Saraya Tavornpanich

Anthropogenic factors and the risk of highly pathogenic avian influenza H5N1: prospects from a spatial-based model.

Beginning in 2003, highly pathogenic avian influenza (HPAI) H5N1 virus spread across Southeast Asia, causing unprecedented epidemics. Thailand was massively infected in 2004 and 2005 and continues today to experience sporadic outbreaks. While research findings suggest that the spread of HPAI H5N1 is influenced primarily by trade patterns, identifying the anthropogenic risk factors involved remains a challenge. In this study, we investigated which anthropogenic factors played a role in the risk of HPAI in Thailand using outbreak data from the “second wave” of the epidemic (3 July 2004 to 5 May 2005) in the country. We first performed a spatial analysis of the relative risk of HPAI H5N1 at the subdistrict level based on a hierarchical Bayesian model. We observed a strong spatial heterogeneity of the relative risk. We then tested a set of potential risk factors in a multivariable linear model. The results confirmed the role of free-grazing ducks and rice-cropping intensity but showed a weak association with fighting cock density. The results also revealed a set of anthropogenic factors significantly linked with the risk of HPAI. High risk was associated strongly with densely populated areas, short distances to a highway junction, and short distances to large cities. These findings highlight a new explanatory pattern for the risk of HPAI and indicate that, in addition to agro-environmental factors, anthropogenic factors play an important role in the spread of H5N1. To limit the spread of future outbreaks, efforts to control the movement of poultry products must be sustained.

Transmission dynamics of pancreas disease (PD) in a Norwegian fjord: aspects of water transport, contact networks and infection pressure among salmon farms

Pancreas disease (PD) in marine salmon farming is caused by salmon pancreas disease virus (SPDV). Virus survival, infection pressure and contact networks among farms influence the potential of PD to spread. The present study aims to explore contact networks and infection pressure and their ability to explain transmission dynamics of PD in a Norwegian fjord. In this study, we included all records of PD by subtype 3 (SPDV3) in the study population from the first reported in August 2006 to the last reported in November 2009. Using logistic regression analyses, we found that contact network by water transport explained better transmission of PD than contact networks defined by ownership or close distance to infected farms. Hydrodynamic modelling can be a valuable tool to forecast the spread of PD and thereby take actions to reduce the transmission. Knowing the risk of getting infected, it is important to avoid water transport from infected farms when new cohorts are transferred to sea water, and to have conscious control regarding management operations between farms.

Optimizing early detection of avian influenza H5N1 in backyard and free-range poultry production systems in Thailand

For infectious diseases such as highly pathogenic avian influenza caused by the H5N1 virus (A/H5N1 HP), early warning system is essential. Evaluating the sensitivity of surveillance is a necessary step in ensuring an efficient and sustainable system. Stochastic scenario tree modeling was used here to assess the sensitivity of the A/H5N1 HP surveillance system in backyard and free-grazing duck farms in Thailand. The whole surveillance system for disease detection was modeled with all components and the sensitivity of each component and of the overall system was estimated. Scenarios were tested according to selection of high-risk areas, inclusion of components and sampling procedure, were tested. Nationwide passive surveillance (SSC1) and risk-based clinical X-ray (SSC2) showed a similar sensitivity level, with a median sensitivity ratio of 0.96 (95% CI 0.40-15.00). They both provide higher sensitivity than the X-ray laboratory component (SSC3). With the current surveillance design, the sensitivity of detection of the overall surveillance system when the three components are implemented, was equal to 100% for a farm level prevalence of 0.05% and 82% (95% CI 71-89%) for a level of infection of 3 farms. Findings from this study illustrate the usefulness of scenario-tree modeling to document freedom from diseases in developing countries.

Risk map and spatial determinants of pancreas disease in the marine phase of Norwegian Atlantic salmon farming sites

BackgroundOutbreaks of pancreas disease (PD) greatly contribute to economic losses due to high mortality, control measures, interrupted production cycles, reduced feed conversion and flesh quality in the aquaculture industries in European salmon-producing countries. The overall objective of this study was to evaluate an effect of potential factors contributing to PD occurrence accounting for spatial congruity of neighboring infected sites, and then create quantitative risk maps for predicting PD occurrence. The study population included active Atlantic salmon farming sites located in the coastal area of 6 southern counties of Norway (where most of PD outbreaks have been reported so far) from 1 January 2009 to 31 December 2010.ResultsUsing a Bayesian modeling approach, with and without spatial component, the final model included site latitude, site density, PD history, and local biomass density. Clearly, the PD infected sites were spatially clustered; however, the cluster was well explained by the covariates of the final model. Based on the final model, we produced a map presenting the predicted probability of the PD occurrence in the southern part of Norway. Subsequently, the predictive capacity of the final model was validated by comparing the predicted probabilities with the observed PD outbreaks in 2011.ConclusionsThe framework of the study could be applied for spatial studies of other infectious aquatic animal diseases.

Expert consultation on risk factors for introduction of infectious pathogens into fish farms.

An expert consultation was conducted to provide quantitative parameters required to inform risk-based surveillance of aquaculture holdings for selected infectious hazards. The hazards were four fish diseases endemic in some or several European countries: infectious salmon anaemia (ISA), viral haemorrhagic septicaemia (VHS), infectious haematopoietic necrosis (IHN), and koi herpes virus disease (KHD). Experts were asked to provide estimates for the relative importance of 5 risk themes for the hazard to be introduced into and infect susceptible fish at the destination. The 5 risk themes were: (1) live fish and egg movements; (2) exposure via water; (3) on-site processing; (4) short distance mechanical transmission and (5) distance independent mechanical transmission. The experts also provided parameter estimates for hazard transmission pathways within the themes. The expert consultation was undertaken in a 2 step approach: an online survey followed by an expert consultation meeting. The expert opinion indicated that live fish movements and exposure via water were the major relevant risk themes. Experts were recruited from several European countries and thus covered a range of farming systems. Therefore, the outputs from the expert consultation have relevance for the European context.

Diagnostic specificity of a real-time RT-PCR in cattle for foot-and-mouth disease and swine for foot-and-mouth disease and classical swine fever based on non-invasive specimen collection.

Foot-and-mouth disease virus (FMDV) and classical swine fever virus (CSFV) are highly contagious and can cause great economic losses when introduced into disease-free regions. Accurate estimates of diagnostic specificity (Sp) are important when considering the implementation of surveillance for these agents. The purpose of this study was to estimate diagnostic Sp of a real-time reverse-transcriptase PCR assay developed for detection of FMDV in cattle and domestic swine and CSFV in domestic swine based on non-invasive specimen collection. One thousand and eighty-eight range beef cattle were sampled from thirteen geographic locations throughout Texas. One thousand and one hundred market hogs and cull sows were sampled. Results for both FMDV and CSFV were considered positive if amplification occurred at or before 40 PCR cycles, inconclusive between 40 and 45 cycles and negative otherwise. Ten cattle had nonspecific PCR amplifications for FMDV, but none were classified as positive and only one as inconclusive. Specificity (95% confidence interval) was estimated as 100% (99.7, 100). There were 19 nonspecific PCR amplifications for FMDV in sampled swine with 1 classified as positive, 6 as inconclusive, and 12 as negative. Specificity (95% confidence interval) was estimated as 99.9% (99.5, 100). There were 21 nonspecific PCR amplifications for CSFV, and 1 was classified as positive. Specificity (95% confidence interval) was estimated as 99.9% (99.5, 100). These assays have high Sp, but nonspecific PCR amplifications can occur.

Estimation of the reproduction number of salmon pancreas disease virus subtype 3 in homogeneously mixed populations of Norwegian farmed Atlantic salmon.

The reproduction number (R) of salmon pancreas disease (PD) was estimated within homogeneously mixing populations (within-cage) of Norwegian farmed Atlantic salmon (Salmo salar L.) based on data collected during PD epidemics from 10 cages at 2 farming sites. Two approaches were used: (a) estimation of an overall reproduction number (R(cmd)) and a time-dependent reproduction number (R(t)) using mortality records during PD epidemics, and (b) estimating the reproduction number during the early stage of infection (R(sd)) based on data from a surveillance program for SPDV subtype 3. The R(cmd) estimates based on the mortality data ranged from 1.02 to 1.45, and the R(sd) estimates ranged from 1.0 to 2.9. Plots of the R(t) estimates covering the whole epidemic period yielded an increasing slope prior to SPDV3 detection. This study presents a framework for the quantitative measurement of a PD epidemic that could be useful for the evaluation of prevention methods. The time-dependent R(t) estimate can provide an early warning of PD outbreaks.

Routine clinical inspections in Norwegian marine salmonid sites: A key role in surveillance for freedom from pathogenic viral haemorrhagic septicaemia (VHS).

Since the mid-1980s, clinical inspections of aquaculture sites carried out on a regular basis by authorized veterinarians and fish health biologists (known as fish health services: FHS) have been an essential part of aquatic animal health surveillance in Norway. The aims of the present study were (1) to evaluate the performance of FHS routine clinical inspections for the detection of VHS and (2) to explore the effectiveness of risk-based prioritisation of FHS inspections for demonstrating freedom from VHS in marine salmonid sites in Norway. A stochastic simulation model was developed to estimate site sensitivity (SeS), population sensitivity (SeP), and probability of freedom (PFree). The estimation of SeS takes into consideration the probability that FHS submit samples if a site is infected, the probability that a sample is tested if submitted, the effective probability of infection in fish with clinical signs, laboratory test sensitivity, and the number of tested samples. SeP and PFree were estimated on a monthly basis over a 12 month period for six alternative surveillance scenarios and included the risk factors: region, species, area production density, and biosecurity level. Model results indicate that the current surveillance system, based on routine inspections by the FHS has a high capability for detecting VHS and that there is a high probability of freedom from VHS in Norwegian marine farmed salmonids (PFree >95%). Sensitivity analysis identified the probabilities that samples are submitted and submitted samples are tested, as the most influential input variables. The model provides a supporting tool for evaluation of potential changes in the surveillance strategy, and can be viewed as a platform for similar exotic viral infectious diseases in marine salmonid farming in Norway, if they share similar risk factors.

Comparison of three methods of surveillance with application to the detection of Johne's disease seropositivity in Texas cattle

Surveillance and monitoring are important for measuring the level of disease in a population, documenting changes in prevalence over time, determining high-risk areas for implementation of control measures, eradicating disease, and documenting freedom from disease. The documentation of freedom from disease has importance for international trade and the resumption of production after an outbreak. Johnes disease (JD) is an example of an endemic disease of cattle that has variable prevalence related to environmental and animal-level factors. Three methods of sample collection were used to describe the prevalence and distribution of JD seropositivity in Texas. Sampled cattle were: (1) extensively managed herds, (2) market cattle, and (3) clinically ill cattle examined by practicing veterinarians throughout Texas. Samples were evaluated for JD using a commercially available serum ELISA. Proportion of seropositive samples was compared and spatial distributions were evaluated for clustering. Difference of JD seropositivity was observed among the three sample populations suggesting that estimation of disease prevalence is dependant upon the source of samples.

A stochastic network-based model to simulate the spread of pancreas disease (PD) in the Norwegian salmon industry based on the observed vessel movements and seaway distance between marine farms

Pancreas disease (PD) is a viral disease of economic importance affecting farmed Atlantic salmon (Salmo salar L.) and rainbow trout (Oncorhyncus mykiss (Walbaum)) in the seawater phase in Ireland, Norway and Scotland. In this study we used a stochastic network-based disease spread model to better understand the role of vessel movements and nearby seaway distance on PD spread in marine farms. We used five different edges definitions and weights for the network construction: high-risk vessel movements, high-risk wellboat movements and high-risk nearby seaway distance at <20 km, <10 km or <5 km, respectively. Models were used to simulate PD spread in marine farms as well as to simulate the spread of marine SAV2 and SAV3 subtypes independently and results were compared with the observed PD, marine SAV2 and SAV3 cases in Norway in 2016. Results revealed that the model that provided the best fit of the observed data and, therefore, the one considered more biologically plausible, was the one using high-risk wellboat movements. The marine SAV2, SAV3 and PD models using wellboat movements were able to correctly simulate the farms status (PD positive or PD negative) with the sensitivity of 84%, 85%, 84% and Specificity of 98%, 97% and 94%, respectively. These results should contribute to inform more cost-effective prevention and control policies to mitigate PD spread and to improve the sustainability and long-term profitability of the salmon industry in Norway.