Inference using a composite-likelihood approximation for stochastic metapopulation model of disease spread

Spatio-temporal pathogen spread is often partially observed at the metapopulation scale. Available data correspond to proxies and are incomplete, censored and heterogeneous. Moreover, representing such biological systems often leads to complex stochastic models. Such complexity together with data characteristics make the analysis of these systems a challenge. Our objective was to develop a new inference procedure to estimate key parameters of stochastic metapopulation models of animal disease spread from longitudinal and spatial datasets, while accurately accounting for characteristics of census data. We applied our procedure to provide new knowledge on the regional spread of \emph{Mycobacterium avium} subsp. \emph{paratuberculosis} (\emph{Map}), which causes bovine paratuberculosis, a worldwide endemic disease. \emph{Map} spread between herds through trade movements was modeled with a stochastic mechanistic model. Comprehensive data from 2005 to 2013 on cattle movements in 12,857 dairy herds in Brittany (western France) and partial data on animal infection status in 2,278 herds sampled from 2007 to 2013 were used. Inference was performed using a new criterion based on a Monte-Carlo approximation of a composite likelihood, coupled to a numerical optimization algorithm (Nelder-Mead Simplex-like). Our criterion showed a clear superiority to alternative ones in identifying the right parameter values, as assessed by an empirical identifiability on simulated data. Point estimates and profile likelihoods allowed us to establish the initial state of the system, identify the risk of pathogen introduction from outside the metapopulation, and confirm the assumption of the low sensitivity of the diagnostic test. Our inference procedure could easily be applied to other spatio-temporal infection dynamics, especially when ABC-like methods face challenges in defining relevant summary statistics

Multi-species temporal network of livestock movements for disease spread

Introduction: The objective of this study is to show the importance of interspecies links and temporal network dynamics of a multi-species livestock movement network. Although both cattle and sheep networks have been previously studied, cattle-sheep multi-species networks have not generally been studied in-depth. The central question of this study is how the combination of cattle and sheep movements affects the potential for disease spread on the combined network. Materials and methods: Our analysis considers static and temporal representations of networks based on recorded animal movements. We computed network-based node importance measures of two single-species networks, and compared the top-ranked premises with the ones in the multi-species network. We propose the use of a measure based on contact chains calculated in a network weighted with transmission probabilities to assess the importance of premises in an outbreak. To ground our investigation in infectious disease epidemiology, we compared this suggested measure with the results of disease simulation models with asymmetric probabilities of transmission between species. Results: Our analysis of the temporal networks shows that the premises which are likely to drive the epidemic in this multi-species network differ from the ones in both the cattle and the sheep networks. Although sheep movements are highly seasonal, the estimated size of an epidemic is significantly larger in the multi-species network than in the cattle network, independently of the period of the year. Finally, we demonstrate that a measure based on contact chains allow us to identify around 30% of the key farms in a simulated epidemic, ignoring markets, whilst static network measures identify less than 10% of these farms. Conclusion: Our results ascertain the importance of combining species networks, as well as considering layers of temporal livestock movements in detail for the study of disease spread

Border disease virus: an exceptional driver of chamois populations among other threats

Though it is accepted that emerging infectious diseases are a threat to planet biodiversity, little information exists about their role as drivers of species extinction. Populations are also affected by natural catastrophes and other pathogens, making it difficult to estimate the particular impact of emerging infectious diseases. Border disease virus genogroup 4 (BDV-4) caused a previously unreported decrease in populations of Pyrenean chamois (Rupicapra pyrenaica pyrenaica) in Spain. Using a population viability analysis, we compared probabilities of extinction of a virtual chamois population affected by winter conditions, density dependence, keratoconjunctivitis, sarcoptic mange, and BD outbreaks. BD-affected populations showed double risk of becoming extinct in 50 years, confirming the exceptional ability of this virus to drive chamois populations

Spread and control of Johne’s disease in an enzootic cattle region: a multi-scale model to evaluate complex strategies combining biosecurity and trade regulations

International audiencePurpose Johne’s disease is a worldwide enzootic disease of cattle inducing a large economic impact for dairy producers due to production losses and early culling of cows. This chronic disease is characterized by a long incubation period, and diagnostic tests used in routine are poorly sensitive. Hence, observing the disease spread in the field is hardly possible, whereas there is a need for evaluating control strategies. Our objective is to better understand the spread of Mycobacterium avium subsp. paratuberculosis (Map) at a regional scale using a modelling approach, and to compare through simulations control strategies combining biosecurity measures (early culling, hygiene improvement, calf management) and tests at purchase. Methods We developed the first multi-scale mechanistic model of Map spread between dairy cattle herds, accounting for stochastic within-herd dynamics (demography and infection), indirect local transmission, and incorporating data on animal trade and on herd-specific size and management. We modeled all of the 12,857 dairy herds located in Brittany (France) having more than 15 dairy females. Data from 2005 to 2013 was used to calibrate each herd size and demographic rates, and to define trade events. We assumed initially 30% of the herds to be infected. Results Each measure tested alone or in combination with tests at purchase succeeded in slowing down the regional Map spread, but not in decreasing the proportion of infected herds. More than two measures had to be combined to effectively reduce the herd-level prevalence. However, in that latter case, only a moderate level of implementation of each measure was required, indicating the operational potential of such combined strategies. Conclusions Our study highlights the challenge of controlling Map spread in an endemically infected region because of poor test characteristics and frequent trade movements. Relevance Our model is a flexible and efficient tool to help collective animal health managers in defining relevant control strategies at a regional scale, accounting for regional specificities in terms of contact network and farms’ characteristic

Controlling the spread of Mycobacterium avium subsp. paratuberculosis at a regional scale based on internal biosecurity and animal movements

International audienceTrade movements represent a major route for the spread of pathogens such as Mycobacterium avium subsp. paratuberculosis (Map) that causes paratuberculosis, in metapopulations of cattle herds. Our objective was to assess scenarios combining measures of Map spread control based either on test at purchase or internal biosecurity (including hygiene improvement, culling, or calf management). A metapopulation model was developed accounting for population and infection dynamics in each dairy herd. Within herd infection dynamics were connected through observed animal trade movements. When considered separately, simulated interventions slowed down the disease spread, but did not induce a decrease in the number of infected herds. Interestingly, in simulations combining several measures, both effects were achieved. In addition to introducing a model of Map spread at a regional scale, our study highlights the key challenges of controlling Map spread in a region endemically infecte

Spread and control of Mycobacterium avium subsp. paratuberculosis (Map) in a metapopulation of cattle herds

International audienceThe spread of Map between herds is mainly due to animal movements, which form a complex network linking farms geographically close or distant. Moreover, the diversity of farming systems in a region (dairy vs beef, variable herd size and structure) and of contacts among farms (frequency, types of animals purchased) can also influence Map spread and control at the scale of the metapopulation of herds. Here we study Map spread between cattle herds to evaluate, at a regional scale, control strategies based on the management of animal movements between herds depending on their epidemiological status. Regional infection dynamics are represented by coupling intra and inter herds dynamics. For each herd, a stochastic compartmental model in discrete time describes realistic population dynamics and Map spread accounting for all the transmission routes and detailed infection progression. Intra-­‐herds dynamics are coupled within a metapopulation model through animal movements. We use animal trade data from the French cattle identification database (2005–2009). A subset of the dairy farms network in the Finistère department in Northwestern France is considered based on the type and size of the herds. Various tests at purchase are implemented, defined by the test sensitivity (Se) and specificity, the animal age and status at purchase and the delay between the test and the removal of detected animals (no delay if testing occurred before animal introduction until a delay of a few weeks). The initial number of infected herds, their prevalence, and the network structure highly influence the speed of Map spread and the endemic state reached 20 to 50 years after the first disease occurrence. Testing purchased animals with Se greater than 0.75 achieves to stop Map spread but not to reduce the regional prevalence. Therefore, a combination of strategies should now be evaluated to also target a decrease in the within-­‐herd contamination level. In parallel, an extension to beef herds is under way

Inferring ASF transmission in domestic pigs and wild boars using a paired model iterative approach

The rapid spread of African swine fever (ASF) in recent years has once again raised awareness of the need to improve our preparedness in preventing and managing outbreaks, for which modelling-based forecasts can play an important role. This is even more important in the case of a disease such as ASF, involving several types of hosts, characterised by a high case-fatality rate and for which there is currently no treatment or vaccine. Within the framework of the ASF challenge, we proposed a modelling approach based on a stochastic mechanistic model and an inference procedure to estimate key transmission parameters from provided data (incomplete and noisy) and generate forecasts for unobserved time horizons. The model is partly data driven and composed of two modules, corresponding to epidemic and demographic dynamics in domestic pig and wild boar (WB) populations, interconnected through the networks of animal trade and/or spatial proximity. The inference consists in an iterative procedure, alternating between the two models and based on a criterion optimisation. Estimates of transmission and detection parameters appeared to be of similar magnitude for each of the three periods of the challenge, except for the transmission rates in WB population through contact with infectious individuals and carcasses, higher during the first period. The predicted number of infected domestic pig farms was in overall agreement with the data. The proportion of positive tested WB was overestimated, but with a trend close to that observed in the data. Comparison of the spatial simulated and observed distributions of detected cases also showed an overestimation of the spread of the pathogen within WB metapopulation. Beyond the quantitative results and the inherent difficulties of real-time forecasting, we built a modelling framework that is flexible enough to accommodate changes in transmission processes and control measures that may occur during an epidemic emergency