Targeted Surveillance Systems Research Articles

Environmental, climatic, anthropogenic features of Filoviruses Disease Outbreak: a systematic review

Abstract Background Filoviruses, including Ebola and Marburg viruses, are a threat to global public health due to their transmission and mortality rates. Despite recent advances in forecasting infectious disease limited knowledge of the ecological niche of Filoviruses still hinders research progress. Studying the ecological context of Filoviruses can provide important information for the development of international preparedness and surveillance systems. Methods We conducted a systematic literature review according to the PRISMA 2020 checklist. A search of relevant articles was performed in PubMed, Scopus, and Web of Science databases. Eligible articles were any type of publication, published in English between 2013 and 2023, discussing environmental, climatic and anthropogenic risk factors of Filovirus disease outbreaks. Results A total of 5818 records were screened, and 41 articles were included. Of the 41 studies analyzed, 21 reported an analysis of risk factors related to Ebola Viruses, 8 of Marburg Virus, and 12 analyzed multiple species of Filoviruses. Of the 41 studies analyzed, 16 also developed risk prediction models aimed at stratifying geographic areas. We collected a total of 19 risk factors divided in 4 categories: environmental risk factors (e.g., forest coverage, cropland coverage), climate risk factors (e.g., annual temperature range, seasonal temporal frequency) population risk factors (e.g., human population density, urban population increase), cultural and behavioral risk factors (e.g., bushmeat trade, use of traditional burial practice). The analyzed models primarily use climate and environmental data, with population and cultural factors being considered to a lesser extent. Conclusions The identification of potential predictors of Filovirus disease outbreak is crucial for geographic mapping of high-risk areas. A multidisciplinary Planetary Health approach is needed to advance research and to develop targeted surveillance systems to prevent pandemic outbreaks. Key messages • Environmental and climate factors play a crucial role in filovirus spillover events, but population and cultural factors should also be considered in future research and risk prediction models. • To improve prediction of pandemic outbreaks more research on disease emergence and transmission dynamics, better surveillance of disease in humans and animals, and better access to data are required.

Molecular Detection and Phylogenetic Analysis of Canine Distemper Virus in Marsican Brown Bear (Ursus arctos marsicanus).

Simple SummaryMarsican brown bear is a subspecies of Eurasian bear, that lives in a few areas of Central Italy, with an estimated population of only 50 animals. For this reason, it is considered one of the most threatened Italian mammals, and specific Conservation Plans are applied with the focus to fight the mortality causes, mainly related to human activities or illegal practices. On the contrary, few reports describing infectious or parasitic diseases in Marsican brown bears are available. Among pathogens, the canine distemper virus (CDV) is responsible for a contagious and multi-organ disease, able to infect a wide range of domestic and wild carnivores. In 2013 a fatal outbreak of distemper was registered in Central Italy, involving dogs, Apennine wolves, badgers, and foxes, but apparently without any consequences for the Marsican brown bears living in the same territories. In this paper, we describe the first CDV infection detected in a live-trapped bear. The identified strain resulted in similarities to CDV recovered from foxes and dogs of the same area. Even if no clinical signs referred to the disease have been detected in the monitored bear, the evidence of a viral pathogen potentially able to menace the conservation of the Marsican brown bear population highlights the importance of continuing observation activities.In this paper, we report the first molecular detection of the canine distemper virus in the Marsican brown bear (Ursus arctos marsicanus). Three subadults and one adult were live-trapped and checked for the main viral pathogens responsible for infectious diseases in this species. The four bears were found to be negative for all investigated viruses except for one, which resulted in a positive outcome for CDV by means of RT-PCR targeting fragments of viral N and H genes. The sequence analysis revealed the specificity of amplicons for the Europe Wildlife lineage of CDV, the same viral strain recovered from three foxes and two unvaccinated dogs coming from the same territories where the positive bear was captured. These results confirm the receptivity of Marsican brown bear for CDV, apparently without any pathological consequences for the positive animal, and suggest the presence in the studied area of a unique wild host-adapted lineage of the virus, able to spread in domestic animals, too. In this respect, continuous and specifically targeted surveillance systems are necessary in order to highlight any changes in the epidemiology of the infection in the territories where the Marsican brown bear lives, along with a more effective vaccination program for domestic dogs co-existing with this endangered species.

COVID-19 Surveillance for Local Decision Making : An Academic, School District, and Public Health Collaboration.

Data-informed decision making is valued among school districts, but challenges remain for local health departments to provide data, especially during a pandemic. We describe the rapid planning and deployment of a school-based COVID-19 surveillance system in a metropolitan US county. In 2020, we used several data sources to construct disease- and school-based indicators for COVID-19 surveillance in Franklin County, an urban county in central Ohio. We collected, processed, analyzed, and visualized data in the COVID-19 Analytics and Targeted Surveillance System for Schools (CATS). CATS included web-based applications (public and secure versions), automated alerts, and weekly reports for the general public and decision makers, including school administrators, school boards, and local health departments. We deployed a pilot version of CATS in less than 2 months (August-September 2020) and added 21 school districts in central Ohio (15 in Franklin County and 6 outside the county) into CATS during the subsequent months. Public-facing web-based applications provided parents and students with local information for data-informed decision making. We created an algorithm to enable local health departments to precisely identify school districts and school buildings at high risk of an outbreak and active SARS-CoV-2 transmission in school settings. Piloting a surveillance system with diverse school districts helps scale up to other districts. Leveraging past relationships and identifying emerging partner needs were critical to rapid and sustainable collaboration. Valuing diverse skill sets is key to rapid deployment of proactive and innovative public health practices during a global pandemic.

Whence the next pandemic? The intersecting global geography of the animal-human interface, poor health systems and air transit centrality reveals conduits for high-impact spillover

The health and economic impacts of infectious disease pandemics are catastrophic as most recently manifested by coronavirus disease 2019 (COVID-19). The emerging infections that lead to substantive epidemics or pandemics are typically zoonoses that cross species boundaries at vulnerable points of animal-human interface. The sharing of space between wildlife and humans, and their domesticated animals, has dramatically increased in recent decades and is a key driver of pathogen spillover. Increasing animal-human interface has also occurred in concert with both increasing globalisation and failing health systems, resulting in a trifecta with dire implications for human and animal health. Nevertheless, to date we lack a geographical description of this trifecta that can be applied strategically to pandemic prevention. This investigation provides the first geographical quantification of the intersection of animal-human interfaces, poor human health system performance and global connectivity via the network of air travel. In so doing, this work provides a systematic, data-driven approach to classifying spillover hazard based on the distribution of animal-human interfaces while simultaneously identifying globally connected cities that are adjacent to these interfaces and which may facilitate global pathogen dissemination. We present this geography of high-impact spillover as a tool for developing targeted surveillance systems and improved health infrastructure in vulnerable areas that may present conduits for future pandemics.

Prioritisation of food-borne parasites in Europe, 2016.

Background and aimsPriority setting is a challenging task for public health professionals. To support health professionals with this and in following a recommendation from the Food and Agriculture Organization of the United Nations (FAO) and World Health Organization (WHO), 35 European parasitologists attended a workshop from 8–12 February 2016 to rank food-borne parasites (FBP) in terms of their importance for Europe and regions within Europe. Methods: Countries were divided into European regions according to those used by the European Society of Clinical Microbiology and Infectious Diseases. We used the same multicriteria decision analysis approach as the FAO/WHO, for comparison of results, and a modified version, for better regional representation. Twenty-five FBP were scored in subgroups, using predefined decision rules. Results: At the European level, Echinococcus multilocularis ranked first, followed by Toxoplasma gondii and Trichinella spiralis. At the regional level, E. multilocularis ranked highest in Northern and Eastern Europe, E. granulosus in South-Western and South-Eastern Europe, and T. gondii in Western Europe. Anisakidae, ranking 17th globally, appeared in each European region’s top 10. In contrast, Taenia solium, ranked highest globally but 10th for Europe. Conclusions: FBP of importance in Europe differ from those of importance globally, requiring targeted surveillance systems, intervention measures, and preparedness planning that differ across the world and across Europe.

Identifying counties vulnerable to diabetes from obesity prevalence in the United States: a spatiotemporal analysis.

Clinical and epidemiological research has reported a strong association between diabetes and obesity. However, whether increased diabetes prevalence is more likely to appear in areas with increased obesity prevalence has not been thoroughly investigated in the United States (US). The Bayesian structured additive regression model was applied to identify whether counties with higher obesity prevalence are more likely clustered in specific regions in 48 contiguous US states. Prevalence data adopted the small area estimate from the Behavioral Risk Factor Surveillance System. Confounding variables like socioeconomic status adopted data were from the American Community Survey. This study reveals that an increased percentage of relative risk of diabetes was more likely to appear in Southeast, Northeast, Central and South regions. Of counties vulnerable to diabetes, 36.8% had low obesity prevalence, and most of them were located in the Southeast, Central, and South regions. The geographic distribution of counties vulnerable to diabetes expanded to the Southwest, West and Northern regions when obesity prevalence increased. This study also discloses that 7.4% of counties had the largest average in predicted diabetes prevalence compared to the other counties. Their average diabetes prevalence escalated from 8.7% in 2004 to 11.2% in 2011. This study not only identifies counties vulnerable to diabetes due to obesity, but also distinguishes counties in terms of different levels of vulnerability to diabetes. The findings can provide the possibility of establishing targeted surveillance systems to raise awareness of diabetes in those counties.

Pig Abattoir Inspection Data: Can It Be Used for Surveillance Purposes?

Statutory recording of carcass lesions at the abattoir may have significant potential as a resource for surveillance of livestock populations. Food Standards Agency (FSA) data in Great Britain are not currently used for surveillance purposes. There are concerns that the sensitivity of detection, combined with other issues, may make the outputs unreliable. In this study we postulate that FSA data could be used for surveillance purposes. To test this we compared FSA data with BPHS (a targeted surveillance system of slaughtered pigs) and laboratory diagnostic scanning surveillance (FarmFile) data, from mid-2008 to mid-2012, for respiratory conditions and tail bite lesions in pigs at population level. We also evaluated the agreement/correlation at batch level between FSA and BPHS inspections in four field trials during 2013. Temporal trends and regional differences at population level were described and compared using logistic regression models. Population temporal analysis showed an increase in respiratory disease in all datasets but with regional differences. For tail bite, the temporal trend and monthly patterns were completely different between the datasets. The field trials were run in three abattoirs and included 322 batches. Pearson’s correlation and Cohen’s kappa tests were used to assess correlation/agreement between inspections systems. It was moderate to strong for high prevalence conditions but slight for low prevalence conditions. We conclude that there is potential to use FSA data as a component of a surveillance system to monitor temporal trends and regional differences of chosen indicators at population level. At producer level and for low prevalence conditions it needs further improvement. Overall a number of issues still need to be addressed in order to provide the pig industry with the confidence to base their decisions on these FSA inspection data. Similar conclusions, at national level, may apply to other livestock sectors but require further evaluation of the inspection and data collection processes.

MOVEMENTS OF LIVE SALMONIDS IN SCOTLAND: CONSERVATION OF NETWORK STRUCTURE

MOVEMENTS OF LIVE SALMONIDS IN SCOTLAND: CONSERVATION OF NETWORK STRUCTURE

光度曲线反演空间目标形状的发展现状

Shape and attitude estimation based on lightcurve inversion provide a new technological approach for space target surveillance systems. Firstly,the necessity of space target information inversion based on lightcurve is discussed. Secondly,the performance of the main three methods of shape estimation are comprehensively summarized,and the method of shape estimation based on nonlinear filter technology is the main development direction in the future. Then,the advantages and disadvantages of nonlinear filter technology are analyzed. Finally,aiming at the problem of inversion,we point out the next research and development direction of fusing multiple sensor information based on the particle filter algorithm with better real-time performance to inverse more complex geometrical shape and surface materials of space target.

Non-uniformity correction of infrared focal plane array in point target surveillance systems

We discuss the influence of non-uniformity and non-uniformity correction on point target detection in infrared surveillance system, and propose a non-uniformity correction approach which is based on signal intensity and sensor characteristics. Theoretical models are used to derive the combined effect of background clutter, sensor random noise, target, non-uniformity and correction error on the signal-to-noise-and-clutter ratio. From our analysis, it can be noted that background clutter intensity is successively modulated by sensor non-uniformity and non-uniformity correction, while sensor random noise is modulated by the non-uniformity correction process only. Furthermore, background clutter and sensor random noise are the key factors that affect the performance of a surveillance system, when it is used to detect point targets. The method presented in this paper takes all of the above into account, moreover, it considers the difference between scanning and staring focal plane array. The experimental results demonstrate the effectiveness of the proposed method.

Estimating risk over time using data from targeted surveillance systems: Application to bovine tuberculosis in Great Britain

For infections that are typically asymptomatic, targeted surveillance systems (whereby individuals at increased risk are tested more frequently) will detect infections earlier on average than systems with random testing or in systems where all individuals are tested at the same intervals. However, estimating temporal trends in infection risk using data from such targeted surveillance systems can be challenging. This is similarly a problem for targeted surveillance to detect faults of individual industrial components. The incidence of bovine tuberculosis (TB) in British cattle has been generally increasing in the last thirty years. Cattle herds are routinely tested for evidence of exposure to the aetiological bacteria Mycobacterium bovis, in a targeted surveillance programme in which the testing interval is determined by past local TB incidence and local veterinary discretion. The UK Department for Environment, Food and Rural Affairs (Defra) report the monthly percentage of tests on officially TB-free (OTF) herds resulting in a confirmed positive test for M. bovis (i.e. the percentage of tested herds with OTF status withdrawn), which contains substantial fluctuations (three years apart) within the increasing trend. As the number of herds tested changes over time, this cyclic trend is difficult to interpret. Here we evaluate an alternative to the Defra method in which we distribute each incident event across the period at risk to infer the underlying trends in infection incidence using a stochastic model of cattle herd incidence and testing frequencies fitted to data on the monthly number of herds tested and number of these with OTF status withdrawn in 2003–2010. We show that for an increasing underlying incidence trend, the current Defra approach can produce artefactual fluctuations whereas the alternative method described provides more accurate descriptions of the underlying risks over time.

Adaptive enhancement of sea-surface targets in infrared images based on local frequency cues

Image enhancement is an important preprocessing step of infrared (IR) based target recognition and surveillance systems. For a better visualization of targets, it is vital to develop image enhancement techniques that increase the contrast between the target and background and emphasize the regions in the target while suppressing noises and background clutter. This study proposes what we believe to be a novel IR image enhancement method for sea-surface targets based on local frequency cues. The image is transformed blockwise into the Fourier domain, and clustering is done according to the number of expected regions to be enhanced in the scene. Based on the variations in the elements in any cluster and the differences between the cluster centers in the frequency domain, two gain matrices are computed for midfrequency and high frequency images by which the image is enhanced accordingly. We provide results for real data and compare the performance of the proposed algorithm through subjective and quantitative tests with four different enhancement methods. The algorithm shows a better performance in the detail visibility of the target.

Surveillance of occupational health disparities: Challenges and opportunities

Increasingly, the occupational health community is turning its attention to the effects of work on previously underserved populations, and researchers have identified many examples of disparities in occupational health outcomes. However, the occupational health status of some underserved worker populations is not described due to limitations in existing surveillance systems. As such, the occupational health community has identified the need to enhance and improve occupational health surveillance to describe the nature and extent of disparities in occupational illnesses and injuries (including fatalities), identify priorities for research and intervention, and evaluate trends. This report summarizes the data sources and methods discussed at an April 2008 workshop organized by NIOSH on the topic of improving surveillance for occupational health disparities. We discuss the capability of existing occupational health surveillance systems to document occupational health disparities and to provide surveillance data on minority and other underserved communities. Use of administrative data, secondary data analysis, and the development of targeted surveillance systems for occupational health surveillance are also discussed. Identifying and reducing occupational health disparities is one of NIOSH's priority areas under the National Occupational Research Agenda (NORA).

Use of epidemiologic information in targeted surveillance for population inference

Epidemiologic information, including animal characteristics (e.g., observable risk factors or clinical signs) predisposing to animal disease, is frequently used for design of targeted surveillance systems, but this information is infrequently used for population inference. In this study, we report the evaluation of use of epidemiologic information for population inference in targeted surveillance in three animal disease scenarios. We adapted sampling theory using Monte Carlo methods to determine target population sample size to detect disease with 95% confidence, using information from the epidemiologic parameters risk ratio and fraction of the population with the characteristic. These parameters and their uncertainties were derived from a reference population. The next step was to use a second (sampled) population to evaluate effects of sampling the targeted population. The focus of the study was on estimation of prevalence. Our results showed that if one is less certain of the epidemiologic parameters, a rational decision is to model the input parameter distributions reflecting this uncertainty, thereby increasing the sample size above the minimum needed for the detection of the disease with a known confidence. Targeted surveillance is appropriate for prevalence estimation when one has representative and justifiable estimates of key epidemiologic parameters.

Respiratory Synctial Virus (RSV) Infection in Hematopoietic Stem Cell Transplant (HCT) Recipients: Risk Factors for Acquisition and Lower Respiratory Tract Disease, and Impact on Mortality.

RSV lower respiratory tract disease (LRTD) is a serious complication after HCT. Risk factors for virus acquisition and progression from RSV upper respiratory tract infection (URI) to RSV LRTD are poorly defined. A targeted surveillance system consisting of routine virologic evaluation for RSV (DFA, shell vial assay, culture) in HCT recipients with URI symptoms was instituted in 10/89. We retrospectively analyzed risk factors of RSV acquisition and the development of RSV-LRTD among patients with RSV-URI between 10/89 and 7/02. From 10/94 to 7/97, patients with RSV-URI received preemptive aerosolized ribavirin (2g/day) for 5–7 d; some patients received full-dose preemptive ribavirin (6g/day). All results are from multivariable models. One hundred forty-seven of 4717 (3.1%) patients were diagnosed with RSV URI and/or LRTD during the first 100 days after HCT. Risk factors for RSV acquisition included bone marrow vs. peripheral blood stem cell (PBSC) (1.7, P=0.01), winter season (P<0.0001), the years 1993 and 1997 when 2 outbreaks occurred (HR 1.7, P=0.01), male sex (HR 1.4, P=0.06). Laminar airflow rooms (HR 0.5, P=0.004) and the period after 1997 (HR 0.65, P=0.09) were protective against acquisition (after 1997, we started to restrict patient contact with staff and caretakers who had an URI with uncontrolled secretions). Risk factors for RSV-LRTD among infected patients included age > 20 year (OR 3.2, P=0.02) and lymphocytopenia < 100/mm3 (OR 4.7, P=0.0005). To assess risk factors for progression from RSV-URI to LRTD, patients who presented with RSV-URI were examined. Lymphocytopenia < 100/mm3 was the only statistically significant factor (OR 14, P<0.001) associated with progression; use of preemptive ribavirin (low- or high-dose) was not statistically significantly associated with a lower progression rate. The development of RSV LRTD as a time-dependent variable was associated with increased mortality after HCT (HR 2.2, p < 0.001), after controlling for age, underlying disease status, donor match and type, conditioning regimen, stem cell source, and CMV serostatus. Thus, RSV acquisition is less common with PBSC transplantation and in a strict isolation setting. Restricting recipients' contact to people with uncontrolled respiratory secretions in the outpatient setting may be beneficial; further studies are needed to confirm this. Lymphocytopenia is an important risk factor for progression from URI to LRTD and RSV-LRTD is independently associated with mortality after HCT.

Surveillance of BSE.

The current method used to identify suspect BSE cases is based on reporting cattle displaying clinical signs compatible with BSE. The reporting of such cases is dependent on the ability of farmers and veterinarians to recognise the disease symptoms and on the willingness to report such cases. Furthermore, it depends on the stage of the disease, because early clinical signs of BSE are not always typical. Histology and immunohistochemistry are established and reliable to confirm BSE in cattle, but the procedure is cumbersome, time consuming and therefore not suited for mass testing of animals. A targeted surveillance system using the Prionics-Western-Blot Test was initiated in Switzerland in 1999. Prionics-positive results are confirmed by histology or immunohistochemistry by the BSE-reference laboratory. This surveillance scheme has confirmed fallen stock and cows subjected to emergency slaughter as the major risk groups. Currently all cattle from these two categories are tested. As a further measure a random sample of cows from regular slaughtering is tested. This enables to determine the BSE status independent of the inaccuracies of a clinical case reporting system. This approach may be helpful to reliably assess the BSE situation in countries with low incidence in order to verify their BSE status and in countries which want to prove their BSE-free status.

Delayed detection of an increase in resistant Acinetobacter at a Detroit hospital.

To study an increase of antimicrobial-resistant Acinetobacter baumannii and to assess reasons for the delayed detection of this increase. Review of medical, laboratory, and infection control records. Plasmid profile analysis of available A baumannii isolates. A 340-bed trauma and intensive care hospital in Detroit, Michigan. The number of hospitalized patients with resistant A baumannii increased during late 1989 and early 1990: 4 in September, 10 in October, 12 in November, 18 in December, and 23 in January (chi square for trend = 14.6, p = .0001). Forty-four (66%) of the 67 patients culture-positive for resistant A baumannii had respiratory tract colonization or infection. Of 11 resistant isolates, 6 had a similar plasmid profile and 5 had no plasmids. Under the hospital's targeted surveillance system, only positive cultures from blood or wounds were investigated; this largely respiratory increase of resistant A baumannii went unrecognized until January 1990. Antimicrobial resistance in A baumannii is an important concern. Such resistance is not necessarily plasmid mediated. Targeted surveillance for this and other agents of nosocomial infection should be used with caution, particularly in hospitals with many debilitated patients.

Multiple sensor data association and fusion in aerospace applications

AbstractThis article presents a summary of some of the issues and methods encountered in the use of multiple sensors for surveillance and tracking problems that arise in aerospace and defense. Applications include air traffic control using multiple internetted ground‐based radar sensors, ship‐based air defense systems, and air‐to‐air systems for drug interdiction and for air combat. During the past decade signal and data processing technology has evolved quickly, in terms of computing power, cost, size, and weight. As a result, the automatic fusion of data from multiple sensors has become a viable way of increasing the capabilities and performance of multiple target surveillance and tracking systems, while simultaneously reducing operator or pilot workload. The design, implementation, and performance of automated single sensor systems are fairly well understood, but the same cannot be said of systems that involve fusion of data from multiple sensors. A number of fundamental issues in this area remain unresolved, and the general problem is an area of active research in both the aerospace and the academic communities.