Mapping the risk of introduction of highly pathogenic avian influenza to Swedish poultry.

Outbreaks of highly pathogenic avian influenza (HPAI) have resulted in severe economic impact for national governments and poultry industries globally and in Sweden in recent years. Veterinary authorities can enforce prevention measures, e.g. mandatory indoor housing of poultry, in HPAI high-risk areas. The aim of this study was to conduct a spatiotemporal mapping of the risk of introduction of highly pathogenic avian influenza virus (HPAIV) to Swedish poultry from wild birds, utilising existing data sources. A raster calculation method was used to assess the spatiotemporal risk of introduction of HPAIV to Swedish poultry. The environmental infectious pressure of HPAIV was first calculated in each 5 km by 5 km cell using four risk factors: density of selected species of wild birds, air temperature, presence of agriculture as land cover and presence of HPAI in wild birds based on data from October 2016-September 2021. The relative importance of each risk factor was weighted based on opinion of experts. The estimated environmental infectious pressure was then multiplied with poultry population density to obtain risk values for risk of introduction of HPAIV to poultry. The results showed a large variation in risk both on national and local level. The counties of Skåne and Östergötland particularly stood out regarding environmental infectious pressure, risk of introduction to poultry and detected outbreaks of HPAI. On the other hand, there were counties, identified as having higher risk of introduction to poultry which never experienced any outbreaks. A possible explanation is the variation in poultry production types present in different areas of Sweden. These results indicate that the national and local variation in risk for HPAIV introduction to poultry in Sweden is high, and this would support more targeted compulsory prevention measures than what has previously been employed in Sweden. With the current and evolving HPAI situation in Europe and on the global level, there is a need for continuous updates to the risk map as the virus evolves and circulates in different wild bird species. The study also identified areas of improvement, in relation to data use and data availability, e.g. improvements to poultry registers, inclusion of citizen reported mortality in wild birds, data from standardised wild bird surveys, wild bird migration data as well as results from ongoing risk-factor studies.

Review state-of-the-art of output-based methodological approaches for substantiating freedom from infection.

A wide variety of control and surveillance programmes that are designed and implemented based on country-specific conditions exists for infectious cattle diseases that are not regulated. This heterogeneity renders difficult the comparison of probabilities of freedom from infection estimated from collected surveillance data. The objectives of this review were to outline the methodological and epidemiological considerations for the estimation of probabilities of freedom from infection from surveillance information and review state-of-the-art methods estimating the probabilities of freedom from infection from heterogeneous surveillance data. Substantiating freedom from infection consists in quantifying the evidence of absence from the absence of evidence. The quantification usually consists in estimating the probability of observing no positive test result, in a given sample, assuming that the infection is present at a chosen (low) prevalence, called the design prevalence. The usual surveillance outputs are the sensitivity of surveillance and the probability of freedom from infection. A variety of factors influencing the choice of a method are presented; disease prevalence context, performance of the tests used, risk factors of infection, structure of the surveillance programme and frequency of testing. The existing methods for estimating the probability of freedom from infection are scenario trees, Bayesian belief networks, simulation methods, Bayesian prevalence estimation methods and the STOC free model. Scenario trees analysis is the current reference method for proving freedom from infection and is widely used in countries that claim freedom. Bayesian belief networks and simulation methods are considered extensions of scenario trees. They can be applied to more complex surveillance schemes and represent complex infection dynamics. Bayesian prevalence estimation methods and the STOC free model allow freedom from infection estimation at the herd-level from longitudinal surveillance data, considering risk factor information and the structure of the population. Comparison of surveillance outputs from heterogeneous surveillance programmes for estimating the probability of freedom from infection is a difficult task. This paper is a 'guide towards substantiating freedom from infection' that describes both all assumptions-limitations and available methods that can be applied in different settings.