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

Development of a syndromic surveillance system to enhance early detection of emerging and re-emerging animal diseases

Animal health surveillance plays an important role in protecting animal health, production and welfare, public health and trade from the negative impacts of disease. To address the challenges posed by new, exotic or re-emerging diseases as well as the limitations of traditional surveillance, new approaches, including syndromic surveillance (SyS) and modern communication technologies have been developed to improve early disease detection. SyS is based on the continuous monitoring of unspecific pre-diagnostic health data in order to detect an unusual increase in counts which may indicate a health hazard in a timely manner. An increasing number of studies has been investigating different types of animal health data for a possible use in SyS. Although the potential of cattle mortality data routinely collected in national cattle registers for use in a SyS system was highlighted, the performance of aberration-detection algorithms applied to such data has not yet been investigated. Furthermore, knowledge about the impact of delayed reporting of these data on outbreak detection performance is limited. Clinical observations made by veterinary practitioners reported in real-time using web- and mobile-based communication tools may improve the timeliness of outbreak detection. The willingness of practitioners to report their observations is essential for the successful implementation of such systems. A lack of knowledge about factors that motivate or hinder practitioners to participate in surveillance was found. The aim of this work was to contribute to the development of a national surveillance system for the early detection of emerging and re-emerging animal diseases in Switzerland, focusing on two Swiss data sources: cattle mortality data routinely reported by farmers to the Swiss system for individual identification and registration of cattle (Tierverkehrsdatenbank TVD); clinical data voluntarily reported by veterinary practitioners to Equinella, an electronic reporting and information system for the early detection of infectious equine diseases in Switzerland. Time series of on-farm and perinatal cattle deaths, extracted from the TVD, were analysed with regard to data quality and explainable temporal patterns, e.g. day-of-week effect or seasonality. A set of three temporal aberration detection algorithms (Shewhart, CuSum, EWMA) was retrospectively applied to these data to assess their performance in detecting varying simulated disease outbreak scenarios. The effect of reporting delay on outbreak detection was investigated in a Bayesian framework. Participation of veterinary practitioners during the first 12 months of the new internet-based reporting platform of Equinella was assessed. Telephone interviews were conducted to gain insights into factors that motivate or hinder practitioners to participate in a voluntary surveillance system offering non-monetary incentives. Furthermore, the suitability of mobile devices such as smartphones for collecting health data was investigated. The TVD provided timely cattle mortality data with comprehensive geographical information, making it a valuable data source for Sys. Mortality time series exhibited temporal patterns, associated with non-health related factors, that had to be considered before applying aberration detection algorithms. The three evaluated control chart algorithms adequately performed under specific outbreak conditions, but none of them was superior in detecting outbreak signals across multiple evaluation metrics. Combining algorithms outputs according to different rules did not satisfactorily increase the system’s overall performance, further illustrating the difficulty in finding a balance between a high sensitivity and a manageable number of false alarms. The Bayesian approach performed similarly well in the scenario where delayed reporting was accounted for to the (ideal) scenario where it was absent. Non-monetary incentives were attractive to sentinel practitioners and overall participation was experienced positive. Insufficient understanding of the reporting system and of its relevance, as well as concerns over the electronic dissemination of health data were identified as potential challenges to sustainable reporting. Mobile devices were sporadically used during the first year and an awareness of the advantages of mobile-based surveillance was yet lacking among practitioners, indicating that they may require some time to become accustomed to novel reporting methods. This work highlighted the value of routinely collected cattle mortality data for use in SyS, but also the need to carefully optimise aberration detection algorithms for a particular data stream. Alternative methods to the binary alarm system may be chosen for a prospective use of cattle mortality data in a SyS system. The value of evidence framework may be suitable for surveillance systems with multiple syndromes and delayed reporting of data. Before integrating these data into a national surveillance system for the early detection of new, exotic or re-emerging diseases, health authorities need to define response protocols enabling investigation of the data that triggered a statistical alarm and to identify the underlying cause. Possibilities for improving sensitivity and specificity were identified that may be addressed when implementing a future SyS system. In addition, the potential of voluntary reporting surveillance system based on non-monetary incentives was shown. Many of the identified barriers to reporting can be addressed in the future, making the outcome of the pilot project favourable. Continued information feedback loops within voluntary sentinel networks will be important to ensure sustainable participation. Combining reporting of syndromic data and mobile devices in a One Health context has the potential to benefit animal and public health as well as to enhance interdisciplinary collaboration

Divergent evolutionary processes associated with colonization of offshore islands

Peer reviewedPublisher PD

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

Investigating the potential of reported cattle mortality data in Switzerland for syndromic surveillance

Systems for the identification and registration of cattle have gradually been receiving attention for use in syndromic surveillance, a relatively recent approach for the early detection of infectious disease outbreaks. Real or near real-time monitoring of deaths or stillbirths reported to these systems offer an opportunity to detect temporal or spatial clusters of increased mortality that could be caused by an infectious disease epidemic. In Switzerland, such data are recorded in the "Tierverkehrsdatenbank" (TVD). To investigate the potential of the Swiss TVD for syndromic surveillance, 3 years of data (2009-2011) were assessed in terms of data quality, including timeliness of reporting and completeness of geographic data. Two time-series consisting of reported on-farm deaths and stillbirths were retrospectively analysed to define and quantify the temporal patterns that result from non-health related factors. Geographic data were almost always present in the TVD data; often at different spatial scales. On-farm deaths were reported to the database by farmers in a timely fashion; stillbirths were less timely. Timeliness and geographic coverage are two important features of disease surveillance systems, highlighting the suitability of the TVD for use in a syndromic surveillance system. Both time series exhibited different temporal patterns that were associated with non-health related factors. To avoid false positive signals, these patterns need to be removed from the data or accounted for in some way before applying aberration detection algorithms in real-time. Evaluating mortality data reported to systems for the identification and registration of cattle is of value for comparing national data systems and as a first step towards a European-wide early detection system for emerging and re-emerging cattle diseases

Experiences with a voluntary surveillance system for early detection of equine diseases in Switzerland

Clinical observations made by practitioners and reported using web- and mobile-based technologies may benefit disease surveillance by improving the timeliness of outbreak detection. Equinella is a voluntary electronic reporting and information system established for the early detection of infectious equine diseases in Switzerland. Sentinel veterinary practitioners have been able to report cases of non-notifiable diseases and clinical symptoms to an internet-based platform since November 2013. Telephone interviews were carried out during the first year to understand the motivating and constraining factors affecting voluntary reporting and the use of mobile devices in a sentinel network. We found that non-monetary incentives attract sentinel practitioners; however, insufficient understanding of the reporting system and of its relevance, as well as concerns over the electronic dissemination of health data were identified as potential challenges to sustainable reporting. Many practitioners are not yet aware of the advantages of mobile-based surveillance and may require some time to become accustomed to novel reporting methods. Finally, our study highlights the need for continued information feedback loops within voluntary sentinel networks

Input file for isolation-with-migration analysis of common voles, Microtus arvalis

The file contains mitochondrial cytb sequences and microsatellite genotypes of common voles formatted for the IMa program. Samples are labelled with isolate number

Morphological measurements of common voles, Microtus arvalis

Morphological coordinates collected from 2D images of common vole skulls. Raw cartesian coordinates in pixels are given in columns Rawx1, Rawy1 through to Rawx30, Rawy30. Columns with respective names starting with Ali are Proscrustes coordinates; the shape variables after the Procrustes superimposition. Samples are labelled with the population code and number from the individual voucher

Phylogenetic tree from cytb sequences of common voles, Microtus arvalis

TreeBase entry containing DNA sequence alignment of mitochondrial cytb gene, phylogenetic tree from the Bayesian inference analysis, details of the analysis. This constitutes Figure 2 in the paper

Microsatellite genotypes of common voles, Microtus arvalis

Genotypes of 14 microsatellite loci in 24 populations of common voles in Arlequin format. Binned fragment size is given. Samples are labelled with isolate number