A simulation study to evaluate the performance of five statistical monitoring methods when applied to different time-series components in the context of control programs for endemic diseases

Disease monitoring and surveillance play a crucial role in control and eradication programs, as it is important to track implemented strategies in order to reduce and/or eliminate a specific disease. The objectives of this study were to assess the performance of different statistical monitoring methods for endemic disease control program scenarios, and to explore what impact of variation (noise) in the data had on the performance of these monitoring methods. We simulated 16 different scenarios of changes in weekly sero-prevalence. The changes included different combinations of increases, decreases and constant sero-prevalence levels (referred as events). Two space-state models were used to model the time series, and different statistical monitoring methods (such as univariate process control algorithms-Shewart Control Chart, Tabular Cumulative Sums, and the V-mask- and monitoring of the trend component-based on 99% confidence intervals and the trend sign) were tested. Performance was evaluated based on the number of iterations in which an alarm was raised for a given week after the changes were introduced. Results revealed that the Shewhart Control Chart was better at detecting increases over decreases in sero-prevalence, whereas the opposite was observed for the Tabular Cumulative Sums. The trend-based methods detected the first event well, but performance was poorer when adapting to several consecutive events. The V-Mask method seemed to perform most consistently, and the impact of noise in the baseline was greater for the Shewhart Control Chart and Tabular Cumulative Sums than for the V-Mask and trend-based methods. The performance of the different statistical monitoring methods varied when monitoring increases and decreases in disease sero-prevalence. Combining two of more methods might improve the potential scope of surveillance systems, allowing them to fulfill different objectives due to their complementary advantages

Retrospective time series analysis of veterinary laboratory data : Preparing a historical baseline for cluster detection in syndromic surveillance

The practice of disease surveillance has shifted in the last two decades towards the introduction of systems capable of early detection of disease. Modern biosurveillance systems explore different sources of pre-diagnostic data, such as patient's chief complaint upon emergency visit or laboratory test orders. These sources of data can provide more rapid detection than traditional surveillance based on case confirmation, but are less specific, and therefore their use poses challenges related to the presence of background noise and unlabelled temporal aberrations in historical data. The overall goal of this study was to carry out retrospective analysis using three years of laboratory test submissions to the Animal Health Laboratory in the province of Ontario, Canada, in order to prepare the data for use in syndromic surveillance. Daily cases were grouped into syndromes and counts for each syndrome were monitored on a daily basis when medians were higher than one case per day, and weekly otherwise. Poisson regression accounting for day-of-week and month was able to capture the day-of-week effect with minimal influence from temporal aberrations. Applying Poisson regression in an iterative manner, that removed data points above the predicted 95th percentile of daily counts, allowed for the removal of these aberrations in the absence of labelled outbreaks, while maintaining the day-of-week effect that was present in the original data. This resulted in the construction of time series that represent the baseline patterns over the past three years, free of temporal aberrations. The final method was thus able to remove temporal aberrations while keeping the original explainable effects in the data, did not need a training period free of aberrations, had minimal adjustment to the aberrations present in the raw data, and did not require labelled outbreaks. Moreover, it was readily applicable to the weekly data by substituting Poisson regression with moving 95th percentiles

Syndromic surveillance using veterinary laboratory data : data pre-processing and algorithm performance evaluation

Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt-Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel

Surveillance affecting infection control in a veterinary teaching hospital

2017 Spring.Includes bibliographical references.Healthcare-associated infections (HCAI) are poorly understood in veterinary medicine. Methicillin-resistant Staphylococcus pseudintermedius (MRSP) is an increasingly reported pathogen of dogs. Consequently, there are increasing concerns regarding treatment difficulties and propagation of antibiotic resistance. The first study seeks to estimate the burden of MRSP carriage among dogs presenting to the Colorado State University Veterinary Teaching Hospital (CSU-VTH). This study enrolled 243 canine patients across 3 different hospital services upon admission to the VTH and 155 canine patients across 3 different hospital services that received paired samples at two different time points. The 3 hospital services were Community Practice (healthy patients), Dermatology (patients with skin disease) and Surgical Oncology (patients with a higher risk of acquiring an infection during visit). The estimated prevalence of MRSP carriage at enrollment and follow-up was 4%. For enrollment samples, no patients enrolled through Community Practice carried MRSP, while 8% of Dermatology patients and 3% of Surgical Oncology patients were MRSP carriers. For paired samples, carriage persistence was only seen for patients enrolled through Dermatology. Results of this study showed that the prevalence of MRSP carriers among dogs presenting to the CSU-VTH falls within ranges previously published. MRSP colonization was seen most commonly among dogs with skin disease and least commonly among healthy dogs. The second study focuses on surveillance for HCAIs via patient temperatures stored in the electronic medical record (EMR) system of a VTH. Little work has been done in veterinary medicine on surveillance of HCAIs in a VTH. The EMR system contains patient temperature data for each visit. This study explores the association between fevers after admission and known risk factors for HCAIs (e.g. duration of stay in the hospital, critical care involvement). This study included all medical records corresponding to canine visits from the period of January 1, 2012 to June 30, 2015. After selecting for visits of ≥ 1 night and removing missing data, 6,254 unique canine visits remained. Visits were classified into type of case (Medicine, Surgery, Oncology, Other) and whether critical care (ECC) was involved). Length of stay was determined based on admission and discharge date. A visit that produced a fever after admission was a visit where the animal had a normal rectal temperature (≤102.5°F) upon admission and subsequently produced a fever (>102.5°F) after admission. The cumulative incidence of fevers after admission was calculated. Odds ratios (OR) between fevers after admission and case type and ECC involvement and duration of stay in the hospital were calculated via multivariable logistic regression. The estimated cumulative incidence of fevers after admission was 9%. Results of multivariable regression showed that a negative association existed between Medicine-type cases, Oncology-type cases and long duration of hospitalization (>2 days). This study shows that fevers after admission are associated with known risk factors for HCAIs and may be useful in a syndromic approach to HCAI surveillance. This study did not explore the association between HCAI and fevers after admission

Veterinary syndromic surveillance : current initiatives and potential for development

This paper reviews recent progress in the development of syndromic surveillance systems for veterinary medicine. Peer-reviewed and grey literature were searched in order to identify surveillance systems that explicitly address outbreak detection based on systematic monitoring of animal population data, in any phase of implementation. The review found that developments in veterinary syndromic surveillance are focused not only on animal health, but also on the use of animals as sentinels for public health, representing a further step towards One Medicine. The main sources of information are clinical data from practitioners and laboratory data, but a number of other sources are being explored. Due to limitations inherent in the way data on animal health is collected, the development of veterinary syndromic surveillance initially focused on animal health data collection strategies, analyzing historical data for their potential to support systematic monitoring, or solving problems of data classification and integration. Systems based on passive notification or data transfers are now dealing with sustainability issues. Given the ongoing barriers in availability of data, diagnostic laboratories appear to provide the most readily available data sources for syndromic surveillance in animal health. As the bottlenecks around data source availability are overcome, the next challenge is consolidating data standards for data classification, promoting the integration of different animal health surveillance systems, and also the integration to public health surveillance. Moreover, the outputs of systems for systematic monitoring of animal health data must be directly connected to real-time decision support systems which are increasingly being used for disease management and control

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

Vers un modèle de surveillance intégrée des maladies exotiques abortives chez les bovins en France métropolitaine : évaluation de la surveillance évènementielle et exploration d’outils complémentaires de surveillance syndromique

The surveillance system for exotic abortive diseases in French cattle (i.e. abortive diseases that are not currently found in France), such as brucellosis, is a typical example of a surveillance system that is in need of improvement. This type of surveillance only actually exists for brucellosis. Clinical surveillance is the cornerstone of brucellosis surveillance and consists in the mandatory notification of each bovine abortion. However, while no quantitatively assessments have been made, it is common knowledge that this type of surveillance suffers from high levels of under-reporting. By providing an in-depth assessment of the bovine abortion notification surveillance system, we quantified its low sensitivity and identified the influence of structural, human and health factors on how decisions to report abortions are taken. In addition, demographic and reproductive data, collected for purposes of traceability and for genetic performance improvement, were used to devise indirect indicators of abortion occurrence. By modeling the temporal and spatio-temporal variations of these indicators, we highlighted the ability for syndromic surveillance systems to identify the occurrence of abortive events at individual and herd scale. Based on these studies, improving exotic abortive disease surveillance requires revising the mandatory notification surveillance system and developing syndromic surveillance systems. More generally, considering the difficulties in predicting the occurrence of exotic or emerging diseases and their clinical and epidemiological forms, it is necessary to reorganize the surveillance of exotic diseases by setting up integrated surveillance systems that would include different surveillance modalities. Such surveillance systems, implemented by production sector, would focus on known or unknown diseases, showing clinical or subclinical forms, and sporadic, epizootic or diffuse patterns, and would thus maximize the ability to detect exotic or emerging diseasesLa surveillance des maladies abortives chez les bovins actuellement absentes du territoire (dites maladies exotiques), parmi lesquelles figure la brucellose, constitue un cas emblématique de système de surveillance à faire évoluer. Cette surveillance n'est réellement organisée que pour la brucellose. Pour cette maladie, la surveillance évènementielle basée sur la déclaration obligatoire de tout avortement (DA) constitue la pierre angulaire de la surveillance, mais souffre de l'avis de l'ensemble des acteurs, d'une forte sous-déclaration, sans que cela ait été évalué. Dans le cadre de cette thèse, l'évaluation approfondie du dispositif de DA a permis de quantifier la faible sensibilité de ce dispositif et d'identifier l'influence de différents facteurs, structurels, humains et sanitaires, sur le processus de déclaration. En parallèle, des données démographiques et de reproduction, collectées respectivement à des fins de traçabilité des animaux et d'amélioration des performances génétiques, ont été utilisées pour élaborer des indicateurs indirects de survenue d'avortements. La modélisation des variations temporelles et spatio-temporelles de ces indicateurs a souligné la capacité d'outils de surveillance syndromique à identifier la survenue d'évènements abortifs à l'échelle individuelle et des élevages. Au vu de ces travaux, l'amélioration de la surveillance des maladies exotiques abortives passe par le renforcement du dispositif de DA et le développement d'outils de surveillance syndromique. Plus globalement, dans un contexte où les risques d'apparition de maladies exotiques ou émergentes et les formes épidémio-cliniques qu'elles revêtiraient sont très difficilement prévisibles, il apparaît nécessaire de revisiter la surveillance des maladies exotiques et émergentes en définissant des systèmes de surveillance intégrée, déclinés par filière de production, associant différentes modalités de surveillance. De tels systèmes, en couvrant des maladies connues ou non, présentes sous forme clinique ou asymptomatique, et sous forme sporadique, épizootique ou diffuse, optimiseraient les chances de détecter les maladies exotiques ou émergente