The Assessment of Knowledge about Tourette's Syndrome among Medical Students and Primary Physicians in Riyadh, Saudi Arabia: A Cross-Sectional Study.

Background: Tourette's syndrome (TS), a chronic, often disabling neuropsychiatric disorder characterized by motor and vocal tics, is frequently misdiagnosed, or patients are delayed in diagnosis. There is severe deficiency of research about Tourette's syndrome (TS) in the Middle East region. Objectives: To evaluate the knowledge and attitude of medical students and primary care physicians (PCPs) about TS and tic disorders. Methods: IRB approved, cross-sectional study. A total of 316 medical students of King Saud bin Abdulaziz University and 59 primary care physicians of Riyadh participated. Convenient, cluster sampling was used. A validated, self-administered questionnaire was used. Sum of all knowledge questions was calculated. Data were analyzed using SPSS software. Results: Survey was completed by 375 students and physicians, of whom 253 (67.5%) were men. Mean general knowledge score was 61.5 (±12.04) out of 100. Majority (66.1%) knew the diagnostic criteria for TS; only 46.1% considered antipsychotics as effective treatment. Only 25.1% had ever heard of habit reversal; 70% wanted to learn more. Only 10% of physicians had treated a patient with TS. There was no difference in knowledge between men and women (p=0.776). Board-certified physicians had a higher knowledge score (p < 0.05). Family physicians demonstrated higher level of knowledge compared to other physicians (p < 0.05). There was no difference between knowledge of students of different years (p=0.859) or between students and physicians (p=0.569). Conclusion: There was alarming lack of knowledge about Tourette syndrome at various level of medical training and practice including students and physicians. Those who achieved board certification and practiced as family physicians fared better in knowledge about Tourette's syndrome.

Bayesian estimation of ELISA and gamma interferon test accuracy for the detection of bovine tuberculosis in caudal fold test-negative dairy cattle in Kuwait.

Bovine tuberculosis (TB) is endemic in Kuwait; cattle identified as TB-positive using the caudal fold test (CFT) are culled. We used a Bayesian approach to estimate the sensitivity (Se) and specificity (Sp) of the IFNγ assay and ELISA, which are not routinely used in Kuwait in CFT-negative dairy cattle. Blood samples from CFT-negative cattle ( n = 384) collected from 38 dairy farms were tested by IFNγ assay and ELISA. The Se and Sp (95% CI) of the IFNγ were 85.0% (67.6-95.3%) and 90.4% (86.7-95.3%), respectively, whereas estimates for the ELISA were 61.1% (33.1-84.6%) and 85.4% (81.7-88.8%). TB prevalence (95 CI%) in CFT-negative cattle was estimated as 2.6% (0.5-9.5%). The IFNγ assay may play a role as an ancillary test for the identification of Mycobacterium bovis-infected cattle that are undetected by CFT.

Semiquantitative Decision Tools for FMD Emergency Vaccination Informed by Field Observations and Simulated Outbreak Data.

We present two simple, semiquantitative model-based decision tools, based on the principle of first 14 days incidence (FFI). The aim is to estimate the likelihood and the consequences, respectively, of the ultimate size of an ongoing FMD epidemic. The tools allow risk assessors to communicate timely, objectively, and efficiently to risk managers and less technically inclined stakeholders about the potential of introducing FMD suppressive emergency vaccination. To explore the FFI principle with complementary field data, we analyzed the FMD outbreaks in Argentina in 2001, with the 17 affected provinces as the units of observation. Two different vaccination strategies were applied during this extended epidemic. In a series of 5,000 Danish simulated FMD epidemics, the numbers of outbreak herds at day 14 and at the end of the epidemics were estimated under different control strategies. To simplify and optimize the presentation of the resulting data for urgent decisions to be made by the risk managers, we estimated the sensitivity, specificity, as well as the negative and positive predictive values, using a chosen day-14 outbreak number as predictor of the magnitude of the number of remaining post-day-14 outbreaks under a continued basic control strategy. Furthermore, during an ongoing outbreak, the actual cumulative number of detected infected herds at day 14 will be known exactly. Among the number of epidemics lasting >14 days out of the 5,000 simulations under the basic control scenario, we selected those with an assumed accumulated number of detected outbreaks at day 14. The distribution of the estimated number of detected outbreaks at the end of the simulated epidemics minus the number at day 14 was estimated for the epidemics lasting more than 14 days. For comparison, the same was done for identical epidemics (i.e., seeded with the same primary outbreak herds) under a suppressive vaccination scenario. The results indicate that, during the course of an FMD epidemic, simulated likelihood predictions of the remaining epidemic size and of potential benefits of alternative control strategies can be presented to risk managers and other stakeholders in objective and easily communicable ways.

The Use of Spatial and Spatiotemporal Modeling for Surveillance of H5N1 Highly Pathogenic Avian Influenza in Poultry in the Middle East.

Since 2005, H5N1 highly pathogenic avian influenza virus (HPAIV) has severely impacted the economy and public health in the Middle East (ME) with Egypt as the most affected country. Understanding the high-risk areas and spatiotemporal distribution of the H5N1 HPAIV in poultry is prerequisite for establishing risk-based surveillance activities at a regional level in the ME. Here, we aimed to predict the geographic range of H5N1 HPAIV outbreaks in poultry in the ME using a set of environmental variables and to investigate the spatiotemporal clustering of outbreaks in the region. Data from the ME for the period 2005-14 were analyzed using maximum entropy ecological niche modeling and the permutation model of the scan statistics. The predicted range of high-risk areas (P > 0.60) for H5N1 HPAIV in poultry included parts of the ME northeastern countries, whereas the Egyptian Nile delta and valley were estimated to be the most suitable locations for occurrence of H5N1 HPAIV outbreaks. The most important environmental predictor that contributed to risk for H5N1 HPAIV was the precipitation of the warmest quarter (47.2%), followed by the type of global livestock production system (18.1%). Most significant spatiotemporal clusters (P < 0.001) were detected in Egypt, Turkey, Kuwait, Saudi Arabia, and Sudan. Results suggest that more information related to poultry holding demographics is needed to further improve prediction of risk for H5N1 HPAIV in the ME, whereas the methodology presented here may be useful in guiding the design of surveillance programs and in identifying areas in which underreporting may have occurred.

Spatial and Temporal Epidemiology of Lumpy Skin Disease in the Middle East, 2012-2015.

Lumpy skin disease virus (LSDV) is an infectious disease of cattle that can have severe economic implications. New LSD outbreaks are currently circulating in the Middle East (ME). Since 2012, severe outbreaks were reported in cattle across the region. Characterizing the spatial and temporal dynamics of LSDV in cattle populations is prerequisite for guiding successful surveillance and control efforts at a regional level in the ME. Here, we aim to model the ecological niche of LSDV and identify epidemic progression patterns over the course of the epidemic. We analyzed publically available outbreak data from the ME for the period 2012-2015 using presence-only maximum entropy ecological niche modeling and the time-dependent method for the estimation of the effective reproductive number (R-TD). High-risk areas (probability >0.60) for LSDV identified by ecological niche modeling included parts of many northeastern ME countries, though Israel and Turkey were estimated to be the most suitable locations for occurrence of LSDV outbreaks. The most important environmental predictors that contributed to the ecological niche of LSDV included annual precipitation, land cover, mean diurnal range, type of livestock production system, and global livestock densities. Average monthly effective R-TD was equal to 2.2 (95% CI: 1.2, 3.5), whereas the largest R-TD was estimated in Israel (R-TD = 22.2, 95 CI: 15.2, 31.5) in September 2013, which indicated that the demographic and environmental conditions during this period were suitable to LSDV super-spreading events. The sharp drop of Isreal's inferred R-TD in the following month reflected the success of their 2013 vaccination campaign in controlling the disease. Our results identified areas in which underreporting of LSDV outbreaks may have occurred. More epidemiological information related to cattle populations are needed to further improve the inferred spatial and temporal characteristics of currently circulating LSDV. However, the methodology presented here may be useful in guiding the design of risk-based surveillance and control programs in the region as well as aid in the formulation of epidemic preparedness plans in neighboring LSDV-free countries.

Applications of Bayesian Phylodynamic Methods in a Recent U.S. Porcine Reproductive and Respiratory Syndrome Virus Outbreak.

Classical phylogenetic methods such as neighbor-joining or maximum likelihood trees, provide limited inferences about the evolution of important pathogens and ignore important evolutionary parameters and uncertainties, which in turn limits decision making related to surveillance, control, and prevention resources. Bayesian phylodynamic models have recently been used to test research hypotheses related to evolution of infectious agents. However, few studies have attempted to model the evolutionary dynamics of porcine reproductive and respiratory syndrome virus (PRRSV) and, to the authors' knowledge, no attempt has been made to use large volumes of routinely collected data, sometimes referred to as big data, in the context of animal disease surveillance. The objective of this study was to explore and discuss the applications of Bayesian phylodynamic methods for modeling the evolution and spread of a notable 1-7-4 RFLP-type PRRSV between 2014 and 2015. A convenience sample of 288 ORF5 sequences was collected from 5 swine production systems in the United States between September 2003 and March 2015. Using coalescence and discrete trait phylodynamic models, we were able to infer population growth and demographic history of the virus, identified the most likely ancestral system (root state posterior probability = 0.95) and revealed significant dispersal routes (Bayes factor > 6) of viral exchange among systems. Results indicate that currently circulating viruses are evolving rapidly, and show a higher level of relative genetic diversity over time, when compared to earlier relatives. Biological soundness of model results is supported by the finding that sow farms were responsible for PRRSV spread within the systems. Such results cannot be obtained by traditional phylogenetic methods, and therefore, our results provide a methodological framework for molecular epidemiological modeling of new PRRSV outbreaks and demonstrate the prospects of phylodynamic models to inform decision-making processes for routine surveillance and, ultimately, to support prevention and control of food animal disease at local and regional scales.

Novel analytic tools for the study of porcine reproductive and respiratory syndrome virus (PRRSv) in endemic settings: lessons learned in the U.S.

Since its emergence in the late 1980's, the porcine reproductive and respiratory syndrome virus (PRRSv) has posed a significant challenge to the pig industry worldwide. Since then, a number of epidemiological tools have been created to support control and eventual elimination of the disease at the farm and regional levels. Still, many aspects of the disease dynamics are yet-to-be elucidated, such as what are the economically optimal control strategies at the farm and regional level, what is the role that the voluntary regional control programs may play, how to optimize the use of molecular tools for surveillance and monitoring in infected settings, what is the full impact of the disease in a farm, or what is the relative contribution of alternative transmission routes on the occurrence of PRRSv outbreaks. Here, we summarize a number of projects demonstrating the use of novel analytical tools in the assessment of PRRSv epidemiology in the United States. Results presented demonstrate how quantitative analysis of routinely collected data may help in understanding regional epidemiology of PRRSv and to quantify its full impact, and how the integration of phylodynamic methods as a standard tool for molecular surveillance of PRRSv might help to inform control and prevention strategies in high-risk epidemiological situations. Ultimately, these tools will help to support PRRSv control at farm and regional levels in endemically infected settings.

Phylodynamics of H5N1 Highly Pathogenic Avian Influenza in Europe, 2005-2010: Potential for Molecular Surveillance of New Outbreaks.

Previous Bayesian phylogeographic studies of H5N1 highly pathogenic avian influenza viruses (HPAIVs) explored the origin and spread of the epidemic from China into Russia, indicating that HPAIV circulated in Russia prior to its detection there in 2005. In this study, we extend this research to explore the evolution and spread of HPAIV within Europe during the 2005-2010 epidemic, using all available sequences of the hemagglutinin (HA) and neuraminidase (NA) gene regions that were collected in Europe and Russia during the outbreak. We use discrete-trait phylodynamic models within a Bayesian statistical framework to explore the evolution of HPAIV. Our results indicate that the genetic diversity and effective population size of HPAIV peaked between mid-2005 and early 2006, followed by drastic decline in 2007, which coincides with the end of the epidemic in Europe. Our results also suggest that domestic birds were the most likely source of the spread of the virus from Russia into Europe. Additionally, estimates of viral dispersal routes indicate that Russia, Romania, and Germany were key epicenters of these outbreaks. Our study quantifies the dynamics of a major European HPAIV pandemic and substantiates the ability of phylodynamic models to improve molecular surveillance of novel AIVs.

Modeling the association of space, time, and host species with variation of the HA, NA, and NS genes of H5N1 highly pathogenic avian influenza viruses isolated from birds in Romania in 2005-2007.

Molecular characterization studies of a diverse collection of avian influenza viruses (AIVs) have demonstrated that AIVs' greatest genetic variability lies in the HA, NA, and NS genes. The objective here was to quantify the association between geographical locations, periods of time, and host species and pairwise nucleotide variation in the HA, NA, and NS genes of 70 isolates of H5N1 highly pathogenic avian influenza virus (HPAIV) collected from October 2005 to December 2007 from birds in Romania. A mixed-binomial Bayesian regression model was used to quantify the probability of nucleotide variation between isolates and its association with space, time, and host species. As expected for the three target genes, a higher probability of nucleotide differences (odds ratios [ORs] > 1) was found between viruses sampled from places at greater geographical distances from each other, viruses sampled over greater periods of time, and viruses derived from different species. The modeling approach in the present study maybe useful in further understanding the molecular epidemiology of H5N1 HPAI virus in bird populations. The methodology presented here will be useful in predicting the most likely genetic distance for any of the three gene segments of viruses that have not yet been isolated or sequenced based on space, time, and host species during the course of an epidemic.

Alternative scan-based approaches to identify space-time clusters of highly pathogenic avian influenza virus H5N1 in wild birds in Denmark and Sweden in 2006.

The objective of this study was to demonstrate the effects of the nature of the information collected through passive surveillance on the detection of space-time clusters of highly pathogenic avian influenza virus (HPAIV) H5N1 cases reported among dead wild birds in Denmark and Sweden in 2006. Data included 1469 records (109 cases, 1360 controls) collected during the regional epidemic between February and June by passive surveillance of dead wild birds. Laboratory diagnoses were obtained by PCR methods and/or virus isolation. The nature of available information influences both the type of model suitable for analysis and its parameterization. Here, we explored four alternative scan-based methods, suitable for detection of clusters only when case data (univariate permutation model), case and hypothesized epidemiological variables (multivariate permutation model), case and control data (univariate Bernoulli model), and case, control, and hypothesized epidemiological variables (multivariate Bernoulli model) are available. Tufted ducks were particularly common among infected wild bird species detected in Denmark and Sweden during the initial phases of this epidemic, and species group (tufted ducks [62 cases, 57 controls] vs. other wild bird species [47 cases, 1303 controls]) was considered in the multivariate models as a covariate potentially associated with clustering. Bernoulli and permutation scan analyses both detected multiple significant (P < 0.01) clusters with similar locations, but with certain differences in their numbers and sizes. The observed-to-expected case ratios in the two clusters detected by the multivariate Bernoulli scan model were substantially heterogeneous. However, the permutation model detected only one of the Swedish clusters and only pinpointed the heterogeneity between species on clustering in the same Danish cluster as detected by the Bernoulli model. The output of the methods described here were shown to be highly sensitive to the choice of the probability model for cases and the choice of plausible assumptions to parameterize the scan statistic tests. The results of the multivariate Bernoulli suggest that with noncase information regarding a potential risk factor, such as species of birds, this method is sensitive and efficient in identifying high-risk areas and time periods for regional occurrence of HPAIV and potentially for similar infectious diseases. Results here demonstrate the impact that the nature of the collected information has on the epidemiological investigation of outbreaks. Results show the importance of collecting information on control data and on variables hypothesized to influence disease risk on the identification of periods of time and locations at high risk for the disease and risk factors associated with clustering as part of the national and international surveillance systems.

Visualization and analysis of the Danish 2006 highly pathogenic avian influenza virus H5N1 wild bird surveillance data by a prototype avian influenza BioPortal.

Infection with highly pathogenic avian influenza virus H5N1 occurred for the first time in Denmark in 2006 during the last part of the European epidemic that mainly affected migrating wild birds. The total number of Danish wild bird cases was 45, of which only one was found through active surveillance using fecal sampling from resting areas for migrating species, whereas passive surveillance of dead wild birds provided 44 cases. One backyard, mixed poultry flock also became infected late in the epidemic. This study describes the spatial and temporal distribution of cases, initially characterized by a spatial-temporal cluster of affected tufted ducks that led to further spread to other wild bird species in the vicinity. The surveillance data also indicate an apparent die-off of the regional epidemic. As a tool in visualizing the spatial and temporal development of the epidemic, a prototype avian influenza (AI) BioPortal was used to provide online web-based access to the data. The AI BioPortal tools include mapping, graphing, phylogenetic tree construction, playback scenarios, and visualization of results of temporal-spatial analyses. Several of the features of this surveillance system compare favorably to the design of existing national and international surveillance information systems, and the AI BioPortal may become a useful tool for disease surveillance and for decision support in the event of future AI epidemics, both at national and international levels.

Parameterization of the duration of infection stages of serotype O foot-and-mouth disease virus: an analytical review and meta-analysis with application to simulation models.

Foot-and-mouth disease (FMD) is considered one of the most important infectious diseases of livestock because of the devastating economic consequences that it inflicts in affected regions. The value of critical parameters, such as the duration of the latency or the duration of the infectious periods, which affect the transmission rate of the FMD virus (FMDV), are believed to be influenced by characteristics of the host and the virus. Disease control and surveillance strategies, as well as FMD simulation models, will benefit from improved parameter estimation. The objective of this study was to quantify the distributions of variables associated with the duration of the latency, subclinical, incubation, and infectiousness periods of FMDV transmission. A double independent, systematic review of 19 retrieved publications reporting results from experimental trials, using 295 animals in four reference laboratories, was performed to extract individual values related to FMDV transmission. Probability density functions were fitted to data and a set of regression models were used to identify factors associated with the assessed parameters. Latent, subclinical, incubation, and infectious periods ranged from 3.1 to 4.8, 2 to 2.3, 5.5 to 6.6, and 3.3 to 5.7 days, respectively. Durations were significantly (p < 0.05) associated independently with route of exposure, type of donor, animal species, strains, characteristics of sampling, and clinical signs. These results will contribute to the improvement of disease control and surveillance strategies and stochastic models used to simulate FMD spread and, ultimately, development of cost-effective plans to prevent and control the potential spread of the disease in FMD-free regions of the world.