Timeliness Of Detection Research Articles

Describing the timeliness of detection, notification and response of public health events under International Health Regulations (2005)

Describing the timeliness of detection, notification and response of public health events under International Health Regulations (2005)

Earlier Outbreak Detection-A Generic Model and Novel Methodology to Guide Earlier Detection Supported by Data From Low- and Mid-Income Countries.

Infectious disease outbreaks can have significant impact on individual health, national economies, and social well-being. Through early detection of an infectious disease, the outbreak can be contained at the local level, thereby reducing adverse effects on populations. Significant time and funding have been invested to improve disease detection timeliness. However, current evaluation methods do not provide evidence-based suggestions or measurements on how to detect outbreaks earlier. Key conditions for earlier detection and their influencing factors remain unclear and unmeasured. Without clarity about conditions and influencing factors, attempts to improve disease detection remain ad hoc and unsystematic.Methods: We developed a generic five-step disease detection model and a novel methodology to use for data collection, analysis, and interpretation. Data was collected in two workshops in Southeast Europe (n = 33 participants) and Southern and East Africa (n = 19 participants), representing mid- and low-income countries. Through systematic, qualitative, and quantitative data analyses, we identified key conditions for earlier detection and prioritized factors that influence them. As participants joined a workshop format and not an experimental setting, no ethics approval was required.Findings: Our analyses suggest that governance is the most important condition for earlier detection in both regions. Facilitating factors for earlier detection are risk communication activities such as information sharing, communication, and collaboration activities. Impeding factors are lack of communication, coordination, and leadership.Interpretation: Governance and risk communication are key influencers for earlier detection in both regions. However, inadequate technical capacity, commonly assumed to be a leading factor impeding early outbreak detection, was not found a leading factor. This insight may be used to pinpoint further improvement strategies.

Factors influencing diagnosis and treatment initiation for multidrug-resistant/rifampicin-resistant tuberculosis in six sub-Saharan African countries: a mixed-methods systematic review

BackgroundDrug-resistant tuberculosis burdens fragile health systems in sub-Saharan Africa (SSA), complicated by high prevalence of HIV. Several African countries reported large gaps between estimated incidence and diagnosed or treated cases....

Early detection of sepsis utilizing deep learning on electronic health record event sequences.

The timeliness of detection of a sepsis incidence in progress is a crucial factor in the outcome for the patient. Machine learning models built from data in electronic health records can be used as an effective tool for improving this timeliness, but so far, the potential for clinical implementations has been largely limited to studies in intensive care units. This study will employ a richer data set that will expand the applicability of these models beyond intensive care units. Furthermore, we will circumvent several important limitations that have been found in the literature: (1) Model evaluations neglect the clinical consequences of a decision to start, or not start, an intervention for sepsis. (2) Models are evaluated shortly before sepsis onset without considering interventions already initiated. (3) Machine learning models are built on a restricted set of clinical parameters, which are not necessarily measured in all departments. (4) Model performance is limited by current knowledge of sepsis, as feature interactions and time dependencies are hard-coded into the model. In this study, we present a model to overcome these shortcomings using a deep learning approach on a diverse multicenter data set. We used retrospective data from multiple Danish hospitals over a seven-year period. Our sepsis detection system is constructed as a combination of a convolutional neural network and a long short-term memory network. We assess model quality by standard concepts of accuracy as well as clinical usefulness, and we suggest a retrospective assessment of interventions by looking at intravenous antibiotics and blood cultures preceding the prediction time. Results show performance ranging from AUROC 0.856 (3 h before sepsis onset) to AUROC 0.756 (24 h before sepsis onset). Evaluating the clinical utility of the model, we find that a large proportion of septic patients did not receive antibiotic treatment or blood culture at the time of the sepsis prediction, and the model could, therefore, facilitate such interventions at an earlier point in time. We present a deep learning system for early detection of sepsis that can learn characteristics of the key factors and interactions from the raw event sequence data itself, without relying on a labor-intensive feature extraction work. Our system outperforms baseline models, such as gradient boosting, which rely on specific data elements and therefore suffer from many missing values in our dataset.

Proliferation resistance evaluation of an HTGR transuranic fuel cycle using PRAETOR code

The proliferation resistance (PR) of an inert matrix fuel (IMF) in the transuranic nuclear fuel cycle (NFC) of a high temperature gas cooled reactor is evaluated relative to the uranium and plutonium mixed-oxide (MOX) NFC of a light water reactor using PRAETOR code and sixty-eight input attributes. The objective is to determine the impacts of chemical stability of IMF and fuel irradiation on the PR. Specific material properties of the IMF, such as lower plutonium content, carbide ceramics coating, and absence of 235U, contribute to enhance its relative PR compared to MOX fuel. The overall PR value of the fresh IMF (an unirradiated direct use material with a one-month diversion detection timeliness goal) is nearly equal to that of the spent MOX fuel (an irradiated direct use nuclear material with a three-month diversion detection timeliness goal). Final results suggest a reduced safeguards inspection frequency to manage the IMF.

Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy.

Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions’ COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.

Indicator-based approach to safety management of railway infrastructure facilities

The Aim of this paper is to develop the methods of analysis and simulation of the processes of occurrence and development of emergencies at complex railway infrastructure facilities. It cites analysis data on the threats, causes and consequences of sudden emergencies at complex railway infrastructure facilities. For the purpose of ensuring reliable operation of technical objects, as well as timely identification of faults, it is proposed to use the indicator-based approach that allows diagnosing and formally analyzing the processes of occurrence and propagation of malfunctions across the elements of complex technical systems. For the purpose of simulating the processes of propagation of the disturbances (hazards of emergencies) that occur as the result of malfunctions, it is proposed to use the theoretic graph approach that involves model and visual representation of the structure of a technical system under consideration in the form of a directed graph that shows the correlations between its elements. Each node and edge of a graph is assigned certain parameters or functionals that reflect the processes of correlated operation of the elements of the simulated system. The propagation of disturbances within a system is simulated with pulse processes initiated in one or several nodes. The paper refers to the developed formalized models of disturbance propagation in a technical system based on the construction of structural components and correlation matrices. The authors introduce the concept of critical element of a technical system that helps identify the event of its failure. Two basic criteria of technical system failure, i.e. the exclusive (a system is considered to have failed if the disturbance has reached any of the critical elements) and absolute criterion (failure occurs if the disturbance has reached the specified subset of critical elements) are defined. The paper provides an analytical example that illustrates the capabilities of the proposed model of disturbance propagation within the structure of a technical system. For the purpose more efficient diagnostics of the hazard of emergencies in railway infrastructure facilities the paper proposes a model of application of structurally integrated indicators that consists in the integration of indicators within the structure of a technical system that would immediately deliver the required and sufficient information in case of emergency. The main task would be to identify a set of indicators with the primary purpose of reducing the information-related stress and concentration of dispatchers’ or operators’ attention on the processes within a technical system that are most relevant in terms of accident-free and safe operation. Basic criteria are identified for the generation of the set of indicators within a complex technical system: maximum of reliability of the disturbance consequences estimate, maximum of accuracy of emergency causes identification, minimum of emergency identification time, minimum of nonrecurrent and current costs. A modified graph model of disturbance propagation in a complex technical system is provided that is the prerequisite for solving the multicriterion problems of optimal location of indicators within the structure of a technical system in terms of completeness, accuracy and timeliness of detection of failures of various types. Automation of the processes of generation of indicator sets using models of disturbance propagation in technical systems will allow using the proposed methods as part of further development of the URRAN methodology in terms of improvement of the decision support in railway infrastructure facilities management.

Indicator-based approach to safety management of railway infrastructure facilities

The Aim of this paper is to develop the methods of analysis and simulation of the processes of occurrence and development of emergencies at complex railway infrastructure facilities. It cites analysis data on the threats, causes and consequences of sudden emergencies at complex railway infrastructure facilities. For the purpose of ensuring reliable operation of technical objects, as well as timely identification of faults, it is proposed to use the indicator-based approach that allows diagnosing and formally analyzing the processes of occurrence and propagation of malfunctions across the elements of complex technical systems. For the purpose of simulating the processes of propagation of the disturbances (hazards of emergencies) that occur as the result of malfunctions, it is proposed to use the theoretic graph approach that involves model and visual representation of the structure of a technical system under consideration in the form of a directed graph that shows the correlations between its elements. Each node and edge of a graph is assigned certain parameters or functionals that reflect the processes of correlated operation of the elements of the simulated system. The propagation of disturbances within a system is simulated with pulse processes initiated in one or several nodes. The paper refers to the developed formalized models of disturbance propagation in a technical system based on the construction of structural components and correlation matrices. The authors introduce the concept of critical element of a technical system that helps identify the event of its failure. Two basic criteria of technical system failure, i.e. the exclusive (a system is considered to have failed if the disturbance has reached any of the critical elements) and absolute criterion (failure occurs if the disturbance has reached the specified subset of critical elements) are defined. The paper provides an analytical example that illustrates the capabilities of the proposed model of disturbance propagation within the structure of a technical system. For the purpose more efficient diagnostics of the hazard of emergencies in railway infrastructure facilities the paper proposes a model of application of structurally integrated indicators that consists in the integration of indicators within the structure of a technical system that would immediately deliver the required and sufficient information in case of emergency. The main task would be to identify a set of indicators with the primary purpose of reducing the information-related stress and concentration of dispatchers’ or operators’ attention on the processes within a technical system that are most relevant in terms of accident-free and safe operation. Basic criteria are identified for the generation of the set of indicators within a complex technical system: maximum of reliability of the disturbance consequences estimate, maximum of accuracy of emergency causes identification, minimum of emergency identification time, minimum of nonrecurrent and current costs. A modified graph model of disturbance propagation in a complex technical system is provided that is the prerequisite for solving the multicriterion problems of optimal location of indicators within the structure of a technical system in terms of completeness, accuracy and timeliness of detection of failures of various types. Automation of the processes of generation of indicator sets using models of disturbance propagation in technical systems will allow using the proposed methods as part of further development of the URRAN methodology in terms of improvement of the decision support in railway infrastructure facilities management.

THE CHARACTERISTICS OF ONCO-UROLOGICAL MORBIDITY OF MOSCOW POPULATION IN 2006-2015

The article presents the analysis of trends specific for onco-urological morbidity of Moscow population on the basis of data of official statistical reports for 2006-2015. It is established that prostate cancer is the most actual onco-urological problem in Moscow. The level of morbidity of prostate cancer in the capital exceeds the corresponding average level in the Russian Federation. During 2006-2015, the morbidity of prostate cancer in Moscow increased up to 33.6% i.e. from 52.7 to 70.4 cases per 100 000 of male population. In Moscow, in 2015, morbidity of kidney cancer made up to 11.73 and bladder cancer - 9.72 cases per 100 000 of population. As compared with 2006, morbidity of prostate cancer decreased up to 3.1% and morbidity of bladder cancer increased up to 9.2%. In Moscow, for morbidity of prostate cancer and bladder cancer are specific lower levels in comparison with national average indices. It is demonstrated that efficiency of target oncologic preventive examinations, timeliness of detection of onco-urological diseases and also efficiency of their treatment in Moscow is higher than in the Russian Federation on the whole. At that, in Moscow 31.6% of cases of prostate cancer, 29,7% of cases of kidney cancer and 17.7% of bladder cancer are detected at stage III-IV of disease. The results of implemented study testify necessity of development of urological care of population, including its out-patient section that effects efficiency and timeliness of detection and effectiveness of treatment of onco-urological diseases.

About problem of timeliness of detection of cervix malignant neoplasms in population of Omsk region

The study was carried out to evaluate timeliness of detection of cervix malignant neoplasms against the background of supply of necessary manpower in the region. The indices of active detection of cervix neoplasm and its stages, degree of neglect of tumor process and lethality during the first year of diagnosed disease are considered. The enumerated indices are correlated with quality of medical services шт part of availability and characteristics of training of available personnel. The comparison of data of the Omsk region with average national data was implemented. The corresponding indices of Omsk and municipal districts of oblast in 2004-2014 were also compared. The percentage of active detection ofcervix cancer decreased to the end ofanalyzed period. However, in municipal districts of oblast it continues to be quite high. The detection of disease at early stages (I-II) in the region is on the whole significantly lower as compared with average national indices. The indices of degree of neglect of tumor process in Omsk, in spite of better conditions of diagnostic of this pathology, are significantly lower as compared with corresponding indices in municipal districts. During analyzed period, annual lethality in the Omsk region decreased up to 1.6 times (decreasing rate - 35.6%). The process proceeded especially actively in rural districts of the oblast (decreasing rate - 70.5%). The lethality at first year of diagnosed disease was absent in 25 municipal districts of region. The staffing of oncologists in "The clinical oncologic dispensary" makes up to 100% and 36.8% out of them have highest category. In the central district hospitals, the staffing of specialists made up to 81.3%. Factually in all institutions this position is combined by specialists of another profile i.e. therapists, obstetrician-gynecologists, surgeons, etc.

Detection and response timeliness of crimean Congo hemorrhagic fever and hemorrhagic fever with renal syndrome in Southeast European region

Purpose: Timeliness is the key feature of detection and response of emerging infectious diseases outbreaks and especially hemorrhagic fevers. To better understand gaps, underlining reasons and propose improvements of surveillance systems a multi-country analysis and evaluation of Time to Detect and Time to Respond (TDTR) to haemorrhagic fever outbreaks has been carried out by Southeast European Center for Surveillance and Control of Infectious Diseases (SECID). Methods & Materials: A standardized spreadsheet template has been developed and used by Albania, Kosovo* and Bulgaria to collect in a structured format the surveillance and response timelines data. The analysis of time lags between detection, confirmation, reporting and responding was estimated through descriptive methods based on measures of central tendency and accompanied by related standard deviations and complemented with graphical representations through bar charts and box plots. Results: The data and surveillance process analysis show that the existing surveillance activities present notable gaps. We measured three main days interval groups:1.Day intervals related to disease onset to progress of CCHF and HFRS;2.Day intervals related to CCHF and HFRS surveillance such as reporting.3.Day intervals related to CCHF and HFRS laboratory diagnosis.4.Day interval related to disease control measures and outcomes. Anyhow there was a considerable timeliness gap from first district visit to hospitalization and further clinical suspected cases and laboratory diagnosis respectively mean (+/−): 2,6; 4,3; 6,3; 8,0 days and maximum: 10; 44, 45 and 24 days. Also considerable gaps were seen related to reporting and control measures. Conclusion: The current system does not always allow analysis of disease, event or outbreak timeliness of detection and response especially when the activities are part of different institutions. During the analysis it was clear that response systems are not well described and documented to allow their timeliness and analysis of timeliness of laboratory diagnosis and its interpretation is the key feature to organize further prevention and control measures.

Using Simulated Annealing to Improve the Information Dissemination Network Structure of a Foreign Animal Disease Outbreak Response

AbstractCommunication is an integral part of emergency response, and improving the information dissemination network for crisis communication can save time, resources, and lives. In a foreign animal disease (FAD) outbreak, timeliness of detection and response are critical. An outbreak of foot-and-mouth disease, a particularly significant FAD, could cripple the agriculture economy. This research uses communication data from a FAD response exercise in Kansas to develop a reliable crisis communication network model, contributing a general method for creating an information dissemination network from empirical communication data. We then introduce a simulated annealing heuristic that identifies an alternative network structure that minimizes the time for information to reach all response participants. The resultant network structure reduces overall information transmission time by almost 90% and reveals actionable observations for improving FAD response communication. We find that not only can a crisis communication network be improved significantly, but also that the quantitative results support qualitative observations from early in the data extraction process. This paper adds original methods to the literature and opens the door for future quantitative work in the area of crisis communication and emergency response.

A novel critical point detection method for mechanical deformation in tightening processes

The mechanical deformation of workpieces due to tightening is a common phenomenon in most assembly processes. Such deformation is typically characterized on the basis of a few critical points from sensing signals during process monitoring. Our previous study focus on improving critical point detection accuracy by establishing a state space model and a two-stage particle filter algorithm. The state variables are estimated in the first stage and the critical point is estimated in the second stage. These two stages are recursively estimated until the estimation of critical point converges. However, such method usually requires a large amount of computational efforts which may not be affordable in practice. To effectively identify critical points as well as meet the timeliness of detection, we improve the estimation algorithm by leveraging the quantification of state changes and estimating the critical point in the first stage. In this way, the critical point can be identified within one stage, thereby significantly reducing computation costs. The results from a real case study indicate that our proposed method delivers efficient critical point detection performance for process monitoring.

An unconditional space–time scan statistic for ZIP‐distributed data

AbstractA scan statistic is proposed for the prospective monitoring of spatiotemporal count data with an excess of zeros. The method that is based on an outbreak model for the zero‐inflated Poisson distribution is shown to be superior to traditional scan statistics based on the Poisson distribution in the presence of structural zeros. The spatial accuracy and the detection timeliness of the proposed scan statistic are investigated by means of simulation, and an application on the weekly cases of Campylobacteriosis in Germany illustrates how the scan statistic could be used to detect emerging disease outbreaks. An implementation of the method is provided in the open‐source R package scanstatistics available on the Comprehensive R Archive Network.

Performances of statistical methods for the detection of seasonal influenza epidemics using a consensus-based gold standard.

Influenza epidemics are monitored using influenza-like illness (ILI) data reported by health-care professionals. Timely detection of the onset of epidemics is often performed by applying a statistical method on weekly ILI incidence estimates with a large range of methods used worldwide. However, performance evaluation and comparison of these algorithms is hindered by: (1) the absence of a gold standard regarding influenza epidemic periods and (2) the absence of consensual evaluation criteria. As of now, performance evaluations metrics are based only on sensitivity, specificity and timeliness of detection, since definitions are not clear for time-repeated measurements such as weekly epidemic detection. We aimed to evaluate several epidemic detection methods by comparing their alerts to a gold standard determined by international expert consensus. We introduced new performance metrics that meet important objective of influenza surveillance in temperate countries: to detect accurately the start of the single epidemic period each year. Evaluations are presented using ILI incidence in France between 1995 and 2011. We found that the two performance metrics defined allowed discrimination between epidemic detection methods. In the context of performance detection evaluation, other metrics used commonly than the standard could better achieve the needs of real-time influenza surveillance.

Timely Verification at Large-Scale Gas Centrifuge Enrichment Plants

ABSTRACTThis article examines challenges in international nuclear safeguards pertaining to the timely detection of highly enriched uranium production at large-scale gas centrifuge enrichment plants. To establish where present gas centrifuge enrichment plant safeguards measures and approaches could be strengthened, we have created a discrete time model for simulating hypothetical misuse scenarios, both through transient phases and at steady-state. We find that timely detection of misuse at modern large-scale facilities presents a challenge for international safeguards. A toolbox of unattended measurement systems, along with remote monitoring, however, could be used to improve detection timeliness, enabling the initiation of follow-up activities, potentially on a rapid time scale. These measures, which would need very low false alarm rates, should be implemented in a graded approach, depending on the characteristics of each enrichment plant and an analysis of plausible acquisition paths for the State in which it is situated. Some of these technologies could provide significant benefit to plant operators.

Value of evidence from syndromic surveillance with delayed reporting

ObjectiveWe apply an empirical Bayesian framework to perform changepoint analysis on multiple cattle mortality data streams, accountingfor delayed reporting of syndromes.IntroductionTaking into account reporting delays in surveillance systems isnot methodologically trivial. Consequently, most use the date of thereception of data, rather than the (often unknown) date of the healthevent itself. The main drawback of this approach is the resultingreduction in sensitivity and specificity1. Combining syndromicdata from multiple data streams (most health events may leave a“signature” in multiple data sources) may be performed in a Bayesianframework where the result is presented in the form of a posteriorprobability for a disease2.MethodsWe used a historical national database on Swiss cattle mortality tomodel daily baseline counts of two syndromic time series3. Reportingdelay was defined as the number of days between reported occurrenceand reporting date. The cumulative probability distribution of theestimated reporting delays was used to calculate for each day theproportion of cases that were reported either on the same day or witha delay of 1 to 14 days.We evaluated outbreak detection performance under threescenarios: (A) delayed data reporting occurs but is not accountedfor; (B) delayed data reporting occurs and is accounted for; and (C)absence of delayed data reporting (i.e. an ideal system). Outputsare presented as the value of evidence (V) in favour of an ongoingoutbreak accumulated overnpoints in time (30 days in this case).At each timet, V is defined as the ratio between the posterior andprior odds for H1versus H0:[insert equation 1 here]Using sensitivity, time to detection and in-control run length,performance of the (V-based) system on large and small non-specificoutbreaks was measured.ResultsThe evolution of V based on the information available on the 1st,5th and 10th day after the onset of an outbreak can be visualised inFig. 1. After 5 days, V shows evidence in favour of an outbreak forboth syndromes combined, as well as for on-farm deaths alone, only inthe “Delay aware” and “No delay” scenarios. The development of Vfor the perinatal deaths alone highlights the importance of consideringmultiple syndromic data streams for outbreak detection, as it speaksin favour of an outbreak at a later stage than on-farm deaths alone orboth syndromes combined.ConclusionsOur empirical Bayes approach is an attractive alternative tomultivariate CUSUM algorithms offering a logical approach toweighting variables and incorporating additional information such asdelayed reporting, and a performance on a comparable level to anideal (no delay) system. Outbreaks are detected earlier and with onlya marginal loss of specificity compared to a system where reportingdelay is present but unaccounted for.We also found that the accumulation of evidence from severaldays resulted in a significantly better outbreak detection timeliness,for a given specificity; or a similar timeliness, but higher specificity,compared to an algorithm4that only looks for days with unusual highnumber of counts.Fig. 1: Evolution of V over three time points (t) for the three scenarios.Outbreak starts at t=651. Number of observed perinatal (circle) and on-farmdeaths (cross), V for both (solid grey) and individual syndromes (dotted greyand black respectively), prior probability that an outbreak is ongoing (greydashed) and posterior probability that an outbreak is ongoing given theevidence (black dashed). Horizontal grey solid line shows V=1.

Effectiveness and Cost Efficiency of Different Surveillance Components for Proving Freedom and Early Detection of Disease: Bluetongue Serotype 8 in Cattle as Case Study for Belgium, France and the Netherlands

Quick detection and recovery of country's freedom status remain a constant challenge in animal health surveillance. The efficacy and cost efficiency of different surveillance components in proving the absence of infection or (early) detection of bluetongue serotype 8 in cattle populations within different countries (the Netherlands, France, Belgium) using surveillance data from years 2006 and 2007 were investigated using an adapted scenario tree model approach. First, surveillance components (sentinel, yearly cross-sectional and passive clinical reporting) within each country were evaluated in terms of efficacy for substantiating freedom of infection. Yearly cross-sectional survey and passive clinical reporting performed well within each country with sensitivity of detection values ranging around 0.99. The sentinel component had a sensitivity of detection around 0.7. Secondly, how effective the components were for (early) detection of bluetongue serotype 8 and whether syndromic surveillance on reproductive performance, milk production and mortality data available from the Netherlands and Belgium could be of added value were evaluated. Epidemic curves were used to estimate the timeliness of detection. Sensitivity analysis revealed that expected within-herd prevalence and number of herds processed were the most influential parameters for proving freedom and early detection. Looking at the assumed direct costs, although total costs were low for sentinel and passive clinical surveillance components, passive clinical surveillance together with syndromic surveillance (based on reproductive performance data) turned out most cost-efficient for the detection of bluetongue serotype 8. To conclude, for emerging or re-emerging vectorborne disease that behaves such as bluetongue serotype 8, it is recommended to use passive clinical and syndromic surveillance as early detection systems for maximum cost efficiency and sensitivity. Once an infection is detected and eradicated, cross-sectional screening for substantiating freedom of infection and sentinel for monitoring the disease evolution are recommended.

Vector-borne disease surveillance in livestock populations: A critical review of literature recommendations and implemented surveillance (BTV-8) in five European countries

Preparedness against vector-borne threats depends on the existence of a long-term, sustainable surveillance of vector-borne disease and their relevant vectors. This work reviewed the availability of such surveillance systems in five European countries (Denmark, France, The Netherlands, Sweden and United Kingdom, part of the CoVetLab network). A qualitative assessment was then performed focusing on surveillance directed particularly to BTV-8. Information regarding surveillance activities were reviewed for the years 2008 and 2012. The results were then complemented with a critical scoping review of the literature aimed at identifying disease surveillance strategies and methods that are currently suggested as best suited to target vector-borne diseases in order to guide future development of surveillance in the countries in question.Passive surveillance was found to be efficient for early detection of diseases during the early phase of introduction into a free country. However, its value diminished once the disease has been established in a territory. Detection of emerging diseases was found to be very context and area specific, and thus active surveillance designs need to take the available epidemiological, ecological and entomological information into account. This was demonstrated by the effectiveness of the bulk milk surveillance in detecting the first case in Sweden, highlighting the need for output based standards to allow the most effective, context dependent, surveillance strategies to be used. Preparedness was of fundamental importance in determining the timeliness of detection and control in each country and that this in turn was heavily influenced by knowledge of emerging diseases in neighboring countries. Therefore it is crucial to share information on outbreaks between researchers and decision-makers and across borders continuously in order to react timely in case of an outbreak. Furthermore, timely reaction to an outbreak was heavily influenced by availability of control measures (vaccines), which is also strengthened if knowledge is shared quickly between countries. The assessment of the bluetongue surveillance in the affected countries showed that the degree of voluntary engagement varied, and that it is important to engage the public by general awareness and dissemination of results. The degree of engagement will also aid in establishing a passive surveillance system.

001.6 Exploring the benefits of molecular testing for gonorrhoea antibiotic resistance surveillance in remote settings

Background The latest nucleic acid amplification tests (NAAT) for gonorrhoea are convenient and accurate, and are often used in place of culture-based tests for diagnosis. However the increasing use of NAATs in remote settings in Australia has compromised surveillance for gonorrhoea antimicrobial resistance (AMR). A molecular resistance test that can make use of samples collected for NAAT diagnosis may provide a means to enhance surveillance in remote settings where the availability of samples suitable for culture-based AMR testing is declining. We used a mathematical model to assess the potential benefit of a molecular test in terms of the timeliness of detection of gonorrhoea AMR. Methods An individual-based mathematical model was developed to describe the transmission of gonorrhoea in a remote Indigenous population in Australia. We estimated the impact of the molecular test on the time delay between first importation and the first confirmation that the prevalence of gonorrhoea AMR has breached the WHO-recommended 5% threshold (when a change in antibiotic should occur). Results The model suggests that when culture is the only means of testing for AMR, the breach will only be detected when the actual prevalence of AMR in the population has already reached 8 – 18%. With the addition of a molecular AMR test and assuming AMR can be determined for all samples, the breach will be detected when the actual prevalence of AMR in the population has reached 6%, which only slightly exceeds the recommended notification threshold of 5%. Conclusion Molecular tests have the potential to provide more timely warning of the emergence of gonorrhoea AMR in remote settings where surveillance is compromised by the increased use of NAATs for diagnosis. This in turn will facilitate earlier treatment switching and more targeted treatment, which has the potential to reduce the population impact of gonorrhoea AMR. Disclosure of interest statement This work was supported by National Health and Medical Research Council Project Grants (APP1025517). The National Neisseria Network is funded by the Australian Government Department of Health. The Kirby Institute is funded by the Australian Government Department of Health and Ageing and is affiliated with the Faculty of Medicine, University of New South Wales. The views expressed in this publication do not necessarily represent the position of the Australian Government.