. having at least two genes of SARS-CoV-2. The average Ct values for S, N, and ORF 1 ab genes were 32.66, 33.03, and 33.95, respectively. Monthly variation depicted a substantial rise in the average copies of N (~120%) and ORF 1 ab (~38%) genes in the month of September as compared to August, while S-gene copies declined by 58% in September 2020. The SARS-CoV-2 genome concentration was higher in the month of September (~924.5 copies/L) than August (~897.5 copies/L), corresponding to a ~2.2-fold rise in the number of confirmed cases during the study period. Further, the percentage change in genome concentration level on a particular date was found in the lead of 1-2 weeks of time with respect to the official confirmed cases registered based on clinical tests on a temporal scale. The results profoundly unravel the potential of WBE surveillance to predict the fluctuation of COVID-19 cases to provide an early warning. Our study explicitly suggests that it is the need of hour that the wastewater surveillance must be included as an integral part of COVID-19 pandemic monitoring which can not only help the water authorities to identify the hotspots within a city but can provide up to 2 weeks of time lead for better tuning the management interventions.

Emergency flood detection using multiple information sources: Integrated analysis of natural hazard monitoring and social media data

Extreme weather events are occurring more frequently as a result of climate change. In October 2019, eastern Japan was hit by Hagibis, a large and high-speed typhoon. This unprecedented typhoon caused the evacuation of over 4000 people, injured more than 300 people, and damaged more than 98,000 dwellings throughout the affected area. Because floods are one of the most devastating natural disasters in Asia, providing an effective early warning system (EWS) is critical to reducing disaster impacts. However, warnings based only on natural hazard monitoring do not offer sufficient protection. Integrating natural hazard monitoring and social media data could improve warning systems to enhance the awareness of disaster managers and citizens about emergency events. We analyzed time-series data including rainfall intensity, 90-min-effective rainfall, and river water level as well as Twitter data related to disaster events during the 5-day period from 11 to 15 October, focusing on the most affected areas in Japan. The analysis included more than 60,000 tweets. Our analysis confirmed the utility of the statistical approach of outbreak detection with social media data in the early detection and local identification of multiple-flood events, and the results from the municipality-level analyses show that tweet frequencies related to the flood disaster ontological categories were significantly correlated to temporal variations in the hazard monitoring data. Thus, flood detection at the administrative level using social media data combined with current hazard monitoring data can enable a decision-driven EWS design. Interactive approaches for decision-making and knowledge production should continue to be considered in the face of climate-change-induced disasters.

Surveillance of Wastewater for Early Epidemic Prediction (SWEEP): Environmental and health security perspectives in the post COVID-19 Anthropocene

The present work summarizes the major research findings related to wastewater-based epidemiology (WBE) study of COVID-19 and puts forward a conceptual framework, termed as "Surveillance of Wastewater for Early Epidemic Prediction (SWEEP)" for implementation of WBE. SWEEP framework is likely to tackle few practical issues related to WBE and simultaneously proposes refinements to the approach for better outcome and efficiency to save precious lives around the globe. It is observed that the present pandemic offers an opportunity for SWEEP to get included in routine urban water management to put the humankind at front to stop such pandemic in future or at least be prepared to fight against it. With global collaboration, SWEEP can be fine-tuned to meet diverse needs, making the present and future generations resilient to future viral outbreaks. Recent WBE studies conducted to check for the presence of SARS-CoV-2 in wastewater revealed that raw sewage samples tested positive to PCR-based assays while the treated samples showed absence of viral titers. Moreover, the lockdown had a positive impact on decreasing the viral loading in sewage. The proposed SWEEP protocol has an advantage over testifying individuals for predicting the stage of pandemic.

Farmers' decision to use drought early warning system in developing countries

Drought is a persistent, sluggish natural disaster in developing countries that has generated a financial burden and an unstable climate. Farmers should adopt early warning systems (EWS) in their strategies for monitoring drought to reduce its serious consequences. However, farmers in developing countries are reluctant to use EWS as their management strategies. Hence, the aim of this study was to investigate the decision of farmers to use climate knowledge through the model of farming activity in Kermanshah Township, Iran. A surveyor questionnaire was used to gather data from 370 wheat farmers using random sampling methods in multi-stage clusters. Results revealed that the decision to use climate information is affected by personal factors, attitude towards climate information, objectives of using climate information, and external/physical farming factors. The result of this study has implications for drought management practitioners. To be specific, the results can aid policymakers to design early alert programs to minimize the risk of drought and thus move from conventional to climate smart agriculture.

A modelling framework for developing early warning systems of COPD emergency admissions

Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of mortality worldwide and is a major contributor to the number of emergency admissions in the UK. We introduce a modelling framework for the development of early warning systems for COPD emergency admissions. We analyse the number of COPD emergency admissions using a Poisson generalised linear mixed model. We group risk factors into three main groups, namely pollution, weather and deprivation. We then carry out variable selection within each of the three domains of COPD risk. Based on a threshold of incidence rate, we then identify the model giving the highest sensitivity and specificity through the use of exceedance probabilities. The developed modelling framework provides a principled likelihood-based approach for detecting the exceedance of thresholds in COPD emergency admissions. Our results indicate that socio-economic risk factors are key to enhance the predictive power of the model.

Why do healthcare professionals fail to escalate as per the early warning system (EWS) protocol? A qualitative evidence synthesis of the barriers and facilitators of escalation

Background

Early warning systems (EWSs) are used to assist clinical judgment in the detection of acute deterioration to avoid or reduce adverse events including unanticipated cardiopulmonary arrest, admission to the intensive care unit and death. Sometimes healthcare professionals (HCPs) do not trigger the alarm and escalate for help according to the EWS protocol and it is unclear why this is the case. The aim of this qualitative evidence synthesis was to answer the question ‘why do HCPs fail to escalate care according to EWS protocols?’ The findings will inform the update of the National Clinical Effectiveness Committee (NCEC) National Clinical Guideline No. 1 Irish National Early Warning System (INEWS).

Methods

A systematic search of the published and grey literature was conducted (until February 2018). Data extraction and quality appraisal were conducted by two reviewers independently using standardised data extraction forms and quality appraisal tools. A thematic synthesis was conducted by two reviewers of the qualitative studies included and categorised into the barriers and facilitators of escalation. GRADE CERQual was used to assess the certainty of the evidence.

Results

Eighteen studies incorporating a variety of HCPs across seven countries were included. The barriers and facilitators to the escalation of care according to EWS protocols were developed into five overarching themes: Governance, Rapid Response Team (RRT) Response, Professional Boundaries, Clinical Experience, and EWS parameters. Barriers to escalation included: Lack of Standardisation, Resources, Lack of accountability, RRT behaviours, Fear, Hierarchy, Increased Conflict, Over confidence, Lack of confidence, and Patient variability. Facilitators included: Accountability, Standardisation, Resources, RRT behaviours, Expertise, Additional support, License to escalate, Bridge across boundaries, Clinical confidence, empowerment, Clinical judgment, and a tool for detecting deterioration. These are all individual yet inter-related barriers and facilitators to escalation.

Conclusions

The findings of this qualitative evidence synthesis provide insight into the real world experience of HCPs when using EWSs. This in turn has the potential to inform policy-makers and HCPs as well as hospital management about emergency response system-related issues in practice and the changes needed to address barriers and facilitators and improve patient safety and quality of care.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-021-00403-9.

Forecasting the probability of local dengue outbreaks in Queensland, Australia

The global incidence of dengue is increasing, and many previously unaffected areas have reported local cases of the vector-borne disease in recent years. For the effective containment of local outbreaks health authorities rely on the prompt notification of new cases. However, due to severe under-reporting and misdiagnosis, non-endemic countries face difficulties in containing local outbreaks, and the possibility of dengue becoming endemic. Outbreak control measures in non-endemic countries are largely reactive and health authorities would benefit from a universal early warning system that forecasts the probability of dengue outbreaks for given times and locations. We develop a model that establishes a link between pre- and post-border risk of dengue outbreaks. Specifically, we predict the probability of travellers importing dengue from other countries as well as the probability of those travellers causing local outbreaks. Our model can act as an early warning system, forecasting likely times and places of dengue outbreaks. We run our model for the Australian state of Queensland over a period of twelve years. Our results reveal the airports where dengue infected travellers are most likely to arrive and geographic locations associated with high outbreak probabilities. Our results can be used by health authorities to better utilise prevention and control resources and lead to the development of new prevention measures.

Detecting national human enteric disease outbreaks linked to animal contact in the United States of America

Enteric pathogens, such as non-typhoidal Salmonella, Campylobacter and Escherichia coli, can reside in the intestinal tract of many animals, including livestock, companion animals, small mammals and reptiles. Often, these animals can appear healthy; nonetheless, humans can become infected after direct or indirect contact, resulting in a substantial illness burden. An estimated 14% of the 3.2 million illnesses that occur in the United States of America (USA) each year from such enteric pathogens are attributable to animal contact. Surveillance for enteric pathogens in the USA includes the compilation and interpretation of both laboratory and epidemiologic data. However, the authors feel that a collaborative, multisectoral and transdisciplinary - or One Health - approach is needed for data collection and analysis, at every level. In addition, they suggest that the future of enteric illness surveillance lies in the development of improved technologies for pathogen detection and characterisation, such as genomic sequencing and metagenomics. In particular, using whole-genome sequencing to compare genetic sequences of enteric pathogens from humans, food, animals and the environment, can help to predict antimicrobial resistance among these pathogens, determine their genetic relatedness and identify outbreaks linked to a common source. In this paper, the authors describe three recent, multi-state human enteric illness outbreaks linked to animal contact in the USA and discuss how integrated disease surveillance was essential to outbreak detection and response. Additional datasharing between public health and animal health laboratories and epidemiologists at the local, national, regional and international level may help to improve surveillance for emerging animal and human health threats and lead to new opportunities for prevention.

Implementing the Risk Identification (RI) and Modified Early Obstetric Warning Signs (MEOWS) tool in district hospitals in Rwanda: a cross-sectional study

BACKGROUND

Despite reaching Millennium Development Goal (MDG) 3, the maternal mortality rate (MMR) is still high in Rwanda. Most deaths occur after transfer of patients with obstetric complications from district hospitals (DHs) to referral hospitals; timely detection and management may improve these outcomes. The RI and MEOWS tool has been designed to predict morbidity and decrease delay of transfer. Our study aimed: 1) to determine if the use of the RI and MEOWS tool is feasible in DHs in Rwanda and 2) to determine the role of the RI and MEOWS tool in predicting morbidity.

METHODS

A cross-sectional study enrolled parturient admitted to 4 district hospitals during the study period from April to July 2019. Data was collected on completeness rate (feasibility) to RI and MEOWS tool, and prediction of morbidity (hemorrhage, infection, and pre-eclampsia).

RESULTS

Among 478 RI and MEOWS forms used, 75.9% forms were fully completed suggesting adequate feasibility. In addition, the RI and MEOWS tool showed to predict morbidity with a sensitivity of 28.9%, a specificity of 93.5%, a PPV of 36.1%, a NPV of 91.1%, an accuracy of 86.2%, and a relative risk of 4.1 (95% Confidential Interval (CI), 2.4-7.1). When asked about challenges faced during use of the RI and MEOWS tool, most of the respondents reported that the tool was long, the staff to patient ratio was low, the English language was a barrier, and the printed forms were sometimes unavailable.

CONCLUSION

The RI and MEOWS tool is a feasible in the DHs of Rwanda. In addition, having moderate or high scores on the RI and MEOWS tool predict morbidity. After consideration of local context, this tool can be considered for scale up to other DHs in Rwanda or other low resources settings.

TRIAL REGISTRATION

This is not a clinical trial rather a quality improvement project. It will be registered retrospectively.

Assessment of electronic disease early warning system for improved disease surveillance and outbreak response in Yemen

BACKGROUND

Diseases Surveillance is a continuous process of data collection, analysis interpretation and dissemination of information for swift public health action. Recent advances in health informatics have led to the implementation of electronic tools to facilitate such critical disease surveillance processes. This study aimed to assess the performance of the national electronic Disease Early Warning System in Yemen (eDEWS) using system attributes: data quality, timeliness, stability, simplicity, predictive value positive, sensitivity, acceptability, flexibility, and representativeness, based on the Centres for Disease Control & Prevention (US CDC) standard indicators.

METHODS

We performed a mixed methods study that occurred in two stages: first, the quantitative data was collected from weekly epidemiological bulletins from 2013 to 2017, all alerts of 2016, and annual eDEWS reports, and then the qualitative method using in-depth interviews was carried out in a convergent strategy. The CDC guideline used to describe the following system attributes: data quality (reporting, and completeness), timeliness, stability, simplicity, predictive value positive, sensitivity, acceptability, flexibility and representativeness.

RESULTS

The finding of this assessment showed that eDEWS is a resilient and reliable system, and despite the conflict in Yemen, the system is still functioning and expanding. The response timeliness remains a challenge, since only 21% of all eDEWS alerts were verified within the first 24 h of detection in 2016. However, identified gaps did not affect the system's ability to identify outbreaks in the current fragile situation. Findings show that eDEWS data is representative, since it covers the entire country. Although, eDEWS covers only 37% of all health facilities, this represents 83% of all functional health facilities in all 23 governorates and all 333 districts.

CONCLUSION

The quality and timeliness of responses are major challenges to eDEWS' functionality, the eDEWS remains the only system that provides regular data on communicable diseases in Yemen. In particular, public health response timeliness needs improvement.

Development and validation of an analytical protocol for the sampling and quantitative analysis of ions on the surface of unstable historic glass in museum collections using ion-exchange chromatography

The early identification of unstable glass objects in museum collections is essential for their conservation, but as yet cannot be accomplished straightforwardly. Accordingly, this paper describes the development and validation of a simple protocol for quantitative determination of ions characteristic of the chemical decay of historic glass, using surface swabbing combined with ion-exchange chromatography. The establishment of a robust protocol is an important step in the development of an early warning system for the chemical deterioration of unstable glass. Using a model system, the protocol was validated for specificity, linearity, accuracy, precision, limits of detection, and limits of quantification for 10 anionic species (fluoride, acetate, formate, chloride, nitrite, bromide, nitrate, carbonate, sulfate and phosphate) and 6 cationic species (lithium, sodium, ammonium, potassium, magnesium and calcium). Good validation parameters (R² > 0.995; RSD < 5%; Recovery 90-100%) were obtained for acetate, formate, nitrite, nitrate, phosphate, lithium, sodium, potassium, magnesium and calcium. Chloride (R² = 0.934; RSD = 13.6%; recovery 71.4%) and carbonate (R² = 0.993; RSD = 10.3%; recovery 120%) had poor validation parameters. Sulfate had low recovery (78.2%), but high reproducibility (RSD = 4.32%) with R² = 0.997. Limits of quantification were below 1 mg/L for all analytes, which is satisfactory for the study of unstable glass in museum collections. The validated sampling protocol was trialled using artificially aged unstable glass fragments, which resulted in a high relative standard deviation (between 1 and 30%). The ability to achieve improved care of historic glass by application of the validated protocol in museum collections is discussed in the context of a pilot study undertaken at the Rijksmuseum, Amsterdam.

Paper-based devices for rapid diagnostics and testing sewage for early warning of COVID-19 outbreak

Coronavirus disease (COVID-19), caused by SARS-CoV-2, evolved into a global pandemic in 2020, and the outbreak has taken an enormous toll on individuals, families, communities and societies around the world. One practical and effective strategy is to implement rapid case identification based on a rapid testing to respond to this public health crisis. Currently, the available technologies used for rapid diagnostics include RT-PCR, RT-LAMP, ELISA and NGS. Still, due to their different limitations, they are not well suited for rapid diagnosis in a variety of locations. Paper-based devices are alternative approaches to achieve rapid diagnosis, which are cost-effective, highly selective, sensitive, portable, and easy-to-use. In addition to individual virus screening, wastewater-based epidemiology has been emerged to be an effective way for early warning of outbreak within the population, which tests viral genome sequence to reflect information on the spread and distribution of the virus because SARS-CoV-2 can be shed into wastewater through the feces and urine from infected population. In this paper, we describe paper-based device as a low-cost and rapid sensor for both diagnosis and testing of sewage for early warning of outbreak. More importantly, the device has great potential for real-time detection in the field, without any advanced facilities or well-trained and skilled personnel, and provides early warning or timely intervention of an outbreak of pandemic.

Willingness to pay for an early warning system for infectious diseases

Early warning systems for infectious diseases and foodborne outbreaks are designed with the aim of increasing the health safety of citizens. As a first step to determine whether investing in such a system offers value for money, this study used contingent valuation to estimate people's willingness to pay for such an early warning system in six European countries. The contingent valuation experiment was conducted through online questionnaires administered in February to March 2018 to cross-sectional, representative samples in the UK, Denmark, Germany, Hungary, Italy, and The Netherlands, yielding a total sample size of 3140. Mean willingness to pay for an early warning system was €21.80 (median €10.00) per household per month. Pooled regression results indicate that willingness to pay increased with household income and risk aversion, while they decreased with age. Overall, our results indicate that approximately 80-90% of people would be willing to pay for an increase in health safety in the form of an early warning system for infectious diseases and food-borne outbreaks. However, our results have to be interpreted in light of the usual drawbacks of willingness to pay experiments.

Fuzzy classifier model to know the sustainability of aquatic organisms and to forecast the aqua farmers

This research work focuses to develop a fuzzy classifier model, to know the sustainability of aquatic animals and/or plants by assessing the aqua pond water quality. This model categorizes the water quality of aqua pond into four levels like as normal, acceptable, abnormal, and dangerous based on the numerical values of physical limits. The developed model is useful to forecast the pond water quality by the aqua farmers to keep up within the acceptable limits at the earliest. Data collected from five ponds are used to develop the fuzzy classifier model. The output of this model is validated using a known set of sample data. This model yields high classification performance against variation in aqua pond water quality parameters and also provides the status of pond water continuously along with a remedy to keep up the water quality.

ReAFFIRM: Real-time Assessment of Flash Flood Impacts - a Regional high-resolution Method

Flash floods evolve rapidly in time, which poses particular challenges to emergency managers. One way to support decision-making is to complement models that estimate the flash flood hazard (e.g. discharge or return period) with tools that directly translate the hazard into the expected socio-economic impacts. This paper presents a method named ReAFFIRM that uses gridded rainfall estimates to assess in real time the flash flood hazard and translate it into the corresponding impacts. In contrast to other studies that mainly focus on individual river catchments, the approach allows for monitoring entire regions at high resolution. The method consists of the following three components: (i) an already existing hazard module that processes the rainfall into values of exceeded return period in the drainage network, (ii) a flood map module that employs the flood maps created within the EU Floods Directive to convert the return periods into the expected flooded areas and flood depths, and (iii) an impact assessment module that combines the flood depths with several layers of socio-economic exposure and vulnerability. Impacts are estimated in three quantitative categories: population in the flooded area, economic losses, and affected critical infrastructures. The performance of ReAFFIRM is shown by applying it in the region of Catalonia (NE Spain) for three significant flash flood events. The results show that the method is capable of identifying areas where the flash floods caused the highest impacts, while some locations affected by less significant impacts were missed. In the locations where the flood extent corresponded to flood observations, the assessments of the population in the flooded area and affected critical infrastructures seemed to perform reasonably well, whereas the economic losses were systematically overestimated. The effects of different sources of uncertainty have been discussed: from the estimation of the hazard to its translation into impacts, which highly depends on the quality of the employed datasets, and in particular on the quality of the rainfall inputs and the comprehensiveness of the flood maps.

Early warning system models and components in emergency and disaster: a systematic literature review protocol

BACKGROUND

Emergency and disaster are on the rise in the world. One of the most important components of disaster risk management is the early warning system. Studies have shown divergent models of warning systems with different structures. However, since no systematic review of early warning systems in disasters has been conducted so far, a systematic review of the models, components, and structures of these systems is essential. This protocol is a systematic review study, which aims to evaluate the existing warning systems and their structure.

METHODOLOGY

This study attempts to comprehensively search the previous studies with terms and expressions including disaster, emergency model, early warning system, and their synonyms at MESH. To this end, English articles, which have been published from 1980 to 2019, will be assessed. Google Scholar, PubMed, Web of Science, and Scopus databases as well as relevant specialized websites will be searched. Studies will be evaluated by two individuals independently.

DISCUSSION

To the best of our knowledge, no systematic review of models, structures, and components of the early warning system has been conducted so far. This study is the first attempt to comprehensively evaluate the models and components of early warning systems. Accordingly, this study will provide evidence of models, structures and elements of the early warning systems.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42018116111.

Reducing Wet Ammonium Deposition in Rocky Mountain National Park: the Development and Evaluation of A Pilot Early Warning System for Agricultural Operations in Eastern Colorado

Agricultural emissions are the primary source of ammonia (NH₃) deposition in Rocky Mountain National Park (RMNP), a Class I area, that is granted special air quality protections under the Clean Air Act. Between 2014 and 2016, the pilot phase of the Colorado agricultural nitrogen early warning system (CANEWS) was developed for agricultural producers to voluntarily and temporarily minimize emissions of NH₃ during periods of upslope winds. The CANEWS was created using trajectory analyses driven by outputs from an ensemble of numerical weather forecasts together with the climatological expertize of human forecasters. Here, we discuss the methods for the CANEWS and offer preliminary analyses of 33 months of the CANEWS based on atmospheric deposition data from two sites in RMNP as well as responses from agricultural producers after warnings were issued. Results showed that the CANEWS accurately predicted 6 of 9 high N deposition weeks at a lower-elevation observation site, but only 4 of 11 high N deposition weeks at a higher-elevation site. Sixty agricultural producers from 39 of Colorado's agricultural operations volunteered for the CANEWS, and a two-way line of communication between agricultural producers and scientists was formed. For each warning issued, an average of 23 producers responded to a postwarning survey. Over 75% of responding CANEWS participants altered their practices after an alert. While the current effort was insufficient to reduce atmospheric deposition, we were encouraged by the collaborative spirit between agricultural, scientific, and resource management communities. Solving a broad and complex social-ecological problem requires both a technological approach, such as the CANEWS, and collaboration and trust from all participants, including agricultural producers, land managers, university researchers, and environmental agencies.

Developing an early warning system of suicide using Google Trends and media reporting

BACKGROUND

Conventional surveillance systems for suicides typically suffer from a substantial time lag of six months to two years. This study aims to develop an early warning system of possible suicide outbreaks in Hong Kong using Google Trends and suicide-related media reporting.

METHODS

Data on 3,534 suicides from 2011 to 2015 were obtained from Hong Kong Census and Statistics Department, and the Coroner's Court. Using data from Google Trends and features extracted from media reporting on suicide news, we fitted Poisson regression models to predict the number and estimate the intensity of suicides on a weekly basis, for six subgroups, defined by gender and age. We adopted the cumulative sum (CUSUM) control chart-based method to identify outbreaks of suicide.

RESULTS

The proposed model was able to predict the number of suicides with reasonably low normalized root mean squared errors, ranging from 15.6% for young females to 24.16% for old females. The suicide intensity curves were well captured by the proposed models for young males and females, but not for other groups. The Sensitivity, Precision and F1 Score of the CUSUM-based method were 50%, 100% and 67% for young females, and 93%, 54% and 68% for young males.

LIMITATIONS

This study focused only on predicting the number of suicides in the current week, not in the future weeks. The model did not include social media, socioeconomic and climate data.

CONCLUSIONS

Our results indicate that Google Trends search terms and media reporting data may be valuable data sources for predicting possible outbreak of suicides in Hong Kong. The proposed system could support effective and targeted interventions.

Range-dependent thresholds for global flood early warning

Early warning systems (EWS) for river flooding are strategic tools for effective disaster risk management in many world regions. When driven by ensemble Numerical Weather Predictions (NWP), flood EWS can provide skillful streamflow forecasts beyond the monthly time scale in large river basins. Yet, effective flood detection is challenged by accurate estimation of warning thresholds that identify specific hazard levels along the entire river network and forecast horizon. This research describes a novel approach to estimate warning thresholds which retain statistical consistency with the operational forecasts at all lead times. The procedure is developed in the context of the Global Flood Awareness System (GloFAS). A 21-year forecast-consistent dataset is used to derive thresholds with global coverage and forecast range up to six weeks. These are compared with thresholds derived from ERA5, a state of the art atmospheric reanalysis used to run the baseline simulation for the years 1986-2017 and to give a best guess of the present hydrological states. Findings show that the use of constant thresholds for 30-day flood forecasting, as in the current operational GloFAS setup, is consistent throughout the entire forecast range in only 30% to 40% of the river network, depending on the flood return period. Findings show that range-dependent thresholds, of weekly duration, are a more suitable alternative to time-invariant thresholds, as they improve the model consistency as well as the skills in flood monitoring and early warning, particularly over longer forecasting range.

Odorous emission reduction from a waste landfill with an optimal protection system based on fuzzy logic

Effective landfill management and operation require an accurate evaluation of the occurrence and extent of odour emission events, which are among the main causes of resident complaints and concerns, in particular in densely urbanised areas. This paper proposes a fuzzy optimal protection system (FOPS) based on fuzzy logic to manage odour production from a municipal solid waste (MSW) landfill. The case study is a MSW landfill in an old quarry site located 6 km north-west of Naples city centre (Italy). The aim is to reduce the odour nuisance in the area surrounding the landfill where there are several sensitive receptors. FOPS is based on logical relationships between local atmospheric dynamics, number and intensity of odour pollution events detected by certain fixed receptors and odour emission rate via an optimal fuzzy approach. Such system allows to start, in real time, established mitigation actions required to further reduce the odorous emissions from the landfill itself (e.g. spraying of perfumed substances and activation of extraction wells), especially when weather conditions might not be favourable and cause by causing a higher odour perception. The fuzzy system was coupled with the air pollutant transport software CALPUFF to simulate the odour dispersion in the considered area taking into account both different odour emission rates and local weather conditions. FOPS results show that this approach can be very useful as, by measuring the odour concentrations, a significant reduction of the odour exceedances over the thresholds fixed, to minimise the olfactory harassment, was observed in the whole area studied.

Developing neural network models for early detection of cardiac arrest in emergency department

BACKGROUND

Automated surveillance for cardiac arrests would be useful in overcrowded emergency departments. The purpose of this study is to develop and test artificial neural network (ANN) classifiers for early detection of patients at risk of cardiac arrest in emergency departments.

METHODS

This is a single-center electronic health record (EHR)-based study. The primary outcome was the development of cardiac arrest within 24 h after prediction. Three ANN models were trained: multilayer perceptron (MLP), long-short-term memory (LSTM), and hybrid. These were compared to other classifiers including the modified early warning score (MEWS), logistic regression, and random forest. We used AUROC, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the comparison.

RESULTS

During the study period, there were a total of 374,605 ED visits and 2,910,321 patient status updates. The ANN models (MLP, LSTM, and hybrid) achieved higher AUROC (AUROC: 0.929, 0.933, and 0.936; 95% confidential interval: 0.926-0.932, 0.930-0.936, and 0.933-0.939, respectively) compared to the non-ANN models, and the hybrid model exhibited the best performance. The ANN classifiers displayed higher performance in most of the test characteristics when the threshold levels of the classifiers were fixed to display the same positive result as those at the three MEWS thresholds (score ≥ 3, ≥4, and ≥5), and when compared with each other.

CONCLUSIONS

The ANN improves upon MEWS and conventional machine learning algorithms for the prediction of cardiac arrests in emergency departments. The hybrid ANN model utilizing both baseline and sequence information achieved the best performance.