Assessing classical swine fever disease control measures using an individual-based model

Lange, M., Kramer-Schadt, S., Thulke, H.-H

Three questions to ask before using model outputs for decision support

Decision makers must have sufficient confidence in models if they are to influence their decisions. We propose three screening questions to critically evaluate models with respect to their purpose, organization, and evidence. They enable a more transparent, robust, and secure use of model outputs

Risk factors for African swine fever incursion in Romanian domestic farms during 2019

African swine fever (ASF) entered Georgia in 2007 and the EU in 2014. In the EU, the virus primarily spread in wild boar (Sus scrofa) in the period from 2014–2018. However, from the summer 2018, numerous domestic pig farms in Romania were affected by ASF. In contrast to the existing knowledge on ASF transmission routes, the understanding of risk factors and the importance of different transmission routes is still limited. In the period from May to September 2019, 655 Romanian pig farms were included in a matched case-control study investigating possible risk factors for ASF incursion in commercial and backyard pig farms. The results showed that close proximity to outbreaks in domestic farms was a risk factor in commercial as well as backyard farms. Furthermore, in backyard farms, herd size, wild boar abundance around the farm, number of domestic outbreaks within 2 km around farms, short distance to wild boar cases and visits of professionals working on farms were statistically significant risk factors. Additionally, growing crops around the farm, which could potentially attract wild boar, and feeding forage from ASF affected areas to the pigs were risk factors for ASF incursion in backyard farms.We acknowledge financial support from EFSA, ANSVSA and from the Danish Veterinary and Food Administration (FVST) as part of the agreement of commissioned work between the Danish Ministry of Food, Agriculture and Fisheries and the University of Copenhagen.Peer reviewe

Rabies Management Implications Based on Raccoon Population Density Indexes

An estimate or index of target species density is important in determining oral rabies vaccination (ORV) bait densities to control and eliminate specific rabies variants. From 1997–2011, we indexed raccoon (Procyon lotor) densities 253 times based on cumulative captures on 163 sites from Maine to Alabama, USA, near ORV zones created to prevent raccoon rabies from spreading to new areas. We conducted indexing under a common cage trapping protocol near the time of annual ORV to aid in bait density decisions. Unique raccoons (n = 8,415) accounted for 68.0% of captures (n = 12,367). We recaptured raccoons 2,669 times. We applied Schnabel and Huggins mark‐recapture models on sites with ≥3 years of capture data and ≥25% recaptures as context for raccoon density indexes (RDIs). Simple linear relationships between RDIs and mark‐recapture estimates supported application of our 2 index. Raccoon density indexes ranged from 0.0–56.9 raccoons/km . For bait density decisions, we evaluated RDIs in the following 4 raccoon density groups, which were statistically different: (0.0–5.0 [n = 70], 5.1–15.0 [n = 129], 15.1–25.0 [n = 31], and \u3e25.0 raccoons/km2 [n = 23]). Mean RDI was positively associated with a higher percentage of developed land cover and a lower percentage of evergreen forest. Non‐target species composition (excluding recaptured raccoons) accounted for 32.0% of captures. Potential bait competitors accounted for 76.5% of non‐targets. The opossum (Didelphis virginiana) was the primary potential bait competitor from 27°N to 44°N latitude, north of which it was numerically replaced by the striped skunk (Mephitis mephitis). We selected the RDI approach over mark-recapture methods because of costs, geographic scope, staff availability, and the need for supplemental serologic samples. The 4 density groups provided adequate sensitivity to support bait density decisions for the current 2 bait density options. Future improvements to the method include providing random trapping locations to field personnel to prevent trap clustering and marking non‐targets to better characterize bait competitors