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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.

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.

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.

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).

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.

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.

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.

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

Aedes aegypti is a vector of many significant arboviruses worldwide, including dengue, Zika, chikungunya, and yellow fever viruses. With vector control methodology pivoting toward rearing and releasing large numbers of insects for either population suppression or virus-blocking, economical remote (sentinel) surveillance methods for release tracking become increasingly necessary. Recent steps in this direction include advances in optical sensors that identify and classify insects based on their wing beat frequency (WBF). As these traps are being developed, there is a strong need to better understand the environmental and biological factors influencing mosquito WBFs. Here, we developed new untethered-subject methodology to detect changes in WBFs of male and female Ae. aegypti. This new methodology involves directing an ultrasonic transducer at a free-flying subject and measuring the Doppler shift of the reflected ultrasonic continuous wave signal. This system's utility was assessed by determining its ability to confirm previous reports on the effect of temperature, body size, and age on the WBFs generated from acoustic or optical-based experiments. The presented ultrasonic method successfully detected expected trends for each factor for both male and female Ae. aegypti without the need for subject manipulation and potential impediment of natural flight dynamics due to tethering. As a result, this ultrasonic methodology provides a new method for understanding the environmental and physiological determinants of male and female WBFs that can inform the design of remote mosquito surveillance systems.

Background Zika virus (ZIKV) is an emerging mosquito-borne arbovirus that can produce serious public health consequences. In 2016, ZIKV caused an epidemic in many countries around the world, including the United States. ZIKV surveillance and vector control is essential to combating future epidemics. However, challenges relating to the timely publication of case reports significantly limit the effectiveness of current surveillance methods. In many countries with poor infrastructure, established systems for case reporting often do not exist. Previous studies investigating the H1N1 pandemic, general influenza and the recent Ebola outbreak have demonstrated that time- and geo-tagged Twitter data, which is immediately available, can be utilized to overcome these limitations. Methods In this study, we employed a recently developed system called Cloudberry to filter a random sample of Twitter data to investigate the feasibility of using such data for ZIKV epidemic tracking on a national and state (Florida) level. Two auto-regressive models were calibrated using weekly ZIKV case counts and zika tweets in order to estimate weekly ZIKV cases 1 week in advance. Results While models tended to over-predict at low case counts and under-predict at extreme high counts, a comparison of predicted versus observed weekly ZIKV case counts following model calibration demonstrated overall reasonable predictive accuracy, with an R2 of 0.74 for the Florida model and 0.70 for the U.S. model. Time-series analysis of predicted and observed ZIKV cases following internal cross-validation exhibited very similar patterns, demonstrating reasonable model performance. Spatially, the distribution of cumulative ZIKV case counts (local- & travel-related) and zika tweets across all 50 U.S. states showed a high correlation (r = 0.73) after adjusting for population. Conclusions This study demonstrates the value of utilizing Twitter data for the purposes of disease surveillance. This is of high value to epidemiologist and public health officials charged with protecting the public during future outbreaks. Electronic supplementary material The online version of this article (10.1186/s12889-019-7103-8) contains supplementary material, which is available to authorized users.

Summary Under‐reporting of foot‐and‐mouth disease (FMD) masks the true prevalence in parts of the world where the disease is endemic. Laboratory testing for the detection of FMD virus (FMDV) is usually reliant upon the collection of vesicular epithelium and fluid samples that can only be collected from acutely infected animals, and therefore animals with sub‐clinical infection may not be identified. Milk is a non‐invasive sample type routinely collected from dairy farms that has been utilized for surveillance of a number of other diseases. The aim of this study was to examine the application of milk as an alternative sample type for FMDV detection and typing, and to evaluate milk as a novel approach for targeted surveillance of FMD in East Africa. FMDV RNA was detected in 73/190 (38%) individual milk samples collected from naturally infected cattle in northern Tanzania. Furthermore, typing information by lineage‐specific rRT‐PCR assays was obtained for 58% of positive samples, and corresponded with the virus types identified during outbreak investigations in the study area. The VP1‐coding sequence data obtained from milk samples corresponded with the sequence data generated from paired epithelial samples collected from the same animal. This study demonstrates that milk represents a potentially valuable sample type for FMDV surveillance and might be used to overcome some of the existing biases of traditional surveillance methods. However, it is recommended that care is taken during sample collection and testing to minimize the likelihood of cross‐contamination. Such approaches could strengthen FMDV surveillance capabilities in East Africa, both at the individual animal and herd level.

Background Giardiasis is the most common intestinal parasitic disease of humans identified in the United States (US) and an important waterborne disease. In the United States, giardiasis has been variably reportable since 1992 and was made a nationally notifiable disease in 2002. Our objective was to describe the epidemiology of US giardiasis cases from 1995 through 2016 using National Notifiable Diseases Surveillance System data. Methods Negative binomial regression models were used to compare incidence rates by age group (0–4, 5–9, 10–19, 20–29, 30–39, 40–49, 50–64, and ≥ 65 years) during 3 time periods (1995–2001, 2002–2010, and 2011–2016). Results During 1995–2016, the average number of reported cases was 19 781 per year (range, 14 623–27 778 cases). The annual incidence of reported giardiasis in the United States decreased across all age groups. This decrease differs by age group and sex and may reflect either changes in surveillance methods (eg, changes to case definitions or reporting practices) or changes in exposure. Incidence rates in males and older age groups did not decrease to the same extent as rates in females and children. Conclusions Trends suggest that differences in exposures by sex and age group are important to the epidemiology of giardiasis. Further investigation into the risk factors of populations with higher rates of giardiasis will support prevention and control efforts.

Context While the data generated by medical students at schools that require electronic patient encounter logs is primarily used to monitor their training progress, it can also be a great source of public health data. Specifically, it can be used for syndromic surveillance, a method used to analyze instantaneous health data for early detection of disease outbreaks. Objective To analyze how the International Classification of Diseases, 10th Revision (ICD-10) codes input by medical students at the Edward Via College of Osteopathic Medicine into the Clinical Rotation Evaluation and Documentation Organizer (CREDO) patient encounter logging system could act as a new syndromic surveillance tool. Methods A CREDO database query was conducted for ICD-10 codes entered between November 1, 2019 and March 13, 2020 using the World Health Organization’s 2011 revised case definitions for Influenza Like Illness (ILI). During that period, medical students had an approximated mean of 3,000 patient encounters per day from over 1,500 clinical sites. A cumulative sum technique was applied to the data to generate alert thresholds. Breast cancer, a disease with a stable incidence during the specified timeframe, was used as a control. Results Total ILI daily ICD-10 counts that exceeded alert thresholds represented unusual levels of disease occurred 11 times from November 20, 2020 through February 28, 2020. This analysis is consistent with the COVID-19 pandemic timeline. The first statistically significant ILI increase occurred nine days prior to the first laboratory confirmed case in the country. Conclusion Syndromic surveillance can be timelier than traditional surveillance methods, which require laboratory testing to confirm disease. As a result of this study, we are installing a real-time alert for ILI into CREDO, so rates can be monitored continuously as an indicator of possible future new infectious disease outbreaks.

Canine distemper virus (CDV) has a broad mammalian host range. In Ontario, Canada, CDV is frequently encountered in wild carnivores and is the most common infectious cause of death for raccoons (Procyon lotor). The isolation of wild-type CDV strains genetically distinct from vaccine strains in North America has renewed interest in the epidemiological patterns of this virus. However, wildlife surveillance is challenging and often utilizes a combination of surveillance methods with aggregation of data from multiple sources. Our objective was to compare raccoon CDV data generated through two separate surveillance components operated by the Ontario-Nunavut node of the Canadian Wildlife Health Cooperative. The raw data generated by each component in addition to the results of multilevel logistic regression and spatial scan statistics, were compared between the datasets. A total of 498 raccoons obtained via passive surveillance between 2007 and 2017 and 887 raccoons obtained via enhanced-passive surveillance between 2014 and 2017, were tested for CDV. The number and geographic distribution of reports, proportion of yearly reports classified as CDV-positive, and characteristics of CDV-positive raccoons differed between passive and enhanced-passive surveillance components. Geographical data demonstrated that CDV infection was present throughout southern Ontario. The geographic area of both surveillance components combined was more representative than either passive or enhanced-passive surveillance in isolation; but was restricted compared to the overall distribution of raccoons in Ontario. Regression analyses produced statistically significant associations between the presence of CDV and host and environmental variables that were at times discordant between the two datasets. Studying the properties of these datasets will inform future passive wildlife surveillance strategies and highlights the impact that a surveillance strategy can have on the results of epidemiological analyses.

Background The COVID-19 pandemic has posed a huge challenge to healthcare systems and their personnel worldwide. The study of the impact of SARS-CoV-2 infection among healthcare workers (HCW), through prevalence studies, will let us know viral expansion, individuals at most risk and the most exposed areas in healthcare organizations. The aim of this study is to gauge the impact of SARS-CoV-2 pandemic in our hospital workforce and identify groups and areas at increased risk. Methods and findings This is a cross-sectional and incidence study carried out on healthcare workers based on molecular and serological diagnosis of SARS-CoV-2 infection. Of the 3013 HCW invited to participate, 2439 (80.9%) were recruited, including 674 (22.4%) who had previously consulted at the Occupational Health Service (OHS) for confirmed exposure and/or presenting symptoms suggestive of COVID-19. A total of 411 (16.9%) and 264 (10.8%) healthcare workers were SARS-CoV-2 IgG and rRT-PCR positive, respectively. The cumulative prevalence considering all studies (IgG positive HCW and/or rRT-PCR positive detection) was 485 (19.9%). SARS-CoV-2 IgG-positive patients in whom the virus was not detected were 221 (9.1%); up to 151 of them (68.3%) did not report any compatible symptoms nor consult at the OHS for this reason. Men became more infected than women (25% vs 18.5%, p = 0.0009), including when data were also classified by age. COVID-19 cumulative prevalence among the HCW assigned to medical departments was higher (25.2%) than others, as well as among medical staff (25.4%) compared with other professional categories (p Conclusions The global impact of the COVID-19 pandemic on HCW of our centre has been 19.9%. Doctors and medical services personnel have had the highest prevalence of SARS-CoV-2 infection, but many of them have not presented compatible symptoms. This emphasizes the performance of continuous surveillance methods of the most exposed health personnel and not only based on the appearance of symptoms.

Mosquito-borne viruses threaten the Caribbean due to the region’s tropical climate and seasonal reception of international tourists. Outbreaks of chikungunya and Zika have demonstrated the rapidity with which these viruses can spread. Concurrently, dengue fever cases have climbed over the past decade. Sustainable disease control measures are urgently needed to quell virus transmission and prevent future outbreaks. Here, to improve upon current control methods, we analyze temporal and spatial patterns of chikungunya, Zika, and dengue outbreaks reported in the Dominican Republic between 2012 and 2018. The viruses that cause these outbreaks are transmitted by Aedes mosquitoes, which are sensitive to seasonal climatological variability. We evaluate whether climate and the spatio-temporal dynamics of dengue outbreaks could explain patterns of emerging disease outbreaks. We find that emerging disease outbreaks were robust to the climatological and spatio-temporal constraints defining seasonal dengue outbreak dynamics, indicating that constant surveillance is required to prevent future health crises., Dengue is endemic in the Dominican Republic, and causes regular outbreaks, whereas Zika and chikungunya are emerging infections in the area. Here, the authors show that outbreaks of the emerging infections could not be predicted by seasonal dengue dynamics.

To review the most common forms of cardiovascular toxicities from anti-cancer drugs, with a focus on their prevention via cardioprotective pharmacologic or lifestyle therapy, risk factors management, screening, monitoring, and surveillance methods. There are almost 20 million cancer survivors in the USA, and their leading cause of death besides cancer recurrence or a secondary cancer is cardiovascular disease (CVD). CVD is prevalent in this population, with the most common forms being cardiomyopathy, ischemia, atrial fibrillation, and hypertension from cancer drugs or radiation therapy. The field of cardio-oncology is continuously evolving with multi-modality risk prediction strategies, moving towards precision surveillance and interventions that allow for safe continuation of life-saving chemotherapy. Preventative measures with implementation of novel drugs (including sodium-glucose cotransporter inhibitors) and modulated chemotherapy administration can aid in cardiotoxicity risk reduction. Recently, clonal hematopoiesis of indeterminate potential has been identified as a common risk factor for atherosclerotic cardiovascular disease present in > 10% of those age 70 or older. Also, risk stratification for atrial fibrillation appears pivotal. Multidisciplinary team management might have a central role in patient management and care. There is a central role for the optimization of cardiovascular disease risk for cancer patients and survivors in multidisciplinary teams, with descriptions of four forms of cardiovascular toxicities. In addition to pharmacologic cardioprotection, lifestyle modification is becoming more prominent as a preventive tool. Moreover, studies relevant to one type of toxicity should be scrutinized routinely to determine how those result may play out in the setting of other types of toxicities. All of these areas of investigation should ultimately be pursued in the setting of personalized medicine, and will likely benefit from the integration of artificial intelligence methods. Studies in genomics and transcriptomics have identified variations in the genome and gene expression profiles that associate with induction of or protection from cardiovascular toxicity and can affect cardioprotection efforts. Larger randomized clinical trials will be needed for all patients, with a focus on incorporation of ethnic minorities likely by intentional oversampling, to overcome decades of demographic homogeneity in the trials.

The Shigella genus includes serious foodborne disease etiologic agents, with 4 species and 54 serotypes. Identification at species and serotype levels is a crucial task in microbiological laboratories. Nevertheless, the genetic similarity between Shigella spp. and Escherichia coli challenges the correct identification and serotyping of Shigella spp., with subsequent negative repercussions on surveillance, epidemiological investigations, and selection of appropriate treatments. For this purpose, multiple techniques have been developed historically ranging from phenotype-based methods and single or multilocus molecular techniques to whole-genome sequencing (WGS). To facilitate the selection of the most relevant method, we herein provide a global overview of historical and emerging identification and serotyping techniques with a particular focus on the WGS-based approaches. This review highlights the excellent discriminatory power of WGS to more accurately elucidate the epidemiology of Shigella spp., disclose novel promising genomic targets for surveillance methods, and validate previous well-established methods.

Background Sri Lanka sustained its malaria-free status by implementing, among other interventions, three core case detection strategies namely Passive Case Detection (PCD), Reactive Case Detection (RACD) and Proactive Case Detection (PACD). The outcomes of these strategies were analysed in terms of their effectiveness in detecting malaria infections for the period from 2017 to 2019. Methods Comparisons were made between the surveillance methods and between years, based on data obtained from the national malaria database and individual case reports of malaria patients. The number of blood smears examined microscopically was used as the measure of the volume of tests conducted. The yield from each case detection method was calculated as the proportion of blood smears which were positive for malaria. Within RACD and PACD, the yield of sub categories of travel cohorts and spatial cohorts was ascertained for 2019. Results A total of 158 malaria cases were reported in 2017–2019. During this period between 666,325 and 725,149 blood smears were examined annually. PCD detected 95.6 %, with a yield of 16.1 cases per 100,000 blood smears examined. RACD and PACD produced a yield of 11.2 and 0.3, respectively. The yield of screening the sub category of travel cohorts was very high for RACD and PACD being 806.5 and 44.9 malaria cases per 100,000 smears, respectively. Despite over half of the blood smears examined being obtained by screening spatial cohorts within RACD and PACD, the yield of both was zero over all three years. Conclusions The PCD arm of case surveillance is the most effective and, therefore, has to continue and be further strengthened as the mainstay of malaria surveillance. Focus on travel cohorts within RACD and PACD should be even greater. Screening of spatial cohorts, on a routine basis and solely because people are resident in previously malarious areas, may be wasteful, except in situations where the risk of local transmission is very high, or is imminent. These findings may apply more broadly to most countries in the post-elimination phase.

The value of routine surveillance after resection of pancreatic ductal adenocarcinoma (PDAC) is unclear, and expert guidelines offer conflicting recommendations. This study is a systematic review of evidence for surveillance programs.A systematic review of studies evaluating different surveillance methods was undertaken. A meta-analysis was performed for those studies reporting rates of asymptomatic recurrence, treatment of recurrence and overall survival, according to different surveillance methods.Ten studies were included in the literature review, with five studies appropriate for meta-analysis (1596 patients). Patients within active surveillance programs were more likely to have recurrence detected at an asymptomatic stage (Pooled Rate: 49.3% vs. 19.1%, p = 0.043). Within studies reporting these outcomes, patients with asymptomatic recurrence were more likely to receive treatment for recurrence (Odds Ratio 3.49; 95% CI: 1.73-7.07; p 0.001) and had longer overall survival (Mean Difference: 9.5 months; 95% CI: 4.1-14.8; p 0.001) than those with symptoms at time of recurrence.Routine surveillance after surgery for PDAC appears to detect more patients at an asymptomatic stage. Data from these non-randomised trials also suggest that treatment rates and survival may be superior in patients were recurrence is detected when asymptomatic. As such, these data suggest that routine surveillance may improve patient outcomes, although an appropriately conducted trial would be required to address concerns that various sources of bias may be affecting these results.

In this article, we investigate the impact of COVID-19 through screening and surveillance methods adopted in India, as well as the potential health system, social, political, and economic consequences. The research was done in a chronological manner, and data was collected between 30 January 2020 till 12 June 2020. Initial containment measures, including point of entry screenings and testing protocols, appeared insufficient. However, testing capacity was gradually expanded after the commencement of a nation-wide lockdown. Modeling predictions have shown varying results on the emergence of cases depending on the infectiousness of asymptomatic individuals, with a peak predicted in mid-July having over two million cases. The country also faces risks of the economic plunge by losing approximately 4% of its gross domestic product, due to containment measures and reduction in goods importation. The low public health expenditure combined with a lack of infrastructure and low fiscal response implies several challenges to scale up the COVID-19 response and management. Therefore, an emergency preparedness and response plan is essential to integrate into the health system of India.

* Abbreviation: COVID-19 — : coronavirus disease 2019 In this issue of Pediatrics , Sisk et al1 used state and territory health department data to describe temporal and geographic trends in coronavirus disease 2019 (COVID-19) among children over a 4-month period. The authors found that in the latter half of the study period, the proportion of cases identified in children increased threefold to fourfold from the start of the study period. We were struck by several features of this report, which we feel have implications for COVID-19 surveillance. First, there was substantial heterogeneity across the data sources aggregated regarding definitions (including the chronological definition of a child) and the data reported. There was also variation across surveillance methods regarding which tests were used and reported. For example, Texas only reports confirmed cases, defined by polymerase chain reaction, and does not report rapid antigen tests, which define a probable case,2 … Address correspondence to Andrea T. Cruz, MD, MPH, Department of Pediatrics, Baylor College of Medicine, 6621 Fannin St, Suite A2210, Houston, TX 77030. E-mail: acruz{at}bcm.edu

Background The National Athletic Treatment, Injury and Outcomes Network Surveillance Program (NATION-SP) was established in 2011 to provide a comprehensive appraisal of injuries sustained by high school student-athletes who received services from athletic trainers (ATs). The purpose of this article is to update the surveillance methods of the NATION-SP for data reported during the 2014–2015 through 2018–2019 academic years. Surveillance System Structure The NATION-SP used a rolling recruitment model to identify a convenience sample of US high schools with access to ATs. The ATs at participating institutions volunteered to contribute data via electronic medical records systems; common data elements were then pushed to and maintained by the Datalys Center for Sports Injury Research and Prevention. The ATs completed detailed reports on each injury, including the condition and circumstances. The treatments component was used to comprehensively assess the services provided to athletes by ATs. The outcomes companion component was developed to monitor patient-reported outcomes after athletic injury. Summary The NATION-SP continues to serve a critical purpose in informing injury-prevention and treatment efforts among high school athletes.

INTRODUCTION AND AIMS. Infoveillance approaches (i.e. surveillance methods using online content) that leverage big data can provide new insights about infectious disease outbreaks and substance use disorder topics. We assessed social media messages about HIV, opioid use and injection drug use in order to understand how unstructured data can prepare public health practitioners for response to future outbreaks. DESIGN AND METHODS. We conducted an retrospective analysis of Twitter messages during the 2015 HIV Indiana outbreak using machine learning, statistical and geospatial analysis to examine the transition between opioid prescription drug abuse to heroin injection use and finally HIV transmission risk, and to test possible associations with disease burden and demographic variables in Indiana and Marion County. Tweets from October 2014 to June 2015 were compared to disease burden at the county level for Indiana, and classification of census blocks by presence of relevant messages was done at the census block level for Marion County. Marion County was used as it exhibited the highest total count of Tweets. RESULTS. 257 messages about substance abuse and HIV were significantly related to HIV rates (P < 0.001) and opioid-related hospitalisations (P = 0.037). Using 157 characteristics from the American Community Survey, a linear classifier was computed with an appreciable correlation (r = 0.49) to risk-related social media messages from Marion County. DISCUSSION AND CONCLUSIONS. Communities appear to communicate online in response to disease burden. Classification produced an accurate equation to model census block risk based on census data, allowing for high-dimensional estimation of risk for blocks with sparse populations.

Background Surgical site infections (SSIs) cause significant patient suffering. Surveillance and feedback of SSI rates is an evidence-based strategy to reduce SSIs, but traditional surveillance methods are slow and prone to bias. The objective of this cluster randomized controlled trial (RCT) is to determine if using optimized statistical process control (SPC) charts for SSI surveillance and feedback lead to a reduction in SSI rates compared to traditional surveillance. Methods The Early 2RIS Trial is a prospective, multicenter cluster RCT using a stepped wedge design. The trial will be performed in 29 hospitals in the Duke Infection Control Outreach Network (DICON) and 105 clusters over 4 years, from March 2016 through February 2020; year one represents a baseline period; thereafter, 8–9 clusters will be randomized to intervention every 3 months over a 3-year period using a stepped wedge randomization design. All patients who undergo one of 13 targeted procedures at study hospitals will be included in the analysis; these procedures will be included in one of six clusters: cardiac, orthopedic, gastrointestinal, OB-GYN, vascular, and spinal. All clusters will undergo traditional surveillance for SSIs; once randomized to intervention, clusters will also undergo surveillance and feedback using optimized SPC charts. Feedback on surveillance data will be provided to all clusters, regardless of allocation or type of surveillance. The primary endpoint is the difference in rates of SSI between the SPC intervention compared to traditional surveillance and feedback alone. Discussion The traditional approach for SSI surveillance and feedback has several major deficiencies because SSIs are rare events. First, traditional statistical methods require aggregation of measurements over time, which delays analysis until enough data accumulate. Second, traditional statistical tests and resulting p values are difficult to interpret. Third, analyses based on average SSI rates during predefined time periods have limited ability to rapidly identify important, real-time trends. Thus, standard analytic methods that compare average SSI rates between arbitrarily designated time intervals may not identify an important SSI rate increase on time unless the “signal” is very strong. Therefore, novel strategies for early identification and investigation of SSI rate increases are needed to decrease SSI rates. While SPC charts are used throughout industry and healthcare to improve and optimize processes, including other types of healthcare-associated infections, they have not been evaluated as a tool for SSI surveillance and feedback in a randomized trial. Trial registration ClinicalTrials.govNCT03075813, Registered March 9, 2017.

IntroductionSpecial Operations Forces conduct military activities using specialised and unconventional techniques that offer a unique and complementary capability to conventional forces. These activities expose Special Operations Forces personnel to different injury risks in comparison with personnel in the conventional forces. Consequently, different injury patterns are expected in this population. The purpose of this research is to establish high-level evidence informing what is known about musculoskeletal injury epidemiology in Special Operations Forces.MethodsA systematic review was conducted using three online databases to identify original studies reporting musculoskeletal injury data in Special Operations Forces. A critical appraisal tool was applied to all included studies. Descriptive data were extracted for demographics, study design details and injuries (eg, injury frequency, injury type, body part injured, activity, mechanism, severity). Results were narratively synthesised.ResultsTwenty-one studies were included. Trainees conducting qualification training had the highest injury frequency, up to 68% injured in a training period. The ankle, knee and lumbar spine were the most common body parts affected. Parachuting caused the most severe injuries. Physical training was the most common activity causing injury, accounting for up to 80% of injuries. Running and lifting were common injury mechanisms. Injury causation information was frequently not reported. Partially validated surveillance methods limited many studies.ConclusionsInjuries are prevalent in Special Operation Forces. Future research should prioritise identifying injury causation information that supports prevention. Focus on improving surveillance methods to enhance the accuracy and comparison of results across cohorts is also recommended.

Digital surveillance methods, such as location tracking apps on smartphones, have been implemented in many countries during the COVID-19 pandemic, but not much is known about predictors of their acceptance. Could it be that prosocial responsibility, to which authorities appealed in order to enhance compliance with quarantine measures, also increases acceptance of digital surveillance and restrictions of privacy? In their fight against the COVID-19 pandemic, governments around the world communicated that self-isolation and social distancing measures are every citizen’s duty in order to protect the health not only of oneself but also of vulnerable others. We suggest that prosocial responsibility besides motivating people to comply with anti-pandemic measures also undermines people’s valuation of privacy. In an online research conducted with US participants, we examined correlates of people’s willingness to sacrifice individual rights and succumb to surveillance with a particular focus on prosocial responsibility. First, replicating prior research, we found that perceived prosocial responsibility was a powerful predictor of compliance with self-isolation and social distancing measures. Second, going beyond prior research, we found that perceived prosocial responsibility also predicted willingness to accept restrictions of individual rights and privacy, as well as to accept digital surveillance for the sake of public health. While we identify a range of additional predictors, the effects of prosocial responsibility hold after controlling for alternative processes, such as perceived self-risk, impact of the pandemic on oneself, or personal value of freedom. These findings suggest that prosocial responsibility may act as a Trojan horse for privacy compromises.

Background Noninfluenza respiratory viruses are responsible for a substantial burden of disease in the United States. Household transmission is thought to contribute significantly to subsequent transmission through the broader community. In the context of the coronavirus disease 2019 (COVID-19) pandemic, contactless surveillance methods are of particular importance. Methods From November 2019 to April 2020, 303 households in the Seattle area were remotely monitored in a prospective longitudinal study for symptoms of respiratory viral illness. Enrolled participants reported weekly symptoms and submitted respiratory samples by mail in the event of an acute respiratory illness (ARI). Specimens were tested for 14 viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), using reverse-transcription polymerase chain reaction. Participants completed all study procedures at home without physical contact with research staff. Results In total, 1171 unique participants in 303 households were monitored for ARI. Of participating households, 128 (42%) included a child aged Conclusions This study highlights the circulation of respiratory viruses within households during the winter months during the emergence of the SARS-CoV-2 pandemic. Contactless methods of recruitment, enrollment, and sample collection were utilized throughout this study and demonstrate the feasibility of home-based, remote monitoring for respiratory infections.

While the clinical, laboratory and epidemiological investigation results of the Ebola outbreak in Likati Health Zone, Democratic Republic of the Congo (DRC) in May 2017 have been previously reported, we provide novel commentary on the contextual, social, and epidemiological characteristics of the epidemic. As first responders with the outbreak Surveillance Team, we explain the procedures that led to a successful epidemiological investigation and ultimately a rapid end to the epidemic. We discuss the role that several factors played in the trajectory of the epidemic, including traditional healers, insufficient knowledge of epidemiological case definitions, a lack of community-based surveillance systems and tools, and remote geography. We also demonstrate how a collaborative Rapid Response Team and implementation of community-based surveillance methods helped counter contextual challenges during the Likati epidemic and aid in identifying and reporting suspected cases and contacts in remote and rural settings. Understanding these factors can hinder or help in the rapid detection, notification, and response to future epidemics in the DRC.

The objectives of the study was to determine the types, challenges and implications of surveillance methods for controlling Covid-19 pandemic. An integrative article review was done. The source of data were documents from WHO, Euro-surveillance, CDC, Saudi CDC, MOH, and journals from PubMed, Medline, etc. The inclusion searching criteria were surveillance, Covid-19, types, benefits and challenges, during the period 2005−2020. Published studies, reviews and guidelines that determined these criteria were collected. Data extraction and analysis were completed for all included articles. A critical appraisal was done based on the University of Michigan Practice Guideline’s levels of evidence. The final sample for the integrative review comprised 30 studies. Results revealed that types of Covid-9 surveillance includes routine surveillance (comprehensive, case-based, and aggregated weakly methods), active, wildlife, syndromic, sentinel and sentinel-syndromic methods. Laboratory and hospital-based surveillance are another important types. Help-lines, surveys, participatory electronic, digital and event-based surveillance are relatively new cost-effective methods. Many surveillance indicators can be calculated. Timely and accurate of surveillance data is an essential element for effective Covid-19 interventions. Regarding challenges, the quality of surveillance in developing countries is constrained by resources and training. The main limitations of surveillance are under-ascertainment/under-reporting, lack of timeliness and completeness of surveillance data. In conclusion, surveillance is a cornerstones for controlling Covid-19 pandemic. Enhancing Covid-19 surveillance is vital for rapid cases detection, containing spread & ending pandemic.

Removing trans fatty acids (TFAs) from the food supply in the Eurasian Economic Union (EAEU) are one of the most effective public health interventions for reducing the risk of noncommunicable diseases. EAEU Member States have taken important steps to reduce TFA in oil and fat products to

Introduction Medical abortion is a safe, effective, and often preferred method of terminating an unintended pregnancy, but access can be made difficult by the laws of a state. Despite modern efforts to prevent unintended pregnancies in the U.S., they comprise almost half of pregnancies and 95% of abortions, signifying that abortion is a necessary and desired healthcare service. This study's purpose is to describe the proliferation of American medical abortion access laws between 2000 and 2018. Methods Policy surveillance methods were used in 2018 to collect 7 types of U.S. medical abortion access restriction laws in place as of December 1, 2018. Statutory histories were reviewed in 2019 to record the laws’ years of enactment, substantive amendment, and repeal. Results A total of 35 states restricted medical abortion access as of 2018. Medical abortion laws increased from 16 in 2000 to 96 by 2018, and only 1 was repealed. A total of 25 states had multiple laws restricting medical abortion access in 2018. Medical abortion access laws surged from 2010 to 2017, but none were passed in 2018. Medical abortion access is generally most restricted in Midwestern and Southern states and least restricted in Western and Northeastern states. Conclusions Although evidence demonstrates medical abortion's safety and efficacy, its access is increasingly limited by law in many states. Further research examining the impacts of these laws on women's health and the consequences of unintended births on women, children, families, and society is needed.

Summary Background Elucidation of the chain of disease transmission and identification of the source of coronavirus disease 2019 (COVID-19) infections are crucial for effective disease containment. We describe an epidemiological investigation that, with use of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serological assays, established links between three clusters of COVID-19. Methods In Singapore, active case-finding and contact tracing were undertaken for all COVID-19 cases. Diagnosis for acute disease was confirmed with RT-PCR testing. When epidemiological information suggested that people might have been nodes of disease transmission but had recovered from illness, SARS-CoV-2 IgG serology testing was used to establish past infection. Findings Three clusters of COVID-19, comprising 28 locally transmitted cases, were identified in Singapore; these clusters were from two churches (Church A and Church B) and a family gathering. The clusters in Church A and Church B were linked by an individual from Church A (A2), who transmitted SARS-CoV-2 infection to the primary case from Church B (F1) at a family gathering they both attended on Jan 25, 2020. All cases were confirmed by RT-PCR testing because they had active disease, except for A2, who at the time of testing had recovered from their illness and tested negative. This individual was eventually diagnosed with past infection by serological testing. ELISA assays showed an optical density of more than 1·4 for SARS-CoV-2 nucleoprotein and receptor binding domain antigens in titres up to 1/400, and viral neutralisation was noted in titres up to 1/320. Interpretation Development and application of a serological assay has helped to establish connections between COVID-19 clusters in Singapore. Serological testing can have a crucial role in identifying convalescent cases or people with milder disease who might have been missed by other surveillance methods. Funding National Research Foundation (Singapore), National Natural Science Foundation (China), and National Medical Research Council (Singapore).

Active influenza A virus (IAV) surveillance in wild waterfowl in the United States has revolved around convenience-based sampling methods, resulting in gaps in surveillance during the spring season. We conducted active IAV surveillance in mallards continuously from July 2017 to July 2019 in the coastal marshes of Lake Erie near Port Clinton, Ohio. We aimed to understand ecological and evolutionary dynamics of IAV across multiple seasons, including the under‑sampled spring season. We collected 2096 cloacal swabs and estimated a 6.1% (95% confidence interval (CI): 0.050&ndash, 0.071) prevalence during the study period. Prevalence was lowest during spring (1.0%, 95% CI: 0.004&ndash, 0.015). Time‑stamped phylogenetic analyses revealed local persistence of genetic lineages of multiple gene segments. The PA segment consists of a lineage detected in multiple seasons with a time to most recent common ancestor of 2.48 years (95% highest posterior density: 2.16&ndash, 2.74). Analysis of the H3 and H6 segments showed close relation between IAVs detected in spring and the following autumn migration. Though the mechanisms behind viral persistence in a single location are not well understood, we provide evidence that viruses can persist across several seasons. Current surveillance methods should be evaluated to ensure they are capturing the breadth of genetic diversity of IAV in waterfowl and prepare for IAV outbreaks in both animals and humans.

Introduction Chikungunya and Zika Virus are vector-borne diseases responsible for a substantial disease burden in the Americas. Between 2013 and 2016, no cases of Chikungunya or Zika Virus were reported by the Venezuelan Ministry of Health. However, peaks of undiagnosed fever cases have been observed during the same period. In the context of scarce data, alternative surveillance methods are needed. Assuming that unusual peaks of acute fever cases correspond to the incidences of both diseases, this study aims to evaluate the use of Google Trends as an indicator of the epidemic behavior of Chikungunya and Zika. Methods Time-series cross-correlations of acute fever cases reported by the Venezuelan Ministry of Health and data on Google search queries related to Chikungunya and Zika were calculated. Results A temporal distinction has been made so that acute febrile cases occurring between 25th of June 2014 and 23rd of April 2015 were attributed to the Chikungunya virus, while cases occurring between 30th of April 2015 and 29th of April 2016 were ascribed to the Zika virus. The highest cross-correlations for each disease were shown at a lag of 0 (r = 0.784) for Chikungunya and at + 1 (r = 0.754) for Zika. Conclusion The strong positive correlation between Google search queries and official data on acute febrile cases suggests that this resource can be used as an indicator of endemic urban arboviruses activity. In the Venezuelan context, Internet search queries might help to overcome some of the gaps that exist in the national surveillance system.

Due to the success of the Global Programme to Eliminate Lymphatic Filariasis (GPELF) many countries have either eliminated the disease as a public health problem or are scheduled to achieve this elimination status in the coming years. The World Health Organization (WHO) recommend that the Transmission Assessment Survey (TAS) is used routinely for post-mass drug administration (MDA) surveillance but it is considered to lack sensitivity in low prevalence settings and not be suitable for post-validation surveillance. Currently there is limited evidence to support programme managers on the design of appropriate alternative strategies to TAS that can be used for post-validation surveillance, as recommended by the WHO. We searched for human and mosquito LF surveillance studies conducted between January 2000 and December 2018 in countries which had either completed MDA or had been validated as having eliminated LF. Article screening and selection were independently conducted. 44 papers met the eligibility criteria, summarising evidence from 22 countries and comprising 83 methodologically distinct surveillance studies. No standardised approach was reported. The most common study type was community-based human testing (n = 42, 47.2%), followed by mosquito xenomonitoring (n = 23, 25.8%) and alternative (non-TAS) forms of school-based human testing (n = 19, 21.3%). Most studies were cross-sectional (n = 61, 73.5%) and used non-random sampling methods. 11 different human diagnostic tests were described. Results suggest that sensitivity of LF surveillance can be increased by incorporating newer human diagnostic tests (including antibody tests) and the use of mosquito xenomonitoring may be able to help identify and target areas of active transmission. Alternative sampling methods including the addition of adults to routine surveillance methods and consideration of community-based sampling could also increase sensitivity. The evidence base to support post-validation surveillance remains limited. Further research is needed on the diagnostic performance and cost-effectiveness of new diagnostic tests and methodologies to guide policy decisions and must be conducted in a range of countries. Evidence on how to integrate surveillance within other routine healthcare processes is also important to support the ongoing sustainability of LF surveillance., Author summary Lymphatic filariasis (LF) is a mosquito-borne disease, which can result in complications including swelling affecting the limbs (lymphoedema) or scrotum (hydrocele). LF can be eliminated by mass drug administration (MDA) which involves whole communities taking drug treatment at regular intervals. After MDA programmes, country programmes conduct the Transmission Assessment Survey (TAS), which tests school children for LF. It is important to continue testing for LF after elimination because there can be a 10-year period between becoming infected and developing symptoms, but it is thought that the use of TAS in such settings is likely to be too expensive and also not sensitive enough to detect low-level infections. Our study assesses the results from 44 studies in areas of low LF prevalence that have investigated methods of surveillance for LF which differ from the standardised TAS approach. These include both human and mosquito studies. Results show that there is currently no standardised approach to testing, but that surveillance can be made more sensitive through the use of new diagnostic tests, such as antibody testing, and also by targeting higher risk populations. However, further research is needed to understand whether these approaches work in a range of settings and whether they are affordable on the ground.

Approximately 2.8 million nonfatal workplace illnesses and injuries were reported in the United States in 2018 (1). Current surveillance methods might underestimate the prevalence of occupational injuries and illnesses (2,3). One way to obtain more information on occupational morbidity is to assess workers' perceptions about whether they have ever experienced health problems related to work (4). Occupational exposures might directly cause, contribute to, exacerbate, or predispose workers to various health problems (work-related health problems). CDC's National Institute for Occupational Safety and Health estimated the lifetime prevalence of self-reported, work-related health problems for the currently employed population overall and stratified by various demographic and job characteristics using data from the 2018 version of the SummerStyles survey. Overall, 35.1% of employed respondents had ever experienced a work-related health problem (95% confidence interval [CI] = 33.0%-37.3%). The most commonly reported work-related health problem was back pain (19.4%, 95% CI = 17.6%-21.2%). Among industries, construction (48.6%, 95% CI = 36.54%-60.58%) had the highest prevalence of any work-related health problems. Workplace injury and illness prevention programs are needed to reduce the prevalence of work-related health problems, especially in higher-risk industries.

Background While early detection and effective control of epidemics depend on appropriate surveillance methods, the Philippines bases its dengue fever surveillance system on a passive surveillance method (notifications from barangay/village health centers, municipal or city health offices, hospitals, and clinics). There is no available mHealth (mobile health) app for dengue fever that includes all the appropriate surveillance methods in early detection of disease outbreaks in the country. Objective This study aimed to evaluate the usability of the Mozzify app in terms of objective quality (engagement, functionality, aesthetics, information) and app subjective and app-specific qualities and compare total app mean score ratings by sociodemographic profile and self and family dengue fever history to see what factors are associated with high app mean score rating among school-based young adult samples and health care professionals. Individual interviews and focus group discussions were also conducted among participants to develop themes from their comments and suggestions to help structure further improvement and future development of the app. Methods User experience sessions were conducted among participants, and the Mobile Application Rating Scale (MARS) professional and user versions (uMARS) were administered followed by individual interviews and focus group discussions. Descriptive statistical analysis of the MARS and uMARS score ratings was performed. The total app mean score ratings by sociodemographic and dengue fever history using nonparametric mean difference analyses were also conducted. Thematic synthesis was used to develop themes from the comments and suggestions raised in individual interviews and focus group discussions. Results Mozzify obtained an overall >4 (out of 5) mean score ratings in the MARS and uMARS app objective quality (4.45), subjective (4.17), and specific (4.55) scales among 948 participants (79 health care professionals and 869 school-based samples). Mean difference analyses revealed that total app mean score ratings were not significantly different across ages and gender among health care professionals and across age, income categories, and self and family dengue fever history but not gender (P Conclusions With its acceptable performance as perceived by health care professionals and school-based young adults, Mozzify has the potential to be used as a strategic health intervention system for early detection of disease outbreaks in the Philippines. It can be used by health care professionals of any age and gender and by school-based samples of any age, socioeconomic status, and dengue fever history. The study also highlights the feasibility of school-based young adults to use health-related apps for disease prevention.

Two vector-borne infections have emerged and spread throughout the north-western part of Europe in the last decade: Bluetongue virus serotype-8 (BTV-8) and the Schmallenberg virus (SBV). The objective of the current study was to compare three statistical methods when applied in a syndromic surveillance context for the early detection of emerging diseases in cattle in the Netherlands. Since BTV-8 and SBV both have a negative effect on milk production in dairy cattle, routinely collected bulk milk recordings were used to compare the three statistical methods in their potential to detect drops in milk production during a period of seven years in which BTV-8 and SBV emerged. A Cusum algorithm, Bayesian disease mapping model, and spatiotemporal cluster analysis using the space-time scan statistic were performed and their performance in terms of sensitivity and specificity was compared. Spatiotemporal cluster analysis performed best for early detection of SBV in cattle in the Netherlands with a relative sensitivity of 71% compared to clinical surveillance and 100% specificity in a year without major disease outbreaks. Sensitivity to detect BTV-8 was low for all methods. However, many alerts of reduced milk production were generated several weeks before the week in which first clinical suspicions were reported. It cannot be excluded that these alerts represent the actual first signs of BTV-8 infections in cattle in the Netherlands thus leading to an underestimation of the sensitivity of the syndromic surveillance methods relative to the clinical surveillance in place.

Objective:In light of the infection risk associated with external ventricular drainage (EVD), we decided to establish the surveillance of EVD-associated meningitis/ventriculitis in German intensive care units (ICUs) in the framework of the German national nosocomial infection surveillance system (KISS). Here, we present the current reference data and subsequent risk-factor analysis for EVD-associated meningitis/ventriculitis rates.Methods:The surveillance method corresponds with the surveillance methods for device-associated infections recommended by the National Healthcare Safety Network (NHSN). All ICUs participating for at least 1 month from 2008 to 2016 in the module ICU-KISS were included in the reference dataset and the multivariate analysis.Results:Current reference data (2008–2016) are based on input from 157 ICUs. The mean EVD-associated meningitis/ventriculitis rate per 1,000 EVD days was 3.96, with little variation between neurosurgical, surgical, interdisciplinary (hospitals with >400 beds), and neurological ICUs. In total, 893 EVD-associated meningitis/ventriculitis cases and 225,351 EVD days were included in the risk-factor analysis. After multivariate analysis, 2 factors remained significant: (1) stay in an ICU labeled other than neurosurgical, surgical, interdisciplinary (>400 beds), and neurological as a protective factor and (2) EVD utilization rate above the 75th quantile as a risk factor for acquisition of EVD-associated meningitis/ventriculitis.Conclusions:EVD-associated meningitis and ventriculitis are frequent complications of care in intensive care patients at risk. A long hospital stay and/or the presence of the EVD puts the patient at high risk for pathogen acquisition with subsequent infection.

Aedes albopictus (Skuse), an invasive disease vector, poses a nuisance and public health threat to communities in the Northeastern United States. Climate change and ongoing adaptation are leading to range expansion of this mosquito into upstate New York and other northeastern states. Organized mosquito control can suppress populations, but it is time consuming, costly, and difficult as Ae. albopictus oviposits in small, artificial, water-holding containers. Unmanned aerial vehicles (UAVs), with centimeter-resolution imaging capabilities, can aid surveillance efforts. In this work, we show that a convolutional neural network trained on images from a UAV is able to detect Ae. albopictus habitat in suburban communities, and the number of containers successfully imaged by UAV predicted the number of containers positive for mosquito larvae per home. The neural network was able to identify some, but not all, potential habitat, with up to 67% precision and 40% recall, and can classify whole properties as positive or negative for larvae 80% of the time. This combined approach of UAV imaging and neutral network analysis has the potential to dramatically increase capacity for surveillance, increasing the reach and reducing the time necessary for conventional on-the-ground surveillance methods.

The aim of this study was to investigate the impact of COVID-19 infection on cardiac surgery community and practice.A 43-question survey was sent to cardiac surgery centers worldwide. The survey analyzed the prepandemic organization of the center, the center's response to Covid-19 in terms of re-organization pathways, surveillance methods, personal-protective equipment (PPE), and allowed surgical practice with results.Sixty-one out of 64 centers (95.3%) fulfilled the survey. One third of ICUs were transformed into COVID-19 dedicated-ICUs and one-third moved to another location inside the hospital. Negative-pressure rooms were available in 60.6% centers. Informative measures from hospital administration were received after the first COVID-19 admitted case in 36.1% and during the spread of the infection inside the hospital in 19.6%. Inadequate supply of PPE was common, with no COVID-surveillance of the medical personnel in 4.9% of centers. COVID-19 infected 7.4% of staff surgeons, 8.3% of residents and 9.5% of anesthetists. Cardiac surgery caseload declined in 93.4% centers. COVID-19 infection in patients receiving cardiac surgery resulted in 41-50% mortality in 9.5% centers, and 91-100% mortality in 4.7% centers. Successful weaning with survival from veno-venous extra corporeal membrane oxygenation (ECMO) and veno-arterial ECMO was50% in 79.2% and 80.0% centers respectively. COVID-19 infection in transplanted patients was rare, with a reported mortality of 0.5% and 1% in one center each.There is room for improvement in hospital surveillance, informative measures and PPE to the personnel. These measurements will reduce current spread of COVID-19 infection among medical personnel and patients, helping the rump up of cardiac surgical practice.

Emergency surveillance following an outbreak of transboundary animal diseases such as classical swine fever (CSF), is conducted to find another new infection as early as possible. Although larger sample sizes can help achieve higher disease surveillance sensitivity, the sample size is limited by the availability of resources in an emergency situation. Moreover, the recent CSF outbreak reported in Japan was associated with fewer clinical signs; this emphasizes the importance of detecting infected farms by surveillance. In this study, we aimed to identify effective and labor-efficient sampling methods showing high probabilities of detecting infection, by simulating infection and sampling in pigsties. We found that impartial sampling, which involves selection of pigs to be sampled from the four corners and the center of the pigsty, and random sampling showed comparable probabilities of detection. Impartial sampling involves sample collection without pig identification and random selection. Owing to its simplicity, impartial sampling is labor-efficient and thus a possible substitute for random sampling. In a group-housing pigsty, testing five pigs from five pens showed a higher detection probability than testing five pigs from one pen. These results suggest preferable surveillance methods for conducting emergency surveillance of infectious diseases.

Purpose: Endoscopic surveillance for Barrett’s oesophagus is undertaken to detect dysplasia and early cancer, and to facilitate early intervention. Evidence supporting current practice is of low quality and often influenced by opinion. This study investigated the preferences of patients for surveillance of Barrett’s oesophagus in an Australian cohort. Methods: Four Barrett’s oesophagus surveillance characteristics/attributes were evaluated within a discrete choice experiment based on literature and expert opinion: (1) surveillance method (endoscopy vs a blood test vs a novel breath test), (2) risk of missing a cancer over a 10-year period, (3) screening interval, and (4) out-of-pocket cost. The data from the discrete choice experiment was analysed within the framework of random utility theory using a mixed logit regression model. Results: The study sample comprised patients (n = 71) undergoing endoscopic surveillance for Barrett’s oesophagus of whom n = 65 completed the discrete choice experiment. The sample was predominantly male (77%) with average age of 65 years. All attributes except surveillance method significantly influenced respondents’ preference for Barrett’s oesophagus surveillance. Policy analyses suggested that compared to the reference case (i.e. endoscopy provided annually at no upfront cost and with a 4% risk of missing cancer), increasing test sensitivity to 0.5% risk of missing cancer would increase participation by up to 50%; surveillance every 5 years would lead to 26% reduction, while every 3 to 3.5 years would result in 7% increase in participation. Respondents were highly averse to paying A$500 for the test, resulting in 48% reduction in participation. None of the other surveillance methods was preferred to endoscopy, both resulting in 11% reduction in participation. Conclusion: Test sensitivity, test frequency and out-of-pocket cost were the key factors influencing surveillance uptake. Patients prefer a test with the highest sensitivity, offered frequently, that incurs no upfront costs. Refereed/Peer-reviewed

A veterinary team’s best work can be undone by a breach in infection control, prevention, and biosecurity (ICPB). Such a breach, in the practice or home-care setting, can lead to medical, social, and financial impacts on patients, clients, and staff, as well as damage the reputation of the hospital. To mitigate these negative outcomes, the AAHA ICPB Guidelines Task Force believes that hospital teams should improve upon their current efforts by limiting pathogen exposure from entering or being transmitted throughout the hospital population and using surveillance methods to detect any new entry of a pathogen into the practice. To support these recommendations, these practice-oriented guidelines include step-by-step instructions to upgrade ICPB efforts in any hospital, including recommendations on the following: establishing an infection control practitioner to coordinate and implement the ICPB program; developing evidence-based standard operating procedures related to tasks performed frequently by the veterinary team (hand hygiene, cleaning and disinfection, phone triage, etc.); assessing the facility’s ICPB strengths and areas of improvement; creating a staff education and training plan; cataloging client education material specific for use in the practice; implementing a surveillance program; and maintaining a compliance evaluation program. Practices with few or no ICPB protocols should be encouraged to take small steps. Creating visible evidence that these protocols are consistently implemented within the hospital will invariably strengthen the loyalties of clients to the hospital as well as deepen the pride the staff have in their roles, both of which are the basis of successful veterinary practice.