Dynamic resource allocation for controlling pathogen spread on a large metapopulation network.

To control the spread of an infectious disease over a large network, the optimal allocation by a social planner of a limited resource is a fundamental and difficult problem. We address this problem for a livestock disease that propagates on an animal trade network according to an epidemiological-demographic model based on animal demographics and trade data. We assume that the resource is dynamically allocated following a certain score, up to the limit of resource availability. We adapt a greedy approach to the metapopulation framework, obtaining new scores that minimize approximations of two different objective functions, for two control measures: vaccination and treatment. Through intensive simulations, we compare the greedy scores with several heuristics. Although topology-based scores can limit the spread of the disease, information on herd health status seems crucial to eradicating the disease. In particular, greedy scores are among the most effective in reducing disease prevalence, even though they do not always perform the best. However, some scores may be preferred in real life because they are easier to calculate or because they use a smaller amount of resources. The developed approach could be adapted to other epidemiological models or to other control measures in the metapopulation setting.

Demographic stochasticity drives epidemiological patterns in wildlife with implications for diseases and population management.

Infectious diseases raise many concerns for wildlife and new insights must be gained to manage infected populations. Wild ungulates provide opportunities to gain such insights as they host many pathogens. Using modelling and data collected from an intensively monitored population of Pyrenean chamois, we investigated the role of stochastic processes in governing epidemiological patterns of pestivirus spread in both protected and hunted populations. We showed that demographic stochasticity led to three epidemiological outcomes: early infection fade-out, epidemic outbreaks with population collapse, either followed by virus extinction or by endemic situations. Without re-introduction, the virus faded out in >50% of replications within 4 years and did not persist >20 years. Test-and-cull of infected animals and vaccination had limited effects relative to the efforts devoted, especially in hunted populations in which only quota reduction somewhat improve population recovery. Success of these strategies also relied on the maintenance of a high level of surveillance of hunter-harvested animals. Our findings suggested that, while surveillance and maintenance of population levels at intermediate densities to avoid large epidemics are useful at any time, a 'do nothing' approach during epidemics could be the 'least bad' management strategy in populations of ungulates species facing pestivirus infection.

A modelling framework based on MDP to coordinate farmers' disease control decisions at a regional scale.

The effectiveness of infectious disease control depends on the ability of health managers to act in a coordinated way. However, with regards to non-notifiable animal diseases, farmers individually decide whether or not to implement control measures, leading to positive and negative externalities for connected farms and possibly impairing disease control at a regional scale. Our objective was to facilitate the identification of optimal incentive schemes at a collective level, adaptive to the epidemiological situation, and minimizing the economic costs due to a disease and its control. We proposed a modelling framework based on Markov Decision Processes (MDP) to identify effective strategies to control PorcineReproductive andRespiratorySyndrome (PRRS), a worldwide endemicinfectiousdisease thatsignificantly impactspig farmproductivity. Using a stochastic discrete-time compartmental model representing PRRS virus spread and control within a group of pig herds, we defined the associated MDP. Using a decision-tree framework, we translated the optimal policy into a limited number of rules providing actions to be performed per 6-month time-step according to the observed system state. We evaluated the effect of varying costs and transition probabilities on optimal policy and epidemiological results. We finally identifiedan adaptive policy that gave the best net financial benefit. The proposed framework is a tool for decision support as it allows decision-makers to identify the optimal policy and to assess its robustness to variations in the values of parameters representing an impact of incentives on farmers' decisions.

Controlling bovine paratuberculosis at a regional scale: Towards a decision modelling tool.

Johne's disease (paratuberculosis), a worldwide enzootic disease of cattle caused by Mycobacterium avium subsp. paratuberculosis (Map), mainly introduced into farms by purchasing infected animals, has a large economic impact for dairy producers. Since diagnostic tests used in routine are poorly sensitive, observing Map spread in the field is hardly possible, whereas there is a need for evaluating control strategies. Our objective was to provide a modelling framework to compare the efficacy of regional control strategies combining internal biosecurity measures and testing of traded animals, against Map spread in a metapopulation of dairy cattle herds. We represented 12,857 dairy herds located in Brittany (France), based on data from 2005 to 2013, used to calibrate herd sizes and demographic rates and to define trade events in a multiscale model of Map infection dynamics. By clustering and categorical descriptive analysis of intensive simulations of this model, based on a numerical experimental design, a large panel of control measures was explored. Their efficacy was assessed on model outputs such as the prevalence and probability of extinction at the metapopulation level. In addition, we proposed a scoring for the effort required to implement control measures and prioritized control strategies based on their theoretical epidemiological efficacy. Our results clearly indicate that eradication cannot be achieved on the mid term using available control measures. However, we identified relevant combinations of measures that lead to the control of Map spread with realistic level of implementation and coverage. The study highlights the challenge of controlling paratuberculosis in an endemically infected region as related to the poor test characteristics and frequent trade movements. Our model lays the foundations for a flexible and efficient tool to help collective animal health managers in defining relevant control strategies at a regional scale, accounting for local specificities in terms of contact network and farms' characteristics.

Market analyses of livestock trade networks to inform the prevention of joint economic and epidemiological risks.

Conventional epidemiological studies of infections spreading through trade networks, e.g., via livestock movements, generally show that central large-size holdings (hubs) should be preferentially surveyed and controlled in order to reduce epidemic spread. However, epidemiological strategies alone may not be economically optimal when costs of control are factored in together with risks of market disruption from targeting core holdings in a supply chain. Using extensive data on animal movements in supply chains for cattle and swine in France, we introduce a method to identify effective strategies for preventing outbreaks with limited budgets while minimizing the risk of market disruptions. Our method involves the categorization of holdings based on position along the supply chain and degree of market share. Our analyses suggest that trade has a higher risk of propagating epidemics through cattle networks, which are dominated by exchanges involving wholesalers, than for swine. We assess the effectiveness of contrasting interventions from the perspectives of regulators and the market, using percolation analysis. We show that preferentially targeting minor, non-central agents can outperform targeting of hubs when the costs to stakeholders and the risks of market disturbance are considered. Our study highlights the importance of assessing joint economic-epidemiological risks in networks underlying pathogen propagation and trade.