The Norwegian Veterinary Prescription Register (VetReg) records all antimicrobials prescribed for food-producing animals in Norway, yet data-quality deficiencies have so far limited its use for quantitative reporting of antimicrobial use (AMU) in cattle. This study assessed the data quality of VetReg, developed a systematic error-correction approach, and estimated AMU in Norwegian cattle from 2018 to 2023. In total, 698,864 cattle prescription records were extracted from VetReg and assessed for completeness, validity, and timeliness. Error correction comprised harmonization of recorded units with product specifications and replacement of implausible quantities with product-specific mean therapeutic doses. AMU was quantified as mass of active substance per kg cattle biomass and as defined daily or course doses for veterinary use (DDDvet/DCDvet), with intramammary and intrauterine products expressed per 1000 cows. Data quality improved throughout the study period: missing herd information in veterinarian-reported records declined from 15.6% to 0.1%, agreement between the recorded units of prescribed medicines (e.g., ml, mg, syringe) and the expected product-specific units increased from 86.0% to 94.2%, the proportion of records reported within one week increased from 58.4% to 77.0%, and 94.4% of prescribed quantities fell within product-specific plausibility limits. Reported AMU declined by 5% over the six years, from 1328 kg (3.6 mg/kg biomass) in 2018 to 1,258 kg (3.4 mg/kg biomass) in 2023. Parenteral use, which comprised 93.3% of mean annual mass-based use, remained stable, whereas dose-based use declined for intramammary products in lactating cows (-29.6%) and dry cows (-34.6%), for intrauterine products (-87.8%) and for oral products (-57.1%). Benzylpenicillin, a narrow-spectrum penicillin, was the most used active substance, with its proportion of total use rising from 80.4% in 2018 to 87.9% in 2023. To our knowledge, this is the first complete prescription-level characterization of AMU in Norwegian cattle, including intramammaries. Although reporting to VetReg is mandatory, previous comparisons with wholesale sales have indicated that not all antimicrobials sold are recorded; these estimates should therefore be interpreted as conservative. Quality-assessment and error-correction frameworks enable granular AMU surveillance from a single prescription-based source and can support targeted antimicrobial stewardship in settings with already low use.
Wild pig (Sus scrofa) carcasses are a critical driver of African swine fever (ASF) transmission cycles, yet carcass persistence, detectability, and contact structure remain largely uncharacterized in Canada. We integrated three approaches to assess carcass-based ASF transmission risk in Alberta: persistence using Kaplan-Meier survival analysis, detection using thermal drones, and contact characterization using camera traps. Four wild pig carcasses were deployed in February 2025 and monitored over 634 camera-days until complete decomposition. Persistence was strongly driven by black bear (Ursus americanus) seasonal activity: during bear hibernation, all carcasses stalled at stage 2 (partially scavenged) for 53 days, as cold temperatures slowed decomposition and smaller scavengers could not open carcass cavities. Following the first bear-carcass detection, carcasses reached complete decomposition within a median of 24 days, a seasonal difference with direct implications for environmental ASF transmission risk. Thermal drone surveys at three altitudes (15 m, 30 m, 60 m) yielded low detection: carcasses were detected in 26.1% of survey attempts during flight and in 32.6% of analyzed images, even at known locations, with best performance at 15 m. One carcass beneath closed spruce canopy was never detected and remained unavailable to thermal survey. Carcasses were visible to the operator in 78.3% of surveys but registered as the scene's thermal maximum in only 26.1%. Detected carcasses showed roughly double the thermal contrast of undetected carcasses (11.7°C vs 5.1°C). Camera traps documented 14 species across 702 detection events, with direct carcass contact in 52%. Wild pigs were detected in four contact events across two sites, including contact with carcass remains by a sounder of one sow and ten piglets. Alberta's cold climate and highly seasonal scavenger activity creates an extended window for carcass-based ASF transmission that coincides with the most challenging period for aerial thermal surveillance, underscoring the need for season-specific outbreak response plans.
Movement standstills are a key strategy for reducing farm contacts during a foreign animal disease outbreak; however, essential activities such as feed delivery may continue under managed conditions. We simulated African swine fever (ASF) transmission across a dense swine production region of the southeastern U.S., incorporating local spread, animal movements, and truck-mediated pathways. We compared four scenarios: baseline, with animal movements and truck contacts active; trucks-off, with truck contacts removed but animal movements active; feed-only, with animal movements halted, but feed delivery continued; and standstill, with animal and truck pathways removed. ASF introductions were stratified by farm density and vehicle network node strength, resulting in nine density–vehicle network node strength categories. Outbreak size varied by initial farm context, with cumulative infected farms ranging from 17.6 to 52.5 in the low-density, low-vehicle network node strength category and from 348.2 to 376.0 in the high-density, high-vehicle network node strength category. Relative to complete standstill, the baseline and trucks-off scenario produced medians of 34.8 (IQR: 29.2–47.3) and 25.4 (IQR: 20.8–41.1) additional infected farms, respectively. In contrast, the feed-only scenario produced a median of 11.3 (IQR: 5.8–15.0) additional infected farms; a 6.4% increase over standstill compared with 24.1% under baseline and 18.6% under trucks-off. We demonstrated that feed delivery may sustain transmission during movement restrictions, but its effect depends on the farm density and vehicle network node strength of the initially infected farm.
