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Background Workplace absenteeism increases significantly during influenza epidemics. Sick leave records may facilitate more timely detection of influenza outbreaks, as trends in increased sick leave may precede alerts issued by sentinel surveillance systems by days or weeks. Sick leave data have not been comprehensively evaluated in comparison to traditional surveillance methods. The aim of this paper is to study the performance and the feasibility of using a detection system based on sick leave data to detect influenza outbreaks. Methods Sick leave records were extracted from private French health insurance data, covering on average 209,932 companies per year across a wide range of sizes and sectors. We used linear regression to estimate the weekly number of new sick leave spells between 2016 and 2017 in 12 French regions, adjusting for trend, seasonality and worker leaves on historical data from 2010 to 2015. Outbreaks were detected using a 95%-prediction interval. This method was compared to results from the French Sentinelles network, a gold-standard primary care surveillance system currently in place. Results Using sick leave data, we detected 92% of reported influenza outbreaks between 2016 and 2017, on average 5.88 weeks prior to outbreak peaks. Compared to the existing Sentinelles model, our method had high sensitivity (89%) and positive predictive value (86%), and detected outbreaks on average 2.5 weeks earlier. Conclusion Sick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.

Surveillance, whether active or passive, is a dynamic process. It is fundamental to public health decision-making and subsequent action. Choice of diseases for surveillance, development of methods, ongoing systematic evaluation and dissemination to those who need to know, are each components which require expert, knowledgeable attention. The communication age will greatly redefine approaches to surveillance, both for data acquisition and dissemination. Especially in the dissemination area, the public health community needs to strengthen its capacity

Academic frauds, dishonesty and cheating are pervasive in Pakistan, but thus far less systematic research has been undertaken on the effectiveness of the policies designed for countering academic dishonesty. Generally, the success of a policy depends on a good design and appropriate implementation. The design aspect of the policies to counter academic dishonesty in Pakistan is studied elsewhere, the purpose of this article is to empirically examine the implementation stages of the same policies. In doing so, the study utilizes a panel data set comprising of 60 cross-sections and 06 time series observations (2012–2017). Results of the rigorous panel regression models shows that leniency in the prescribed punishments and an exhaustive belief on the efficacy of traditional examination surveillance methods are counterproductive. The study however failed to substantiate the direct relationship of discrimination and academic dishonesty in the sample. It is therefore concluded that prescribed punishments must be awarded in letter and spirit and that there is need for coordination at different stages of the implementation. Further, the study also recommends that traditional forms of examination surveillance must be augmented using modern technology.

Hospital-onset COVID-19 infections (HOCIs) are associated with excess morbidity and mortality in patients and healthcare workers. The aim of this review was to explore and describe the current literature in HOCI surveillance. Medline, EMBASE, the Cochrane Database of Systematic Reviews, the Cochrane Register of Controlled Trials, and MedRxiv were searched up to 30 November 2020 using broad search criteria. Articles of HOCI surveillance systems were included. Data describing HOCI definitions, HOCI incidence, types of HOCI identification surveillance systems, and level of system implementation were extracted. A total of 292 citations were identified. Nine studies on HOCI surveillance were included. Six studies reported on the proportion of HOCI among hospitalized COVID-19 patients, which ranged from 0 to 15.2%. Six studies provided HOCI case definitions. Standardized national definitions provided by the UK and US governments were identified. Four studies included healthcare workers in the surveillance. One study articulated a multimodal strategy of infection prevention and control practices including HOCI surveillance. All identified HOCI surveillance systems were implemented at institutional level, with eight studies focusing on all hospital inpatients and one study focusing on patients in the emergency department. Multiple types of surveillance were identified. Four studies reported automated surveillance, of which one included real-time analysis, and one included genomic data. Overall, the study quality was limited by the observational nature with short follow-up periods. In conclusion, HOCI case definitions and surveillance methods were developed pragmatically. Whilst standardized case definitions and surveillance systems are ideal for integration with existing routine surveillance activities and adoption in different settings, we acknowledged the difficulties in establishing such standards in the short-term.

The epidemiology of human malaria differs considerably between and within geographic regions due, in part, to variability in mosquito species behaviours. Recently, the WHO emphasised stratifying interventions using local surveillance data to reduce malaria. The usefulness of vector surveillance is entirely dependent on the biases inherent in the sampling methods deployed to monitor mosquito populations. To understand and interpret mosquito surveillance data, the frequency of use of malaria vector collection methods was analysed from a georeferenced vector dataset (> 10,000 data records), extracted from 875 manuscripts across Africa, the Americas and the Asia-Pacific region. Commonly deployed mosquito collection methods tend to target anticipated vector behaviours in a region to maximise sample size (and by default, ignoring other behaviours). Mosquito collection methods targeting both host-seeking and resting behaviours were seldomly deployed concurrently at the same site. A balanced sampling design using multiple methods would improve the understanding of the range of vector behaviours, leading to improved surveillance and more effective vector control.

SARS-CoV-2, the virus that causes COVID-19, is constantly mutating, leading to new variants (1). Variants have the potential to affect transmission, disease severity, diagnostics, therapeutics, and natural and vaccine-induced immunity. In November 2020, CDC established national surveillance for SARS-CoV-2 variants using genomic sequencing. As of May 6, 2021, sequences from 177,044 SARS-CoV-2-positive specimens collected during December 20, 2020-May 6, 2021, from 55 U.S. jurisdictions had been generated by or reported to CDC. These included 3,275 sequences for the 2-week period ending January 2, 2021, compared with 25,000 sequences for the 2-week period ending April 24, 2021 (0.1% and 3.1% of reported positive SARS-CoV-2 tests, respectively). Because sequences might be generated by multiple laboratories and sequence availability varies both geographically and over time, CDC developed statistical weighting and variance estimation methods to generate population-based estimates of the proportions of identified variants among SARS-CoV-2 infections circulating nationwide and in each of the 10 U.S. Department of Health and Human Services (HHS) geographic regions.* During the 2-week period ending April 24, 2021, the B.1.1.7 and P.1 variants represented an estimated 66.0% and 5.0% of U.S. SARS-CoV-2 infections, respectively, demonstrating the rise to predominance of the B.1.1.7 variant of concern† (VOC) and emergence of the P.1 VOC in the United States. Using SARS-CoV-2 genomic surveillance methods to analyze surveillance data produces timely population-based estimates of the proportions of variants circulating nationally and regionally. Surveillance findings demonstrate the potential for new variants to emerge and become predominant, and the importance of robust genomic surveillance. Along with efforts to characterize the clinical and public health impact of SARS-CoV-2 variants, surveillance can help guide interventions to control the COVID-19 pandemic in the United States.

Executive SummaryIn 2020 a group of U.S. healthcare leaders formed the National Organization to Prevent Hospital-Acquired Pneumonia (NOHAP) to issue a call to action to address non–ventilator-associated hospital-acquired pneumonia (NVHAP). NVHAP is one of the most common and morbid healthcare-associated infections, but it is not tracked, reported, or actively prevented by most hospitals. This national call to action includes (1) launching a national healthcare conversation about NVHAP prevention; (2) adding NVHAP prevention measures to education for patients, healthcare professionals, and students; (3) challenging healthcare systems and insurers to implement and support NVHAP prevention; and (4) encouraging researchers to develop new strategies for NVHAP surveillance and prevention. The purpose of this document is to outline research needs to support the NVHAP call to action. Primary needs include the development of better models to estimate the economic cost of NVHAP, to elucidate the pathophysiology of NVHAP and identify the most promising pathways for prevention, to develop objective and efficient surveillance methods to track NVHAP, to rigorously test the impact of prevention strategies proposed to prevent NVHAP, and to identify the policy levers that will best engage hospitals in NVHAP surveillance and prevention. A joint task force developed this document including stakeholders from the Veterans’ Health Administration (VHA), the U.S. Centers for Disease Control and Prevention (CDC), The Joint Commission, the American Dental Association, the Patient Safety Movement Foundation, Oral Health Nursing Education and Practice (OHNEP), Teaching Oral-Systemic Health (TOSH), industry partners and academia.

Group A rotaviruses (RVAs) have been introduced as the most important causative agents of acute gastroenteritis in the young children. One of every 260 children born globally will die due to rotavirus (RV) before 5 years old. The RV is widely known as a viral indicator for health (fecal contamination) because this pathogen has a high treatment resistance nature, which has been listed as a relevant waterborne pathogen by the World Health Organization (WHO). Therefore, monitoring of environmental is important, and RV is one of the best-known indicators for monitoring. It has been proved that common standards for microbiological water quality do not guarantee the absence of viruses. On the other hand, in order to recover and determine RV quantity within water, standard methods are scarce. Therefore, dependable prediction of RV quantities in water sample is crucial to be able to improve supervision efficiency of the treatment procedure, precise quantitative evaluation of the microbial risks as well as microbiological water safety. Hence, this study aimed to introduce approaches to detecting and controlling RV in environmental waters, and discussed the challenges faced to enable a clear perception on the ubiquity of the RV within different types of water across the world.

To date, the only robust estimates of severe malaria cases include children who present to the formal healthcare system. It is a challenge to use these data because of varying age ranges of reporting, different diagnosis techniques, surveillance methods, and healthcare utilization. This analysis examined data from 37 Demographic and Health Surveys and Malaria Indicator Surveys across 19 countries in sub-Saharan Africa collected between 2011 and 2018. The outcome of interest is a proxy indicator for severe malaria, defined as a proportion of children aged 6–59 months with at least one self-reported symptom of severe illness including loss of consciousness, rapid breathing, seizures, or severe anemia (hemoglobin < 5 g/dL) among those who were positive for malaria. The study includes a weighted descriptive, country-level analysis and a multilevel mixed-effects logistic regression model to assess the determinants of severe malaria. Among children positive for malaria across all surveys, 4.5% (95% CI: 4.1–4.8) had at least one sign or symptom of severe malaria, which was significantly associated with age, residence, wealth, and year of survey fieldwork at a P-value less than 0.05. This analysis presents a novel and an alternative approach of estimating the fraction of severe malaria cases among malaria-positive children younger than 5 years in malaria-endemic countries. Estimating severe malaria cases through population-based surveys allows countries to estimate severe malaria across time and to compare with other countries. Having a population-level estimate of severe malaria cases helps further our understanding of the burden and epidemiology of severe malaria.

Background: The spreading of health-related rumors can profoundly put society at risk, and the investigation of strategies and methods can efficiently prevent the dissemination of hazardous rumor is necessary, especially during a public health emergency including disease outbreaks. In this article we review the studies that implicated the surveillance system in identifying rumors and discuss the different aspects of current methods in this field. Methods: We searched PubMed, EMBASE, Scopus, and Web of Science databases for relevant publications in English from 2000 to 2020. The PICOS approach was used to select articles, and two reviewers extracted the data. Findings were categorized as a source of rumors, type of systems, data collection, and data transmission methods. The quality of the articles was assessed using the Mixed Method Appraisal Tool (MMAT) checklist. Results: Five studies that presented the methods used for rumor detection in different outbreaks were included in the critical appraisal process. Findings were grouped into four categories: source of rumors, type of systems, data collection, and data transmission methods. The source of rumors in most studies was media, including new social and traditional media. The most used data collection methods were human-computer interaction technique, and automatic and manual methods each were discussed in one study. Also, the data transmission method was asynchronous in the majority of studies. Conclusion: Based on our findings, the most common rumor detection systems used in the outbreaks were manual and/or human-computer methods which are considered to be time-consuming processes. Due to the ever-increasing amount of modern social media platforms and the fast-spreading of misinformation in the times of outbreaks, developing the automatically and real-time tools for rumor detection is a vital need.

Wastewater-based surveillance methods have been implemented in several countries as a tool for monitoring SARS-CoV-2 at a community scale. A variety of methods have been used for concentrating, extracting, and detecting the virus, with no clear consensus on the most effective approach. In this note, we report preliminary findings from a study that is tracking SARS-CoV-2 in wastewater in Halifax, Nova Scotia, with a specific focus on the use of four reverse transcriptase quantitative PCR (RT-qPCR) assays for detecting the virus in wastewater. We were able to detect the virus in wastewater samples during the initial rise of cases in the Halifax region in early November 2020. Levels of the targeted SARS-CoV-2 gene fragments increased and fell in response to reported cases of COVID-19. The CDC N1 and E RT-qPCR assays demonstrated greater relative sensitivity than the CDC N2 and N3 assays for detection of SARS-CoV-2 in raw sewage samples.

Public health guidelines on physical activity (PA) establish national policy agendas and provide the basis for setting goals and targets. Advances in measurement and resulting new scientific findings lead to evolution of PA guidelines. PA surveillance serves to track compliance with national guidelines, usually expressed as the proportion of the population ‘meeting’ the main quantitative guidelines. The WHO recently completed a process to review and update the global PA guidelines. Changes to the guidelines, such as removal of a 10-min bout criterion, pose challenges for PA surveillance. We review the evolution of PA guidelines and associated surveillance methods and explore implications of the updated guidelines for changes in population surveillance and opportunities for technological approaches to PA to enhance surveillance.

In this letter, the authors propose a novel approach to develop an intelligent automated real-time surveillance system based solely on efficient tracking and facial recognition. With this architecture, we aim at compensating for the deficiencies of face-recognition-based surveillance methods that are computationally intractable or unreliable to be deployed for real-time automated surveillance. The presented work aims to construct a standalone video surveillance system, which is capable of detecting, identifying, and tracking persons in a video feed, snapshotting interesting physical feature changes, and transmitting this information along with the snapshots and identification parameter to the user. Specifically, we adopt a computationally cheap and accurate object tracker Deep SORT and combine it with a very accurate and computationally heavy facial recognition model FaceNet. The proposed method shows a 115% increase in runtime for the aforementioned combined architecture. The system also consists of a smartphone interface for efficient searching and indexing of identifications across timestamps.

Background In 2018, Philadelphia identified an outbreak of new human immunodeficiency virus (HIV) infections among persons who inject drugs (PWID). Although conventional HIV surveillance systems capture individual-level behavioral risk, they are not able to capture the social and environmental factors contributing to rapid transmission. Methods HIV surveillance data were used to assess demographic, clinical, and behavioral factors for PWID with HIV diagnosed during 2017 and 2018. Social factors such as homelessness, disruption of encampments, and trends in sexual behaviors, drug use and syringe availability among PWID were captured through National HIV Behavioral Surveillance, routine hepatitis and sexually transmitted infection surveillance, and shelter and homeless outreach data. Results In 2018, there were 71 new infections among PWID, an increase of 115% since 2016. During this time, opioid overdose deaths peaked at 59 deaths per 100 000 persons, 85% of which involved the use of fentanyl. While overall reported homelessness increased, rates of those living unsheltered rose by 13%. The Philadelphia Department of Public Health identified increased injection frequency, encampment closures, and lack of syringe access as promoters of continued HIV transmission. Conclusion The use of conventional surveillance methods only is inadequate for determining HIV risk during outbreaks. Incorporation of individual and aggregate level data on social and environmental determinants is necessary to develop effective outbreak response interventions.

Background Currently there are only two population studies on sepsis incidence in Asia. The burden of sepsis in Hong Kong is unknown. We developed a sepsis surveillance method to estimate sepsis incidence from a population electronic health record (EHR) in Hong Kong using objective clinical data. The study objective was to assess our method’s performance in identifying sepsis using a retrospective cohort. We compared its accuracy to administrative sepsis surveillance methods such as Angus’ and Martin’s methods. Method In this single centre retrospective study we applied our sepsis surveillance method on adult patients admitted to a tertiary hospital in Hong Kong. Two clinicians independently reviewed the clinical notes to determine which patients had sepsis. Performance was assessed by sensitivity, specificity, positive predictive value, negative predictive value and area under the curve (AUC) of Angus’, Martin’s and our surveillance methods using clinical review as “gold standard.” Results Between January 1 and February 28, 2018, our sepsis surveillance method identified 1352 adult patients hospitalised with suspected infection. We found that 38.9% (95%CI 36.3–41.5) of these patients had sepsis. Using a 490 patient validation cohort, two clinicians had good agreement with weighted kappa of 0.75 (95% CI 0.69–0.81) before coming to consensus on diagnosis of uncomplicated infection or sepsis for all patients. Our method had sensitivity 0.93 (95%CI 0.89–0.96), specificity 0.86 (95%CI 0.82–0.90) and an AUC 0.90 (95%CI 0.87–0.92) when validated against clinician review. In contrast, Angus’ and Martin’s methods had AUCs 0.56 (95%CI 0.53–0.58) and 0.56 (95%CI 0.52–0.59), respectively. Conclusions A sepsis surveillance method based on objective data from a population EHR in Hong Kong was more accurate than administrative methods. It may be used to estimate sepsis population incidence and outcomes in Hong Kong. Trial registration This study was retrospectively registered at clinicaltrials.gov on October 3, 2019 (NCT04114214).

This paper focuses on identifying key legal considerations and developments in the area of surveillance in Europein human rights, with its emphasis on the jurisprudence of the European Court of Human Rights. The aim of this research was to enhance and align law and practices in this area in Russiaand Europe. The author analysed the core and most novel Court cases that may be applicable to the subject matter, including by analogy, as well as the latest research in this area. This paper considers, inter alia, ability to challenge relevant law and practices in abstracto, legitimate aims justifying interference, the requirements for the relevant laws, fetters to authorities’ discretion on surveillance matters, and appropriate nature of supervision by authorities and the scope of their powers, as well as certain other safeguards. This paper also discusses interactions and balances between freedom and security, modern approaches taken by the EU and theUS, and tensions on pervasive surveillance matters. This paper reveals that, in a COVID-19 world, with those privacy issues that arise from the “track and trace” system and similar practices having already been widely scrutinised by the courts, it is possible to fight COVID-19 through surveillance methods with minimum interference with human rights. Key considerations outlined in this paper are pertinent to all sorts of surveillance features in the modern world. This paper should serve as an impetus for enhancing human rights protection through case law and legal framework in this area, with a view to strengthen democratic values without compromising health and safety concerns.

This paper explores the extent to which current interpretations of the notion of agency, as traditionally perceived under human rights law, pose challenges to human rights protection in light of algorithmic surveillance. After examining the notion of agency under the European Convention on Human Rights as a criterion for applications' admissibility, the paper looks into the safeguards of notification and of redress – crucial safeguards developed by the Court in secret surveillance cases – which are used as examples to illustrate their insufficiency in light of algorithmic surveillance. The use of algorithms creates new surveillance methods and challenges fundamental presuppositions on the notion of agency in human rights protection. Focusing on the victim status does not provide a viable solution to problems arising from the use of Artificial Intelligence in state surveillance. The paper thus raises questions for further research concluding that a new way of thinking about agency for the protection of human rights in the context of algorithmic surveillance is needed in order to offer effective protection to individuals.

Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance. We present an overview of recent advances in spatiotemporal disease surveillance for NCDs, using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and, through a simulation study, we compare their performance. We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public.

Introduction: Mosquitoes of the genus Aedes transmit dengue, Zika and chikungunya viruses, and the incidence of these diseases is increasing in the Pacific. This can be attributed to increased movement of people and goods, unplanned urbanisation, and global warming, among other factors. As vaccines are unavailable, we rely on vector control programs to prevent disease transmission. This study aimed to evaluate current practice in vector control and surveillance in 10 Pacific Island countries and identify evidence-based vector control interventions and surveillance methods for use in these countries. Methods: This study was conducted in preparation for TechCamp New Zealand, 24-26 January 2018, which aimed to work with stakeholders from 10 Pacific nations to reduce the spread of vector-borne diseases in the region. We conducted a literature review of published reviews and meta-analyses evaluating Aedes control and surveillance to find methods appropriate for use in Pacific Island countries. We collected information regarding current Aedes mosquito control and surveillance practice in 10 Pacific countries from TechCamp participants, through a survey, presentation and interview. Findings: Findings include evidence-based vector control interventions and surveillance methods, and current vector control and surveillance practice in 10 Pacific Island countries. Combinations of vector control interventions, applied appropriately, can prevent disease transmission. Although such programs exist in the Pacific, some interventions do not currently follow best practice. Key barriers to implementing evidence-based practice include lack of targeted education, internet and network coverage, personnel and expertise. Conclusions: Future goals for the region include the adaptation of current practice to evidence-based practice, and the development of vector and risk factor surveillance for targeted mosquito control. New developments should be sustainable and not reliant on internet or network real-time coverage. Education should be targeted to local communities to maximise community participation.

Background Rift Valley fever virus (RVFV) is a lethal threat to humans and livestock in many parts of Africa, the Arabian Peninsula, and the Indian Ocean. This systematic review’s objective was to consolidate understanding of RVFV epidemiology during 1999–2021 and highlight knowledge gaps relevant to plans for human vaccine trials. Methodology/Principal findings The review is registered with PROSPERO (CRD42020221622). Reports of RVFV infection or exposure among humans, animals, and/or vectors in Africa, the Arabian Peninsula, and the Indian Ocean during the period January 1999 to June 2021 were eligible for inclusion. Online databases were searched for publications, and supplemental materials were recovered from official reports and research colleagues. Exposures were classified into five groups: 1) acute human RVF cases, 2) acute animal cases, 3) human RVFV sero-surveys, 4) animal sero-surveys, and 5) arthropod infections. Human risk factors, circulating RVFV lineages, and surveillance methods were also tabulated. In meta-analysis of risks, summary odds ratios were computed using random-effects modeling. 1104 unique human or animal RVFV transmission events were reported in 39 countries during 1999–2021. Outbreaks among humans or animals occurred at rates of 5.8/year and 12.4/year, respectively, with Mauritania, Madagascar, Kenya, South Africa, and Sudan having the most human outbreak years. Men had greater odds of RVFV infection than women, and animal contact, butchering, milking, and handling aborted material were significantly associated with greater odds of exposure. Animal infection risk was linked to location, proximity to water, and exposure to other herds or wildlife. RVFV was detected in a variety of mosquito vectors during interepidemic periods, confirming ongoing transmission. Conclusions/Significance With broad variability in surveillance, case finding, survey design, and RVFV case confirmation, combined with uncertainty about populations-at-risk, there were inconsistent results from location to location. However, it was evident that RVFV transmission is expanding its range and frequency. Gaps assessment indicated the need to harmonize human and animal surveillance and improve diagnostics and genotyping. Given the frequency of RVFV outbreaks, human vaccination has strong potential to mitigate the impact of this now widely endemic disease., Author summary Rift Valley fever virus (RVFV) is a globally important mosquito-transmitted zoonosis that is also directly transmissible via aerosolization of body fluids from infected animals. RVFV outbreaks cause mass mortality of young livestock and pregnancy losses in both humans and animals. Severe human cases also result in hemorrhagic fever, encephalitis, and death. Loss of livestock additionally threatens the livelihood of people who depend on animals for income and food. In endemic areas, initiation of RVFV outbreaks is connected to weather events that cause excess rainfall, leading to flooding and subsequent mosquito blooms. However, the natural cycle of RVFV transmission is complex, requiring congregation of susceptible mammalian hosts and mosquito vectors in suitable environments. Several human vaccine candidates are in different stages of development, but none are yet licensed for use in human populations. In this systematic review, we assessed the 1999–2021 frequency and distribution of RVFV outbreaks among humans, animals, and vectors to identify potential locations and population targets for a human RVFV vaccine efficacy trial. It focuses on current understanding of RVFV epidemiology and the identification of gaps that pose critical barriers to controlling expansion of RVFV and implementing new protective measures including human vaccination.

Simple Summary Surveillance methods for oral squamous cell carcinoma (OSCC) patients may be chosen by considering the risk of recurrence. We performed a retrospective cohort study in 324 OSCC patients after complete surgical resection as the primary treatment from 2007 to 2020 at our hospital. Regarding the time to occurrence of postsurgical events, we found that half of cases of local recurrence, cervical lymph node, and distant metastasis occurred within 200 days, and 75% of all these events occurred within 400 days. However, the mean time for second primary cancer was 1589 days. The postsurgical events were detected by imaging examinations earlier than they were by visual examination and palpation. It is desirable to perform FDG-PET/CT within 3–6 months and at 1 year after surgery and to consider CECT as an option in between FDG-PET/CT, while continuing history and physical examinations for about 5 years based on an individual risk assessment. Abstract Background: The surveillance methods oral squamous cell carcinoma (OSCC) patients may be chosen by considering the risk for recurrence, and it is important to establish appropriate methods during the period in which latent/dormant cancer cells become more apparent. To investigate the appropriate surveillance of patients with OSCC based on the individual risk for recurrence and/or metastasis, we performed a retrospective cohort study after the complete surgical resection of OSCC as the primary treatment. Methods: The study was performed in 324 patients with OSCC who had been primarily treated with surgery from 2007 to 2020 at our hospital. We investigated the period, timing, and methods (visual examination, palpation and imaging using FDG-PET/CT or CECT) for surveillance in each case that comprised postsurgical treatment. Results: Regarding the time to occurrence of postsurgical events, we found that half of cases of local recurrence, cervical lymph node metastasis, and distant metastasis occurred within 200 days, and 75% of all of these events occurred within 400 days. However, the mean time for second primary cancer was 1589 days. The postsurgical events were detected earlier by imaging examinations than they were by visual examination and palpation. Conclusions: For the surveillance of patients with OSCC after primary surgery, it is desirable to perform FDG-PET/CT within 3–6 months and at 1 year after surgery and to consider CECT as an option in between FDG-PET/CT, while continuing history and physical examinations for about 5 years based on individual risk assessment.

ObjectivesWith the availability of COVID-19 vaccines, public health focus is shifting to post-vaccination surveillance to identify breakthrough infections in vaccinated populations. Therefore, the objectives of these reviews are to identify scientific evidence and international guidance on surveillance and testing approaches to monitor the presence of the virus in a vaccinated population.MethodWe searched Ovid MEDLINE®, including Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Embase, EBM Reviews - Cochrane Central Register of Controlled Trials, and EBM Reviews - Cochrane Database of Systematic Reviews. We also searched the Web of Science Core Collection. A grey literature search was also conducted. This search was limited to studies conducted since December 2020 and current to June 13th, 2021. There were no language limitations. COVID-19 surveillance studies that were published after December 2020 but did not specify whether they tested a vaccinated population were also considered for inclusion.For the international guidance review, a grey literature search was conducted, including a thorough search of Google, websites of international government organizations (e.g., Center for Disease Control and Prevention [CDC], World Health Organization [WHO]), and McMaster Health Forum (CoVID-END). This search was primarily examining surveillance guidance published since December 2020 (to capture guidance specific to vaccinations) and any relevant pre-December 2020 guidance.ResultsThirty-three studies were included for data synthesis of scientific evidence on surveillance of COVID-19. All the studies were published between April and June 2021. Twenty-one studies were from peer-reviewed journals. Five approaches to monitoring post-vaccination COVID-19 cases and emerging variants of concern were identified, including screening with reverse transcriptase polymerase chain reaction (RT-PCR) and/or a rapid antigen test, genomic surveillance, wastewater surveillance, metagenomics, and testing of air filters on public buses. For population surveillance, the following considerations and limitations were observed: variability in person-to-person testing frequency; lower sensitivity of antigen tests; timing of infections relative to PCR testing can result in missed infections; large studies may fail to identify local variations; and loss of interest in testing by participants in long follow-up studies.Through comprehensive grey literature searching, 68 international guidance documents were captured for full-text review. A total of 26 documents met the inclusion criteria and were included in our synthesis. Seven overarching surveillance methods emerged in the literature. PCR-testing was the most recommended surveillance method, followed by genomic screening, serosurveillance, wastewater surveillance, antigen testing, health record screening, and syndromic surveillance.ConclusionEvidence for post-vaccination COVID-19 surveillance was derived from studies in partially or fully vaccinated populations. Population PCR screening, supplemented by rapid antigen tests, was the most frequently used surveillance method and also the most commonly recommended across jurisdictions. Most recent guidance on COVID-19 surveillance is not specific to vaccinated individuals, or it is in effect but has not yet been updated to reflect that. Therefore, more evidence-informed guidance on testing and surveillance approaches in a vaccinated population that incorporates all testing modalities is required.EXECUTIVE SUMMARYObjectivesWith the availability of COVID-19 vaccines, public health focus is shifting to post-vaccination surveillance to identify breakthrough infections in vaccinated populations. Therefore, the objectives of these reviews are to: 1) identify scientific evidence on surveillance and testing approaches to monitor the presence of the virus in a vaccinated population and determine how these influence testing strategies; 2) identify international guidance on testing and surveillance for COVID-19 and its variants of concern in a vaccinated population; and 3) identify emerging technologies for surveillance.DesignA rapid review was conducted to identify scientific evidence on COVID-19 surveillance and testing approaches, and a targeted literature review was conducted on international guidance.MethodWe searched Ovid MEDLINE®, including Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Embase, EBM Reviews - Cochrane Central Register of Controlled Trials, and EBM Reviews - Cochrane Database of Systematic Reviews. We also searched the Web of Science Core Collection. We performed all searches on June 13, 2021. A grey literature search was also conducted, including: MedRxiv, Google, McMaster Health Forum (COVID-END), and websites of international government organizations (e.g., Center for Disease Control and Prevention [CDC], World Health Organization [WHO]). This search was limited to studies conducted since December 2020 and current to June 13th, 2021. There were no language limitations. COVID-19 surveillance studies that were published after December 2020 but did not specify whether they tested a vaccinated population were also considered for inclusion.For the international guidance review, a grey literature search was conducted, including a thorough search of Google, websites of international government organizations (e.g., Center for Disease Control and Prevention [CDC], World Health Organization [WHO]), and McMaster Health Forum (CoVID-END). This search was primarily examining surveillance guidance published since December 2020 (to capture guidance specific to vaccinations) and any relevant pre-December 2020 guidance. Although the primary focus was on surveillance guidance in a vaccinated population, guidance that was published after December 2020 but was not vaccine-specific was also considered for inclusion; it was assumed that this guidance was still in effect and was not yet updated. There were no language limitations. A patient partner was engaged during the co-production of a plain language summary for both the rapid review of primary literature and the review of international guidance.ResultsThirty-three studies were included for data synthesis of scientific evidence on surveillance of COVID-19. All the studies were published between April and June 2021. Twenty-one studies were from peer-reviewed journals. Five approaches to monitoring post-vaccination COVID-19 cases and emerging variants of concern were identified including, screening with reverse transcriptase polymerase chain reaction (RT-PCR) and/or a rapid antigen test, genomic surveillance, wastewater surveillance, metagenomics, and testing of air filters on public buses. Population surveillance with RT-PCR testing and/or rapid antigen testing was utilized in 22 studies, mostly in healthcare settings, but also in long-term care facilities (LTCFs) and in the community. The frequency of testing varied depending on whether there was an outbreak.For population surveillance, the following considerations and limitations were observed: studies with discretionary access to testing have highly variable person-to-person testing frequency; antigen tests have lower sensitivity, therefore some positive cases may be missed; timing of infections relative to PCR testing as well as the sensitivity of the tests can result in missed infections; large sample sizes from multicentre studies increase generalizability, but fail to identify local variations from individual centres; with electronic database surveillance, it is difficult to confirm whether patients with a breakthrough infection and a previous positive SARS-CoV-2 test result had a true reinfection or had prolonged shedding from the previous infection; and participants lose interest in studies with long follow-up, with decrease in testing rates over time.Six wastewater surveillance and three genomic surveillance studies were identified in this review. A number of benefits such as, good correlation with clinical data, ability to predict major outbreaks, and rapid turnaround time were observed with wastewater surveillance. However, challenges such as, inconsistencies in variant representation depending on where the samples were taken within the community, differences in the capacity of wastewater to predict case numbers based on the size of the wastewater treatment plants, and cost, were noted. Emerging technologies like viral detection in public transport filters, novel sampling, and assay platforms were also identified.Through comprehensive grey literature searching, 68 international guidance documents were captured for full-text review. A total of 26 documents met the inclusion criteria and were included in our synthesis. Most were not specific to vaccinated populations but reported on a surveillance method of COVID-19 and were therefore included in the review; it was assumed that they were still in effect but have not yet been updated. Eleven countries/regions were represented, including Australia, Brazil, France, Germany, India, New Zealand, Spain, United Kingdom, United States, Europe, and International. All of the guidance documents included surveillance methods appropriate for community settings. Other settings of interest were healthcare settings, including hospitals and primary care centres, long-term care facilities, points of entry for travel, schools, and other sentinel sites (e.g., prisons and closed settings). Seven overarching surveillance methods emerged in the literature. PCR-testing was the most recommended surveillance method, followed by genomic screening, serosurveillance, wastewater surveillance, antigen testing, health record screening, and syndromic surveillance.Only one document (published by Public Health England) was identified that provided guidance on surveillance specific to vaccinated populations. The document outlined a plan to surveil and monitor COVID-19 in vaccinated populations through a series of targeted longitudinal studies, routine surveillance, enhanced surveillance, use of electronic health records, surveillance of vaccine failure (including follow-up with viral whole genome sequencing), and sero-surveillance (including blood donor samples, routine blood tests, and residual sera).ConclusionEvidence for post-vaccination COVID-19 surveillance was derived from studies in partially or fully vaccinated populations. Population PCR screening, supplemented by rapid antigen tests, was the most frequently used surveillance method and also the most commonly recommended across jurisdictions. The selection of testing method and the frequency of testing was determined by the intensity of the disease and the scale of testing. Other common testing methods included wastewater surveillance and genomic surveillance. A few novel technologies are emerging, however, many of these are yet to be utilized in the real-world setting. There is limited evidence-based guidance on surveillance in a vaccinated population. Most recent guidance on COVID-19 surveillance is not specific to vaccinated individuals, or it is in effect but has not yet been updated to reflect that. Therefore, more evidence-informed guidance on testing and surveillance approaches in a vaccinated population that incorporates all testing modalities is required.Protocol/Topic RegistrationPROSPERO-CRD42021261215.Key DefinitionsAntigen:a foreign protein which induces an immune response in the body, especially the production of antibodiesFully vaccinated: refers to individuals who have received complete dosage of a given vaccinePartially vaccinated:refers to individuals who have received an incomplete dosage of a given vaccineSero-surveillance:estimation of antibody levels against infectious diseasesSurveillance:ongoing systematic collection, analysis, and interpretation of health data that are essential to the planning, implementation, and evaluation of public health practiceVariants of Concern:a variant for which there is evidence of an increase in transmissibility and/or more severe diseaseVariants:virus with a permanent change in its genetic sequence

Introduction: Ultrasound (US) is current standard of care for imaging surveillance in patients at risk for hepatocellular carcinoma (HCC). Magnetic resonance imaging (MRI) has been explored as an alternative, given the higher sensitivity of MRI, although this comes at a higher cost. We performed a cost-effective analysis comparing US and a dual-sequence non-contrast MRI (NCEMRI) for HCC surveillance, in the local setting. Methods: Cost-effectiveness analysis of no surveillance, US surveillance and NCEMRI surveillance was performed using Markov modelling and microsimulation. At-risk patient cohort was simulated and followed-up for 40 years to estimate their disease status, direct medical costs, and effectiveness. Quality-adjusted life years (QALYs) and incremental cost effectiveness ratio were calculated. Results: 482,000 patients with an average age of 40 years were simulated and followed up for 40 years. The average total costs and QALYs for the three scenarios – no surveillance, US surveillance and NCEMRI surveillance were S$1,193/7.460 QALYs; S$8,099/11.195 QALYs; S$9,720/11.366 QALYs, respectively. Conclusion: Despite NCEMRI having a superior diagnostic accuracy, it is a less cost-effective strategy than US for HCC surveillance in the general at-risk population. Future local cost-effectiveness analyses should include stratifying surveillance methods with a variety of imaging techniques (US, NCEMRI, CEMRI) based on patients’ risk profiles.

Invasive mammalian herbivores can have significant unwanted impacts on indigenous ecosystems and production landscapes. Their impacts are often mitigated using sustained lethal control or eradication. If eradication is the chosen option, it is critical to confirm where the pest population is distributed and prove that eradication has been achieved following the removal intervention. This form of pest management requires robust and quantitative estimates of surveillance. We compared the detection probabilities, derived surveillance system sensitivities (under standardized conditions), and cost of surveillance for four survey methods for invasive Bennett’s wallabies (Notamacropus rufogriseus) in South Island, New Zealand: helicopter surveys with observers, helicopter surveys using a thermal imaging camera, a ground hunter with detector dogs, and camera traps. We found camera traps and ground hunters with dogs were the most sensitive surveillance methods (82% and 45% chance of detecting a wallaby assuming full search coverage of a 100‐ha area, respectively) and were most cost‐effective (62% and 85% cheaper than helicopter surveys with observers, respectively). However, although these methods are useful for surveying small areas (~1000 ha or less), capacity and time issues make them impractical for surveying large areas. The helicopter‐based methods will likely be more suitable for monitoring large areas (e.g., 10,000–50,000 ha) that need to be completed more rapidly than could be achieved using ground‐based methods. The model that we used for evaluating detection probability and surveillance sensitivity for different survey methods is highly applicable for managing a wide range of mammalian pests and confidently declaring their absence from an area.

Introduction: Early in the COVID-19 pandemic, the Centers for Disease Control and Prevention (CDC) rapidly initiated COVID-19 surveillance by leveraging existing hospital networks to assess disease burden among hospitalized inpatients and inform prevention efforts.Materials and Methods: The Surveillance Platform for Enteric and Respiratory Infectious Organisms at Veterans Affairs Medical Centers (SUPERNOVA) is a network of five United States Veterans Affairs Medical Centers which serves nearly 400,000 Veterans annually and conducts laboratory-based passive and active monitoring for pathogens associated with acute gastroenteritis and acute respiratory illness among hospitalized Veterans. This paper presents surveillance methods for adapting the SUPERNOVA surveillance platform to prospectively evaluate COVID-19 epidemiology during a public health emergency, including detecting, characterizing, and monitoring patients with and without COVID-19 beginning in March 2020. To allow for case-control analyses, patients with COVID-19 and patients with non-COVID-19 acute respiratory illness were included.Results: SUPERNOVA included 1,235 participants with COVID-19 and 707 participants with other acute respiratory illnesses hospitalized during February through December 2020. Most participants were male (93.1%), with a median age of 70 years, and 45.8% non-Hispanic Black and 32.6% non-Hispanic White. Among those with COVID-19, 28.2% were transferred to an intensive care unit, 9.4% received invasive mechanical ventilation, and 13.9% died. Compared with controls, after adjusting for age, sex, and race/ethnicity, COVID-19 case-patients had significantly higher risk of mortality, respiratory failure, and invasive mechanical ventilation, and longer hospital stays.Discussion: Strengths of the SUPERNOVA platform for COVID-19 surveillance include the ability to collect and integrate multiple types of data, including clinical and illness outcome information, and SARS-CoV-2 laboratory test results from respiratory and serum specimens. Analysis of data from this platform also enables formal comparisons of participants with and without COVID-19. Surveillance data collected during a public health emergency from this key U.S. population of Veterans will be useful for epidemiologic investigations of COVID-19 spectrum of disease, underlying medical conditions, virus variants, and vaccine effectiveness, according to public health priorities and needs.

Background Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes. Methods A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children’s hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children’s Hospital, Cincinnati, OH, Seattle Children’s Hospital, Seattle, WA, and Children’s Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review. Results Among 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p = 0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time. Conclusions Year of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.

The continued spread of SARS-CoV-2 and emergence of new variants with higher transmission rates and/or partial resistance to vaccines has further highlighted the need for large-scale testing and genomic surveillance. However, current diagnostic testing (e.g., PCR) and genomic surveillance methods (e.g., whole genome sequencing) are performed separately, thus limiting the detection and tracing of SARS-CoV-2 and emerging variants. Here, we developed DeepSARS, a high-throughput platform for simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2 by the integration of molecular barcoding, targeted deep sequencing, and computational phylogenetics. DeepSARS enables highly sensitive viral detection, while also capturing genomic diversity and viral evolution. We show that DeepSARS can be rapidly adapted for identification of emerging variants, such as alpha, beta, gamma, and delta strains, and profile mutational changes at the population level. DeepSARS sets the foundation for quantitative diagnostics that capture viral evolution and diversity.Abstract FigureGraphical abstractDeepSARS uses molecular barcodes (BCs) and multiplexed targeted deep sequencing (NGS) to enable simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2.

Background: In order to prevent spread and improve control of infectious diseases, public health experts need to closely monitor human and animal populations. Infectious disease surveillance is an established, routine data collection process essential for early warning, rapid response, and disease control. The quantity of data potentially useful for early warning and surveillance has increased exponentially due to social media and other big data streams. Digital epidemiology is a novel discipline that includes harvesting, analysing, and interpreting data that were not initially collected for healthcare needs to enhance traditional surveillance. During the current COVID-19 pandemic, the importance of digital epidemiology complementing traditional public health approaches has been highlighted.Objective: The aim of this paper is to provide a comprehensive overview for the application of data and digital solutions to support surveillance strategies and draw implications for surveillance in the context of the COVID-19 pandemic and beyond.Methods: A search was conducted in PubMed databases. Articles published between January 2005 and May 2020 on the use of digital solutions to support surveillance strategies in pandemic settings and health emergencies were evaluated.Results: In this paper, we provide a comprehensive overview of digital epidemiology, available data sources, and components of 21st-century digital surveillance, early warning and response, outbreak management and control, and digital interventions.Conclusions: Our main purpose was to highlight the plausible use of new surveillance strategies, with implications for the COVID-19 pandemic strategies and then to identify opportunities and challenges for the successful development and implementation of digital solutions during non-emergency times of routine surveillance, with readiness for early-warning and response for future pandemics. The enhancement of traditional surveillance systems with novel digital surveillance methods opens a direction for the most effective framework for preparedness and response to future pandemics.

Background: Pregnancy identification and follow-up surveillance can enhance the reporting of pregnancy outcomes, including stillbirths and perinatal and early postnatal mortality. This paper reviews pregnancy surveillance methods used in Health and Demographic Surveillance Systems (HDSSs) in low- and middle-income countries. Methods: We searched articles containing information about pregnancy identification methods used in HDSSs published between January 2002 and October 2019 using PubMed and Google Scholar. A total of 37 articles were included through literature review and 22 additional articles were identified via manual search of references. We reviewed the gray literature, including websites, online reports, data collection instruments, and HDSS protocols from the Child Health and Mortality Prevention Study (CHAMPS) Network and the International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH). In total, we reviewed information from 52 HDSSs described in 67 sources. Results: Substantial variability exists in pregnancy surveillance approaches across the 52 HDSSs, and surveillance methods are not always clearly documented. 42% of HDSSs applied restrictions based on residency duration to identify who should be included in surveillance. Most commonly, eligible individuals resided in the demographic surveillance area (DSA) for at least three months. 44% of the HDSSs restricted eligibility for pregnancy surveillance based on a woman’s age, with most only monitoring women 15-49 years. 10% had eligibility criteria based on marital status, while 11% explicitly included unmarried women in pregnancy surveillance. 38% allowed proxy respondents to answer questions about a woman’s pregnancy status in her absence. 20% of HDSSs supplemented pregnancy surveillance with investigations by community health workers or key informants and by linking HDSS data with data from antenatal clinics. Conclusions: Methodological guidelines for conducting pregnancy surveillance should be clearly documented and meticulously implemented, as they can have implications for data quality and accurately informing maternal and child health programs.

Accurate and timely monitoring of emerging genomic diversity is crucial for limiting the spread of potentially more transmissible/pathogenic strains of SARS-CoV-2. At the time of writing, over 1.8M distinct viral genome sequences have been made publicly available, and a sophisticated nomenclature system based on phylogenetic evidence and expert manual curation has allowed the relatively rapid classification of emerging lineages of potential concern.Here, we propose a complementary approach that integrates fine-grained spatiotemporal estimates of allele frequency with unsupervised clustering of viral haplotypes, and demonstrate that multiple highly frequent genetic variants, arising within large and/or rapidly expanding SARS-CoV-2 lineages, have highly biased geographic distributions and are not adequately captured by current SARS-CoV-2 nomenclature standards.Our results advocate a partial revision of current methods used to track SARS-CoV-2 genomic diversity and highlight the importance of the application of strategies based on the systematic analysis and integration of regional data. Here we provide a complementary, completely automated and reproducible framework for the mapping of genetic diversity in time and across different geographic regions, and for the prioritization of virus variants of potential concern. We believe that the approach outlined in this study will contribute to relevant advances to current genomic surveillance methods.

Pigs are a major meat source worldwide and a pillar of Chinese animal husbandry; hence, their health and safety are a prioritized concern of the national economy. Although pig viruses have been continuously investigated, the full extent of the pig virome has remained unknown and emerging viruses are still a major threat to the pig industry. Here, we report a comprehensive study to delineate the pig virome of 1,841 healthy weaned pigs from 45 commercial farms collected from 25 major pig-producing regions across China. A viromic sequence data set, named Pigs_VIRES, which matched 96,586 viral genes from at least 249 genera within 66 families and which almost tripled the number of previously published pig viromic genes, was established. The majority of the mammalian viruses were closely related to currently known ones. A comparison with previously published viromes of bovines, avians, and humans has revealed the distinct composition of Pigs_VIRES, which has provided characteristic viromic signatures of serum, pharyngeal, and anal samples that were significantly influenced by farming management and disease control measures. Taken together, Pigs_VIRES has revealed the most complete viromic data set of healthy pigs to date. The compiled data also provide useful guidance to pig viral disease control and prevention and the biosafety management of pig farms. Especially, the established viromic protocol has created a precision surveillance strategy to potentially innovate currently used surveillance methods of animal infectious diseases, particularly by making precision surveillance available to other animal species on a large scale or even during a nationwide surveillance campaign. IMPORTANCE Pigs are deeply involved in human lives; hence, their viruses are associated with public health. Here, we established the most comprehensive virome of healthy piglets to date, which provides a viromic baseline of weaned pigs for disease prevention and control, highlighting that longitudinal viromic monitoring is needed to better understand the dynamics of the virome in pig development and disease occurrence. The present study also shows how high standards of animal farm management with strict biosafety measures can significantly minimize the risk of introduction of pathogenic viruses into pig farms. Particularly, the viromic strategy established, i.e., high-throughput detection and analyses of various known and unknown pathogenic viruses in a single test at large scale, has completely innovated current surveillance measures in provision of timely and precise detection of all potentially existing pathogenic viruses and can be widely applied in other animal species.

The COVID-19 pandemic has given rise to rapid and widespread international pursuit of wastewater surveillance for genetic signals of SARS-CoV-2, the virus causing the pandemic. Environmental scientists and engineers familiar with the techniques required for this endeavor have responded. Many of the environmental scientists engaged in these investigations have not necessarily had experience with the ethical obligations associated with generating and handling human health data. The Canadian Water Network facilitated adoption of these surveillance methods by creating a national coalition, which included a public health advisory group that recognized a need for ethics guidance for the wastewater approach to public health surveillance. This Policy Analysis addresses that need and is based on a review of relevant ethics literature tightly focused on ethics applicable to public health surveillance. That review revealed that classical health bioethics governing clinical practice and general public health ethics guidance did not adequately address key issues in wastewater surveillance. The 2017 World Health Organization guidelines, directly based on a systematic literature review, specifically addressed ethical issues in public health surveillance. The application of relevant ethical guidance to wastewater surveillance is analyzed and summarized for environmental scientists.

Emerging and recurrent outbreaks caused by zoonotic agents pose a public health risk. They result in morbidity and mortality in humans and significant losses in the livestock and food industries. This highlights the need for rapid surveillance methods. Despite the high reliability of conventional pathogen detection methods, they have high detection limits and are time-consuming and not suitable for on-site analysis. Furthermore, the unpredictable spread of zoonotic infections due to a complex combination of risk factors urges the development of innovative technologies to overcome current limitations in early warning and detection. Biosensing, in particular, is highlighted here, as it offers rapid and cost-effective devices for use at the site of infection while increasing the sensitivity of detection. Portuguese research in biosensors for zoonotic pathogens is the focus of this review. This branch of research produces exciting and innovative devices for the study of the most widespread pathogenic bacteria. The studies presented here relate to the different classes of pathogens whose characteristics and routes of infection are also described. Many advances have been made in recent years, and Portuguese research teams have increased publications in this field. However, biosensing still needs to be extended to other pathogens, including potentially pandemic viruses. In addition, the use of biosensors as part of routine diagnostics in hospitals for humans, in animal infections for veterinary medicine, and food control has not yet been achieved. Therefore, a convergence of Portuguese efforts with global studies on biosensors to control emerging zoonotic diseases is foreseen for the future., Centro de Investigação Desenvolvimento e Inovação da Academia Militar (CINAMIL) from Academia Militar/Instituto Universitário Militar by project SIPA (Sistema Integrado de Proteção Alimentar), info:eu-repo/semantics/publishedVersion

Wastewater based epidemiology (WBE) has been successfully applied for SARS-CoV-2 surveillance at the city and building levels. However, sampling at the city level does not provide sufficient spatial granularity to identify COVID-19 hotspots, while data from building-level sampling are too narrow in scope for broader public health application. The objective of this study was to examine the feasibility of using wastewater from wastewater collection systems (WCSs) to monitor COVID-19 hotspots at the zip code level. In this study, 24-h composite wastewater samples were collected from five manholes and two wastewater treatment plants (WWTPs) in the City of Lincoln, Nebraska. By comparing to the reported weekly COVID-19 case numbers, we identified different hotspots responsible for two COVID-19 surges during the study period. One zip code was the only sampling locations that was consistently tested positive during the first COVID-19 surge. In comparison, nearly all the zip codes tested exhibited virus concentration increases that overlapped with the second COVID-19 surge, suggesting broader spread of the virus at that time. These findings demonstrate the feasibility of using WBE to monitor COVID-19 at the zip code level. Highly localized disease surveillance methods can improve public health prevention and mitigation measures at the community level., Graphical abstract Unlabelled Image

Hepatocellular cancer (HCC) is the fourth leading cause of cancer-related deaths worldwide and the fastest growing cause of cancer deaths in the United States. The overall prognosis of HCC remains dismal, except for the subset of patients who are diagnosed at early stage and receive potentially curative therapies, such as surgical resection and liver transplantation. Given this, expert society guidelines recommend HCC surveillance every 6 months in at-risk individuals. Despite these recommendations, the effectiveness of HCC surveillance remains a subject of debate. We discuss current best practices for HCC surveillance and the evidence that support these recommendations. We also describe several initiatives that are underway to improve HCC surveillance and outline areas that may serve as high-yield targets for future research. Overall, we believe these efforts will help the field move toward precision surveillance, where surveillance tests and intervals are tailored to individual HCC risk. Doing so can maximize surveillance benefits, minimize surveillance harms, and optimize overall value for all patients.

Hospital-acquired Infections (HAIs) surveillance, defined as the systematic collection of data related to a certain health event, is considered an essential dimension for a prevention HAI program to be effective. In recent years, new automated HAI surveillance methods have emerged with the wide adoption of electronic health records (EHR). Here we present the validation results against the gold standard of HAIs diagnosis of the InNoCBR system deployed in the Ourense University Hospital Complex (Spain). Acting as a totally autonomous system, InNoCBR achieves a HAI sensitivity of 70.83% and a specificity of 97.76%, with a positive predictive value of 77.24%. The kappa index for infection type classification is 0.67. Sensitivity varies depending on infection type, where bloodstream infection attains the best value (93.33%), whereas the respiratory infection could be improved the most (53.33%). Working as a semi-automatic system, InNoCBR reaches a high level of sensitivity (81.73%), specificity (99.47%), and a meritorious positive predictive value (94.33%). Xunta de Galicia | Ref. ED431C2018/55-GRC