Wastewater early warning system for SARS-CoV-2 outbreaks and variants in a Coruña, Spain.

Wastewater-based epidemiology has been widely used as a cost-effective method for tracking the COVID-19 pandemic at the community level. Here we describe COVIDBENS, a wastewater surveillance program running from June 2020 to March 2022 in the wastewater treatment plant of Bens in A Coruña (Spain). The main goal of this work was to provide an effective early warning tool based in wastewater epidemiology to help in decision-making at both the social and public health levels. RT-qPCR procedures and Illumina sequencing were used to weekly monitor the viral load and to detect SARS-CoV-2 mutations in wastewater, respectively. In addition, own statistical models were applied to estimate the real number of infected people and the frequency of each emerging variant circulating in the community, which considerable improved the surveillance strategy. Our analysis detected 6 viral load waves in A Coruña with concentrations between 103 and 106 SARS-CoV-2 RNA copies/L. Our system was able to anticipate community outbreaks during the pandemic with 8-36 days in advance with respect to clinical reports and, to detect the emergence of new SARS-CoV-2 variants in A Coruña such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529 and BA.2) in wastewater with 42, 30, and 27 days, respectively, before the health system did. Data generated here helped local authorities and health managers to give a faster and more efficient response to the pandemic situation, and also allowed important industrial companies to adapt their production to each situation. The wastewater-based epidemiology program developed in our metropolitan area of A Coruña (Spain) during the SARS-CoV-2 pandemic served as a powerful early warning system combining statistical models with mutations and viral load monitoring in wastewater over time.

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.

Artificial intelligence in public health: the potential of epidemic early warning systems.

The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.

Wastewater pandemic preparedness: Toward an end-to-end pathogen monitoring program.

Molecular analysis of public wastewater has great potential as a harbinger for community health and health threats. Long-used to monitor the presence of enteric viruses, in particular polio, recent successes of wastewater as a reliable lead indicator for trends in SARS-CoV-2 levels and hospital admissions has generated optimism and emerging evidence that similar science can be applied to other pathogens of pandemic potential (PPPs), especially respiratory viruses and their variants of concern (VOC). However, there are substantial challenges associated with implementation of this ideal, namely that multiple and distinct fields of inquiry must be bridged and coordinated. These include engineering, molecular sciences, temporal-geospatial analytics, epidemiology and medical, and governmental and public health messaging, all of which present their own caveats. Here, we outline a framework for an integrated, state-wide, end-to-end human pathogen monitoring program using wastewater to track viral PPPs.

Impact of an Early Warning System Protocol, for Patients Admitted to the Medical Floors with SARS-COV2 Pneumonia, on ICU Admission.

BACKGROUND: COVID-19 placed a significant burden on the global healthcare system. Strain in critical care capacity has been associated with increased COVID-19-related ICU mortality. This study evaluates the impact of an early warning system and response team implemented on medical floors to safely triage and care for critically ill patients on the floor and preserve ICU capacity. METHODS: We conducted a multicenter, retrospective cohort study, comparing outcomes between intervention and control hospitals within a US eight-hospital urban network. Patients hospitalized with COVID-19 pneumonia between April 13th, 2020 and June 19th, 2020 were included in the study, which was a time of a regional surge of COVID-19 admissions. An automated, electronic early warning protocol to identify patients with moderate-severe hypoxemia on the medical floors and implement early interventions was implemented at one of the eight hospitals ("the intervention hospital"). RESULTS: Among 1024 patients, 403 (39%) were admitted to the intervention hospital and 621 (61%) were admitted to one of the control hospitals. Adjusted for potential confounders, patients at the intervention hospital were less likely to be admitted to the ICU (HR = 0.73, 95% CI 0.53, 1.000, P = .0499) compared to the control hospitals. Patients admitted from the floors to the ICU at the intervention hospital had shorter ICU stay (HR for ICU discharge: 1.74; 95% CI 1.21, 2.51, P = .003). There was no significant difference between intervention and control hospitals in need for mechanical ventilation (OR = 0.93; 95% CI 0.38, 2.31; P = .88) or hospital mortality (OR = 0.79; 95% CI 0.52, 1.18; P = .25). CONCLUSION: A protocol to conserve ICU beds by implementing an early warning system with a dedicated response team to manage respiratory distress on the floors reduced ICU admission and was not associated with worse outcomes compared to hospitals that managed similar levels of respiratory distress in the ICU.

The COVID-19 Pandemic's impact on sustainability and expansion of a Pediatric Early Warning System in resource-limited hospitals.

BACKGROUND: The COVID-19 pandemic impacted healthcare delivery worldwide, including pediatric cancer care, with a disproportionate effect in resource-limited settings. This study evaluates its impact on existing quality improvement (QI) programs. METHODS: We conducted 71 semi-structured interviews of key stakeholders at five resource-limited pediatric oncology centers participating in a collaborative to implement Pediatric Early Warning System (PEWS). Interviews were conducted virtually using a structured interview guide, recorded, transcribed, and translated into English. Two coders developed a codebook of a priori and inductive codes and independently coded all transcripts, achieving a kappa of 0.8-0.9. Thematic analysis explored the impact of the pandemic on PEWS. RESULTS: All hospitals reported limitations in material resources, reduction in staffing, and impacts on patient care due to the pandemic. However, the impact on PEWS varied across centers. Identified factors that promoted or limited ongoing PEWS use included the availability of material resources needed for PEWS, staff turnover, PEWS training for staff, and the willingness of staff and hospital leaders to prioritize PEWS. Consequently, some hospitals were able to sustain PEWS; others halted or reduced PEWS use to prioritize other work. Similarly, the pandemic delayed plans at all hospitals to expand PEWS to other units. Several participants were hopeful for future expansion of PEWS post-pandemic. CONCLUSION: The COVID-19 pandemic created challenges for sustainability and scale of PEWS, an ongoing QI program, in these resource-limited pediatric oncology centers. Several factors mitigated these challenges and promoted ongoing PEWS use. These results can guide strategies to sustain effective QI interventions during future health crises.

Distributed denial of service attack prediction: Challenges, open issues and opportunities

Distributed Denial of Service (DDoS) attack is one of the biggest cyber threats. DDoS attacks have evolved in quantity and volume to evade detection and increase damage. Changes during the COVID-19 pandemic have left traditional perimeter-based security measures vulnerable to attackers that have diversified their activities by targeting health services, e-commerce, and educational services. DDoS attack prediction searches for signals of attack preparation to warn about the imminence of the attack. Prediction is necessary to handle high-volumetric DDoS attacks and to increase the time to defend against them. This survey article presents the classification of studies from the literature comprising the current state-of-the-art on DDoS attack prediction. It highlights the results of this extensive literature review categorizing the works by prediction time, architecture, employed methodology, and the type of data utilized to predict attacks. Further, this survey details each identified study and, finally, it emphasizes the research opportunities to evolve the DDoS attack prediction state-of-the-art.

IoT Based Cytokine Storm Symptoms Early Warning System for Covid-19 Self-Isolating Patient

Covid-19, which has spread throughout the world, has reportedly caused millions of deaths. Among the causes of the patient's death is the phase after the patient is declared negative for COVID, but there is a cytokine storm. In this study, an IoT-based technology was proposed to be able to detect abnormalities in COVID-19 patients, even though they already had a negative Covid status based on the PCR test. The implementation of this technology allows former Covid patients to be monitored from anywhere as long as they are connected to the internet, using designed wearable devices and dedicated mobile apps for them. Based on experiment result, all the sensors have the ability to work and sense patient body indicators with error below 5%. This study demonstrated the flawless use of a mobile app dedicated to monitor patients' health during the pandemic. When patient health condition indicating exposed to cytokine storm, a warning notification is appear at the mobile app. © 2022 IEEE.

COVID-19 surveillance in wastewater: An epidemiological tool for the monitoring of SARS-CoV-2.

The coronavirus disease 2019 (COVID-19) pandemic has prompted a lot of questions globally regarding the range of information about the virus's possible routes of transmission, diagnostics, and therapeutic tools. Worldwide studies have pointed out the importance of monitoring and early surveillance techniques based on the identification of viral RNA in wastewater. These studies indicated the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in human feces, which is shed via excreta including mucus, feces, saliva, and sputum. Subsequently, they get dumped into wastewater, and their presence in wastewater provides a possibility of using it as a tool to help prevent and eradicate the virus. Its monitoring is still done in many regions worldwide and serves as an early "warning signal"; however, a lot of limitations of wastewater surveillance have also been identified.

Challenges to Sustainability of Pediatric Early Warning System (Pews) in Resource-Limited Hospitals in Latin America

BACKGROUND AND AIM: Pediatric Early Warning Systems (PEWS) are evidence-based interventions that improve early identification of deterioration in resource-limited hospitals. While PEWS can be successfully implemented in these settings, little is known about their sustainability postimplementation. This study evaluates staff perspectives on the importance of, and challenges to, sustaining PEWS. METHOD(S): We conducted semi-structured interviews of PEWS implementation leaders and hospital directors at 5 pediatric oncology centers sustaining PEWS in Latin America. Interviews were conducted in Spanish, transcribed, and translated into English. A code book was developed combining a priori and inductively derived codes. Transcripts were independently coded by 2 coders achieving a kappa of 0.8-0.9. Thematic content analyses explored staff perceptions on PEWS sustainability. RESULT(S): We interviewed 71 staff including physicians (45%), nurses (45%), and administrators (10%). Participants emphasized the importance of sustaining PEWS for continued patient benefit. However, participants reported a range of challenges sustaining PEWS, including fluctuations in human and material resources needed for PEWS, staff turnover and insufficient training, difficulty achieving new leadership buy-in, lack of internal systems to promote ongoing monitoring of PEWS, and the COVID-19 pandemic (Table 1). Together, these challenges resulted in multiple impacts, ranging from a small reduction in PEWS quality to complete disruption of PEWS use resulting in loss of benefits to patient outcomes in some units. CONCLUSION(S): While sustainability of evidence-based interventions like PEWS is valued by staff in resourcelimited hospitals, participants reported multiple challenges to sustainability resulting in reduced patient benefit. Future work should focus on identifying factors that promote intervention sustainability in these settings. (Table Presented).

Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review.

BACKGROUND: Global pandemics have occurred with increasing frequency over the past decade reflecting the sub-optimum operationalization of surveillance systems handling human health data. Despite the wide array of current surveillance methods, their effectiveness varies with multiple factors. Here, we perform a systematic review of the effectiveness of alternative infectious diseases Early Warning Systems (EWSs) with a focus on the surveillance data collection methods, and taking into consideration feasibility in different settings. METHODS: We searched PubMed and Scopus databases on 21 October 2022. Articles were included if they covered the implementation of an early warning system and evaluated infectious diseases outbreaks that had potential to become pandemics. Of 1669 studies screened, 68 were included in the final sample. We performed quality assessment using an adapted CASP Checklist. RESULTS: Of the 68 articles included, 42 articles found EWSs successfully functioned independently as surveillance systems for pandemic-wide infectious diseases outbreaks, and 16 studies reported EWSs to have contributing surveillance features through complementary roles. Chief complaints from emergency departments' data is an effective EWS but it requires standardized formats across hospitals. Centralized Public Health records-based EWSs facilitate information sharing; however, they rely on clinicians' reporting of cases. Facilitated reporting by remote health settings and rapid alarm transmission are key advantages of Web-based EWSs. Pharmaceutical sales and laboratory results did not prove solo effectiveness. The EWS design combining surveillance data from both health records and staff was very successful. Also, daily surveillance data notification was the most successful and accepted enhancement strategy especially during mass gathering events. Eventually, in Low Middle Income Countries, working to improve and enhance existing systems was more critical than implementing new Syndromic Surveillance approaches. CONCLUSIONS: Our study was able to evaluate the effectiveness of Early Warning Systems in different contexts and resource settings based on the EWSs' method of data collection. There is consistent evidence that EWSs compiling pre-diagnosis data are more proactive to detect outbreaks. However, the fact that Syndromic Surveillance Systems (SSS) are more proactive than diagnostic disease surveillance should not be taken as an effective clue for outbreaks detection.

Can the modified ESG-KMV logit model explain the default risk of internet finance companies?

With the rapid development of internet finance in China, the risk management of internet finance has become an urgent issue. This study analyzes the factors that affect the default risk of Chinese internet finance companies based on measuring the distance to default of companies. This study incorporates ESG rating into the evaluation model to comprehensively reflect the default risk factors. The traditional KMV model is modified with ESG rating, and results are used to construct the panel logit model. Based on internet finance firms listed on China A-Shares data from 2016 to 2020, our results show the following: first, the modified ESG-KMV logit model can effectively analyze the influencing factors of the internet finance default risk. Second, ROE, accounts receivable turnover ratio, asset-liability ratio and z-value are important factors that affect the default risk of internet finance companies. Third, it is also found that COVID-19 has significantly impacted the default risk of internet finance companies. As a policy implication, the regulator can incorporate ESG into the measurement of the default risk to create more awareness among internet finance companies on the importance of the environment and sustainability to human societies. Copyright © 2022 Zeng, Lau and Abdul Bahri.

Surveillance of SARS-CoV-2 in Urban Wastewater in Italy

The presence of SARS-CoV-2 RNA in wastewaters was demonstrated early into the COVID-19 pandemic. Data on the presence of SARS-CoV-2 in urban wastewater can be exploited for different aims, including: i) description of outbreaks trends, ii) early warning system for new COVID-19 outbreaks or for the spread of the virus in new territories, iii) study of SARS-Co V-2 genetic diversity and detection of its variants, and iv) estimating the prevalence of COVID-19 infections. Therefore, wastewater surveillance (known as Wastewater Based Epidemiology, WBE) can be a powerful tool to support the decision-making process on public health measures. Italy was among the first EU countries investigating the occurrence and concentration of SARS-Co V-2 RNA in urban wastewaters, virus detection being accomplished at an early phase of the epidemic, between February and May 2020 in north and central Italy. The present study reports on the methodological issues, related to sample data collection and management, encountered in establishing the systematic, wastewater-based SARS-CoV-2 surveillance, and describes the results of the first six months of surveillance. © 2022 IEEE.

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.

Practitioner Attitudes towards an Early Warning System : From Professional Distraction to Relational Support

As the general population ages and life expectancy increases in the United States, demand for virtual health care is on the rise. Undoubtedly, the next several decades will see increases in automated patient care and use of data-driven warning systems, trends which have already accelerated in the wake of Covid-19. Thus, understanding how traditionally trained healthcare practitioners respond to predictive analytics, like early warning systems, is vital for their successful implementation in the future. © 2021 IEEE.

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.

Pulmonary Illness Detection Early Warning System

Chronic Obstructive Pulmonary Disease is the 2nd most common genesis of Non-Communicable Diseases (NCD)-related deaths in India. Not everyone had the chance to go to a medical facility or hospital for problems/diseases other than COVID-19 amidst lockdown as there was uncertainty of getting infected by COVID-19. To cater this issue this device/software can detect and diagnose diseases such as pneumonia, heart failure, chronic obstructive pulmonary disease (COPD), emphysema, asthma, bronchitis, foreign body in the lungs or airways etc. This process uses methodology of signal, sound and audio processing and image analysis. Normal sound samples of healthy human body would be taken in consideration and then be compared with the samples of the person whom it is tested on, different levels or frequency range of sounds/body noises that a person makes differs in different analysis, for example 'crackles' these are high pitched breath sounds made when the small air sacs get liquid filled and the person may have pneumonia or a heart failure. This not only work as a warning system that is early but also can reduce human workload and can deplete human error while using a stethoscope for the same. © 2021 IEEE.

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.

Global media as an early warning tool for food fraud;an assessment of MedISys-FF

Food fraud is a serious problem that may compromise the safety of the food products being sold on the market. Previous studies have shown that food fraud is associated with a large variety of food products and the fraud type may vary from deliberate changing of the food product (i.e. substitution, tampering, dilution etc.) to the manipulation of documents. It is therefore important that all actors within the food supply chain (food producers, authorities), have methodologies and tools available to detect fraudulent products at an early stage so that preventative measures can be taken. Several such systems exist (i.e. iRASFF, EMA, HorizonScan, AAC-FF, MedISys-FF), but currently only MedISys-FF is publicly online available. In this study, we analyzed food fraud cases collected by MedISys-FF over a 6-year period (2015–2020) and show global trends and developments in food fraud activities. In the period investigated, the system has collected 4375 articles on food fraud incidents from 164 countries in 41 different languages. Fraud with meat and meat products were most frequently reported (27.7%), followed by milk and milk products (10.5%), cereal and bakery products (8.3%), and fish and fish products (7.7%). Most of the fraud was related to expiration date (58.3%) followed by tampering (22.2%) and mislabeling of country of origin (11.4%). Network analysis showed that the focus of the articles was on food products being frauded. The validity of MedISys-FF as an early warning system was demonstrated with COVID-19. The system has collected articles discussing potential food fraud risks due to the COVID-19 crisis. We therefore conclude that MedISys-FF is a very useful tool to detect early trends in food fraud and may be used by all actors in the food system to ensure safe, healthy, and authentic food.

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.