A dynamic spatio-temporal model to investigate the effect of cattle movements on the spread of bluetongue BTV-8 in Belgium

Spatio-temporal modelling of Culicoides Latreille (Diptera: Ceratopogonidae) populations on Reunion Island (Indian Ocean)

BACKGROUND

Reunion Island regularly faces outbreaks of bluetongue and epizootic hemorrhagic diseases, two insect-borne orbiviral diseases of ruminants. Hematophagous midges of the genus Culicoides (Diptera: Ceratopogonidae) are the vectors of bluetongue (BTV) and epizootic hemorrhagic disease (EHDV) viruses. In a previous study, statistical models based on environmental and meteorological data were developed for the five Culicoides species present in the island to provide a better understanding of their ecology and predict their presence and abundance. The purpose of this study was to couple these statistical models with a Geographic Information System (GIS) to produce dynamic maps of the distribution of Culicoides throughout the island.

METHODS

Based on meteorological data from ground weather stations and satellite-derived environmental data, the abundance of each of the five Culicoides species was estimated for the 2214 husbandry locations on the island for the period ranging from February 2016 to June 2018. A large-scale Culicoides sampling campaign including 100 farms was carried out in March 2018 to validate the model.

RESULTS

According to the model predictions, no husbandry location was free of Culicoides throughout the study period. The five Culicoides species were present on average in 57.0% of the husbandry locations for C. bolitinos Meiswinkel, 40.7% for C. enderleini Cornet & Brunhes, 26.5% for C. grahamii Austen, 87.1% for C. imicola Kieffer and 91.8% for C. kibatiensis Goetghebuer. The models also showed high seasonal variations in their distribution. During the validation process, predictions were acceptable for C. bolitinos, C. enderleini and C. kibatiensis, with normalized root mean square errors (NRMSE) of 15.4%, 13.6% and 16.5%, respectively. The NRMSE was 27.4% for C. grahamii. For C. imicola, the NRMSE was acceptable (11.9%) considering all husbandry locations except in two specific areas, the Cirque de Salazie-an inner mountainous part of the island-and the sea edge, where the model overestimated its abundance.

CONCLUSIONS

Our model provides, for the first time to our knowledge, an operational tool to better understand and predict the distribution of Culicoides in Reunion Island. As it predicts a wide spatial distribution of the five Culicoides species throughout the year and taking into consideration their vector competence, our results suggest that BTV and EHDV can circulate continuously on the island. As further actions, our model could be coupled with an epidemiological model of BTV and EHDV transmission to improve risk assessment of Culicoides-borne diseases on the island.

A quantitative analysis of the spatial and temporal evolution patterns of the bluetongue virus outbreak in the island of Lesvos, Greece, in 2014

Bluetongue virus (BTV) causes an infectious disease called bluetongue, a vector-borne viral disease of ruminants, which has major implications and causes severe economic damage due to its effect on livestock. These economic costs are mostly ascribed to the trade restrictions imposed during the epidemic period. In August 2014, an epidemic of bluetongue occurred in the island of Lesvos, Greece. The epidemic was severe and evolved over time, lasting until December 2014. The total cases of infected farms were 490, including a total number of 136,368 small ruminants. In this paper, we describe a bluetongue virus serotype 4 (BTV-4) epidemic and utilize Bayesian epidemic models to capture the spatio-temporal spread of the disease. Our study provides important insights into the drivers of BTV transmission and has implications for designing control strategies. The results showed strong spatial autocorrelations, with BTV being more likely to spread between farms located nearby. The spatial modelling results proposed a certain spatial radius (~12 km) around the onset of a similar epidemic for imposing restrictions on animal movement, which can be sufficient for the control of the disease and limit economic damage.

A network-based approach to modelling bluetongue spread in France

Bluetongue virus serotype 8 (BTV-8) was reported for the first time in Europe in 2006, causing the largest bluetongue outbreak ever recorded. France was mostly impacted in 2007/09. Trade restrictions were implemented all along. Vaccination became available from 2008: a limited number of doses was first administered in an emergency vaccination campaign, followed by two nationwide compulsory vaccination campaigns in 2009 and 2010. France regained a disease-free status in December 2012, but BTV may have kept circulating undetected as infected herds have been reported again since August 2015. We developed a stochastic dynamic compartmental model of BTV transmission in cattle and sheep to analyze the relative importance of vector active flight and host movements in disease spread, and assess the effectiveness of control measures. We represented BTV transmission both within and between French administrative subdivisions called cantons, during the 2007/09 outbreak and until the end of 2010, when compulsory vaccination was interrupted. Within-canton transmission was vector-borne, and between canton transmission could occur through three contact networks that accounted for movements of: (i) vectors between pastures located at close distance; (ii) cattle and sheep between pastures of the same farm; (iii) traded cattle. We estimated the model parameters by approximate Bayesian computation, using data from the 2007 French outbreak. With this framework, we were able to reproduce the BTV-8 epizootic wave. Host movements between distant pastures of the same farm were found to have a major contribution to BTV spread to disease-free areas, thus raising practical questions about herd management during outbreaks. We found that cattle trade restrictions had been well complied with; without them, the whole French territory would have been infected by winter 2007. The 2008 emergency vaccination campaign had little impact on disease spread as almost half vaccine doses had likely been administered to already immune cattle. Alternatively, establishing a vaccination buffer zone would have allowed a better control of BTV in 2008: limiting its spatial expansion and decreasing the number of infected cattle and sheep. We also showed a major role of compulsory vaccination in controlling the outbreak in 2009 and 2010, though we predicted a possible low-level circulation after the last detection.

Bluetongue transmission and control in Europe: A systematic review of compartmental mathematical models

The growing frequency of bluetongue virus (BTV) incursions in Europe in recent years led to the largest BTV outbreak ever recorded in 2006/09, with a dramatic impact on the cattle and sheep industries. The complex epidemiology of this vector-borne disease of ruminants and its recent emergence need to be better understood to identify and implement efficient control strategies. Mathematical models provide useful tools for that purpose; many of them have been developed in the light of the 2006/09 outbreak. We aimed to provide a systematic review of compartmental mathematical models dedicated to BTV occurrence or transmission in European countries, to assess robustness of findings to different modelling approaches and assumptions. We identified relevant papers from PubMed and Scopus databases, 21 of which were included in the review following the selection process laid out in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. We systematically extracted data from these papers to address the diversity and evolution of modelling approaches, and to identify important characteristics for future model development. Then, we summarized the main insights provided into bluetongue epidemiology, and discussed the relevance of these models as tools for risk mapping and for the design of surveillance and control systems. On the whole, the mechanistic models reviewed provided flexible frameworks, yielding mostly epidemiological insights specific to geographical areas and study periods. Despite the limitations of these models that sometimes relied on strong assumptions, we advocate their use to facilitate and inform evidence-based decision-making in animal health.

Ambient Air Pollution-related Mortality in Dairy Cattle: Does It Corroborate Human Findings?

BACKGROUND

Despite insights for humans, short-term associations of air pollution with mortality to our knowledge have never been studied in animals. We investigated the association between ambient air pollution and risk of mortality in dairy cows and assessed effect modification by season.

METHODS

We collected ozone (O3), particulate matter (PM10), and nitrogen dioxide (NO2) concentrations at the municipality level for 87,108 dairy cow deaths in Belgium from 2006 to 2009. We combined a case-crossover design with time-varying distributed lag models.

RESULTS

We found acute and delayed associations between air pollution and dairy cattle mortality during the warm season. The increase in mortality for a 10 μg/m increase in 2-day (lag 0-1) O3 was 1.2% (95% confidence interval [CI] = 0.3%, 2.1%), and the corresponding estimates for a 10 μg/m increase in same-day (lag 0) PM10 and NO2 were 1.6% (95% CI = 0.0%, 3.1%) and 9.2% (95% CI = 6.3%, 12%), respectively. Compared with the acute increases, the cumulative 26-day (lag 0-25) estimates were considerably larger for O3 (3.0%; 95% CI = 0.2%, 6.0%) and PM10 (3.2%; 95% CI = -0.6%, 7.2%), but not for NO2 (1.4%; 95% CI = -4.9%, 8.2%). In the cold season, we only observed increased mortality risks associated with same-day (lag 0) exposure to NO2 (1.4%; 95% CI = -0.1%, 3.1%) and with 26-day (lag 0-25) exposure to O3 (4.6%; 95% CI = 2.2%, 7.0%).

CONCLUSIONS

Our study adds to the epidemiologic findings in humans and reinforces the evidence on the plausibility of causal effects. Furthermore, our results indicate that air pollution associations go beyond short-term mortality displacement. (See video abstract at http://links.lww.com/EDE/B105.).

Quantifying the roles of host movement and vector dispersal in the transmission of vector-borne diseases of livestock

The role of host movement in the spread of vector-borne diseases of livestock has been little studied. Here we develop a mathematical framework that allows us to disentangle and quantify the roles of vector dispersal and livestock movement in transmission between farms. We apply this framework to outbreaks of bluetongue virus (BTV) and Schmallenberg virus (SBV) in Great Britain, both of which are spread by Culicoides biting midges and have recently emerged in northern Europe. For BTV we estimate parameters by fitting the model to outbreak data using approximate Bayesian computation, while for SBV we use previously derived estimates. We find that around 90% of transmission of BTV between farms is a result of vector dispersal, while for SBV this proportion is 98%. This difference is a consequence of higher vector competence and shorter duration of viraemia for SBV compared with BTV. For both viruses we estimate that the mean number of secondary infections per infected farm is greater than one for vector dispersal, but below one for livestock movements. Although livestock movements account for a small proportion of transmission and cannot sustain an outbreak on their own, they play an important role in establishing new foci of infection. However, the impact of restricting livestock movements on the spread of both viruses depends critically on assumptions made about the distances over which vector dispersal occurs. If vector dispersal occurs primarily at a local scale (99% of transmission occurs <25 km), movement restrictions are predicted to be effective at reducing spread, but if dispersal occurs frequently over longer distances (99% of transmission occurs <50 km) they are not.

Diseases which are transmitted by the bites of insects can be spread to new locations through the movement of both infected insects and infected hosts. The importance of these routes has implications for disease control, because we can often restrict host movement, and so potentially reduce spread, but cannot easily restrict insect movements. Despite this, the importance of host movements has been little studied. Here we develop a mathematical model which allows us to disentangle and quantify transmission by insect dispersal and by host movement. We apply the model to two diseases of cattle and sheep transmitted by biting midges that have emerged in northern Europe in the past decade, bluetongue virus (BTV) and Schmallenberg virus (SBV). For both viruses, we show insect movements account for a majority of spread between farms. Although they cannot sustain an epidemic on their own, animal movements play an important role in introducing disease to new areas.

Epidemic predictions in an imperfect world: modelling disease spread with partial data

‘Big-data’ epidemic models are being increasingly used to influence government policy to help with control and eradication of infectious diseases. In the case of livestock, detailed movement records have been used to parametrize realistic transmission models. While livestock movement data are readily available in the UK and other countries in the EU, in many countries around the world, such detailed data are not available. By using a comprehensive database of the UK cattle trade network, we implement various sampling strategies to determine the quantity of network data required to give accurate epidemiological predictions. It is found that by targeting nodes with the highest number of movements, accurate predictions on the size and spatial spread of epidemics can be made. This work has implications for countries such as the USA, where access to data is limited, and developing countries that may lack the resources to collect a full dataset on livestock movements.

Evaluation of farm-level parameters derived from animal movements for use in risk-based surveillance programmes of cattle in Switzerland

Background

This study focused on the descriptive analysis of cattle movements and farm-level parameters derived from cattle movements, which are considered to be generically suitable for risk-based surveillance systems in Switzerland for diseases where animal movements constitute an important risk pathway.

Methods

A framework was developed to select farms for surveillance based on a risk score summarizing 5 parameters. The proposed framework was validated using data from the bovine viral diarrhoea (BVD) surveillance programme in 2013.

Results

A cumulative score was calculated per farm, including the following parameters; the maximum monthly ingoing contact chain (in 2012), the average number of animals per incoming movement, use of mixed alpine pastures and the number of weeks in 2012 a farm had movements registered. The final score for the farm depended on the distribution of the parameters. Different cut offs; 50, 90, 95 and 99 %, were explored. The final scores ranged between 0 and 5. Validation of the scores against results from the BVD surveillance programme 2013 gave promising results for setting the cut off for each of the five selected farm level criteria at the 50th percentile. Restricting testing to farms with a score ≥ 2 would have resulted in the same number of detected BVD positive farms as testing all farms, i.e., the outcome of the 2013 surveillance programme could have been reached with a smaller survey.

Conclusions

The seasonality and time dependency of the activity of single farms in the networks requires a careful assessment of the actual time period included to determine farm level criteria. However, selecting farms in the sample for risk-based surveillance can be optimized with the proposed scoring system. The system was validated using data from the BVD eradication program. The proposed method is a promising framework for the selection of farms according to the risk of infection based on animal movements.

Confirmation of spatial patterns and temperature effects in Bluetongue virus serotype-8 transmission in NW-Europe from the 2007 reported case data

Two separate analyses were carried out to understand the epidemiology of Bluetongue virus serotype 8 (BTV-8) in 2007 in North West Europe: First, the temporal change in transmission rates was compared to the evolution of temperature during that season. Second, we evaluated the spatio-temporal dynamics of newly reported outbreaks, to estimate a spatial transmission kernel. For both analyses, the approach as used before in analysing the 2006 BTV-8 epidemic had to be adapted in order to take into account the fact that the 2007 epidemic was not a newly arising epidemic, but one advancing from whereto it had already spread in 2006. We found that within the area already affected by the 2006 outbreak, the pattern of newly infected farms in 2007 cannot be explained by between-farm transmission, but rather by local re-emergence of the virus throughout that region. This indicates that persistence through winter was ubiquitous for BTV-8. Just like in 2006, we also found that the temperature at which the infection starts to spread lies close to 15 °C. Finally, we found that the shape of the transmission kernel is in line with the one from the 2006 epidemic. In conclusion, despite the substantial differences between 2006 and 2007 in temperature patterns (2006 featured a heat wave in July, whereas 2007 was more regular) and spatial epidemic extent, both the minimum temperature required for transmission and the transmission kernel were similar to those estimated for the 2006 outbreak, indicating that they are robust properties, suitable for extrapolation to other years and similar regions.

Exploring cattle movements in Belgium

Movement of animals from one farm to another is a potential risk and can lead to the spreading of livestock diseases. Therefore, in order to implement effective control measures, it is important to understand the movement network in a given area. Using the SANITEL data from 2005 to 2009, around 2 million cattle movements in Belgium were traced. Exploratory analysis revealed different spatial structures for the movement of different cattle types: fattening calves are mostly moved to the Antwerp region, adult cattle are moved to different parts in Belgium. Based on these differences, movement of cattle would more likely cause a spread of disease to a larger number of areas in Belgium as compared to the fattening calves. A closer inspection of the spatial and temporal patterns of cattle movement using a weighted negative binomial model, revealed a significant short-distance movement of bovine which could be an important factor contributing to the local spreading of a disease. The model however revealed hot spot areas of movement in Belgium; four areas in the Walloon region (Luxembourg, Hainaut, Namur and Liege) were found as hot spot areas while East and West Flanders are important "receivers" of movement. This implies that an introduction of a disease to these Walloon regions could result in a spread toward the East and West Flanders regions, as what happened in the case of Bluetongue BTV-8 outbreak in 2006. The temporal component in the model also revealed a linear trend and short- and long-term seasonality in the cattle movement with a peak around spring and autumn. The result of this explorative analysis enabled the identification of "hot spots" in time and space which is important in enhancing any existing monitoring and surveillance system.