IntroductionElectronic information systems (EIS) that implement a ‘One Health’ approach by integrating antimicrobial resistance (AMR) data across the human, animal and environmental health sectors, have been identified as a global priority. However, evidence on the availability, technical capacities and effectiveness of such EIS is scarce.MethodsThrough a qualitative synthesis of evidence, this systematic scoping review aims to: identify EIS for AMR surveillance that operate across human, animal and environmental health sectors; describe their technical characteristics and capabilities; and assess whether there is evidence for the effectiveness of the various EIS for AMR surveillance. Studies and reports between 1 January 2000 and 21 July 2021 from peer-reviewed and grey literature in the English language were included.Results26 studies and reports were included in the final review, of which 27 EIS were described. None of the EIS integrated AMR data in a One Health approach across all three sectors. While there was a lack of evidence of thorough evaluations of the effectiveness of the identified EIS, several surveillance system effectiveness indicators were reported for most EIS. Standardised reporting of the effectiveness of EIS is recommended for future publications. The capabilities of the EIS varied in their technical design features, in terms of usability, data display tools and desired outputs. EIS that included interactive features, and geospatial maps are increasingly relevant for future trends in AMR data analytics.ConclusionNo EIS for AMR surveillance was identified that was designed to integrate a broad range of AMR data from humans, animals and the environment, representing a major gap in global efforts to implement One Health approaches to address AMR.

African swine fever (ASF) in Asia and the Pacific is currently dominated by ASF virus transmission within and between domestic pig populations. The contribution made by wild suids is currently not well understood; their distribution, density and susceptibility to the virus has raised concerns that their role in the epidemiology of ASF in the region might be underestimated. Whilst in the Republic of Korea wild suids are considered important in the spread and maintenance of ASF virus, there is an apparent underreporting to official sources of the disease in wild suids from other countires and regions. A review of the current literature, an analysis of the official reporting resources and a survey of the World Organisation of Animal Health Member delegates in Asia and the Pacific were used to assess the potential role of wild suids in ASF outbreaks, and also to gain insight into what ASF management or control strategies are currently implemented for wild suids. Applying appropriate population control and management strategies can be increased in some areas, especially to assist in the conservation of endangered endemic wild suids in this region.

We describe results from a panel study in which pigs from a 17-sow African swine fever (ASF) positive herd in Thái Bình province, Vietnam were followed over time to record the date of onset of ASF signs and the date of death from ASF. Our objectives were to: (1) fit a susceptible-exposed-infectious-removed disease model to the data with transmission coefficients estimated using Approximate Bayesian Computation; and (2) provide commentary on how a model of this type might be used to provide decision support for disease control authorities For the outbreak in this herd the median of the average latent period was 10 days (95% HPD [highest posterior density interval]: 2 to 19 days) and the median of the average duration of infectiousness was 3 days (95% HPD: 2 to 4 days). The estimated median for the transmission coefficient was 3.3 (95% HPD: 0.4 to 8.9) infectious contacts per ASF-infectious pig per day. The estimated median for the basic reproductive number, R 0 , was 10 (95% HPD: 1.1 to 30). Our estimates of the basic reproductive number R 0 were greater than estimates of R 0 for ASF reported previously. The results presented in this study may be used to estimate the number of pigs expected to be showing clinical signs at a given number of days following an estimated incursion date. This will allow sample size calculations, with or without adjustment to account for less than perfect sensitivity of clinical examination, to be used to determine the appropriate number of pigs to examine to detect at least one with disease. A second use of the results of this study would be to inform the equation-based within-herd spread components of stochastic agent based and hybrid simulation models of ASF.