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Antimicrobial resistances (AMR) in bacteria, such as ESBL/AmpC-producing E. coli , are a burden to human and animal health. This burden is mainly driven by the consumption and release of antimicrobial substances into the environment. The pollution and contamination of habitats by AMR in bacteria and antimicrobial substances can lead to the transmission of bacterial AMR to wildlife. Therefore, it is necessary to understand the transmission cycle of antibiotics and resistant bacteria between humans, and animals as well as their occurrences in the environment. Environmental factors associated with the occurrence of bacterial AMR in wildlife can lead to a better understanding of the distribution of bacterial AMR in humans and animals using One Health approaches. Here, we analyzed data gathered in the framework of the German zoonoses monitoring program in 2016 and 2020 using spatiotemporal statistics to identify relevant environmental factors (e.g., livestock density, climatic variables, and human density) in association with the spatial distribution of ESBL/AmpC-producing E. coli . For this purpose, we developed a generic data integration and analysis pipeline to link spatially explicit environmental factors to the monitoring data. Finally, we built a binomial generalized linear mixed model (GLMM) to determine the factors associated with the spatial distribution of ESBL/AmpC-producing E. coli . In 2016 and 2020, 807 fecal samples from hunted wild boar ( Sus scrofa L .) were randomly taken in 13 federal states and selectively analyzed for ESBL/AmpC-producing E. coli . Forty-eight isolates were identified in 12 German federal states, with an overall prevalence of 6%. We observed an almost three times higher probability of ESBL/AmpC-producing E. coli isolates in wild boar in counties with high cattle densities (OR = 2.57, p ≤ 0.01). Furthermore, we identified a seasonal effect in areas with high precipitation during the off-hunting seasons (OR = 2.78, p = 0.025) and low precipitation throughout the years (OR = 0.42, p = 0.025). However, due to the low amount of identified isolates, confidence intervals were wide, indicating a high level of uncertainty. This suggests that further studies on smaller scales need to be conducted with multiannual data and improved metadata, e.g., on the location, the hunting procedure, and species characteristics to be collected during field sampling., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Günther, Kramer-Schadt, Fuhrmann and Belik.)

Estimating eco-epidemiological parameters in free-ranging populations can be challenging. As known individuals may be undetected during a field session, or their health status uncertain, the collected data are typically "imperfect". Multi-event capture-mark-recapture (MECMR) models constitute a substantial methodological advance by accounting for such imperfect data. In these models, animals can be "undetected" or "detected" at each time step. Detected animals can be assigned an infection state, such as "susceptible" (S), "infected" (I), or "recovered" (R), or an "unknown" (U) state, when for instance no biological sample could be collected. There may be heterogeneity in the assignment of infection states, depending on the manifestation of the disease in the host or the diagnostic method. For example, if obtaining the samples needed to prove viral infection in a detected animal is difficult, this can result in a low chance of assigning the I state. Currently, it is unknown how much uncertainty MECMR models can tolerate to provide reliable estimates of eco-epidemiological parameters and whether these parameters are sensitive to heterogeneity in the assignment of infection states. We used simulations to assess how estimates of the survival probability of individuals in different infection states and the probabilities of infection and recovery responded to (1) increasing infection state uncertainty (i.e., the proportion of U) from 20 to 90%, and (2) heterogeneity in the probability of assigning infection states. We simulated data, mimicking a highly virulent disease, and used SIR-MECMR models to quantify bias and precision. For most parameter estimates, bias increased and precision decreased gradually with state uncertainty. The probabilities of survival of I and R individuals and of detection of R individuals were very robust to increasing state uncertainty. In contrast, the probabilities of survival and detection of S individuals, and the infection and recovery probabilities showed high biases and low precisions when state uncertainty was >50%, particularly when the assignment of the S state was reduced. Considering this specific disease scenario, SIR-MECMR models are globally robust to state uncertainty and heterogeneity in state assignment, but the previously mentioned parameter estimates should be carefully interpreted if the proportion of U is high.

Epidemiological models of infectious diseases are essential tools in support of risk assessment, surveillance design, and contingency planning in public and animal health. Direct pathogen transmission from host to host is an essential process of each host-pathogen system and respective epidemiological modeling concepts. It is widely accepted that numerous diseases involve indirect transmission (IT) through pathogens shed by infectious hosts to their environment. However, epidemiological models largely do not represent pathogen persistence outside the host explicitly. We hypothesize that this simplification might bias management-related model predictions for disease agents that can persist outside their host for a certain time span. We adapted an individual-based, spatially explicit epidemiological model that can mimic both transmission processes. One version explicitly simulated indirect pathogen transmission through a contaminated environment. The second version simulated direct host-to-host transmission only. We aligned the model variants by the transmission potential per infectious host (i.e., basic reproductive number R 0 ) and the spatial transmission kernel of the infection to allow unbiased comparison of predictions. The quantitative model results are provided for the example of surveillance plans for early detection of foot-and-mouth disease in wild boar, a social host. We applied systematic sampling strategies on the serological status of randomly selected host individuals in both models. We compared between the model variants the time to detection and the area affected prior to detection, measures that strongly influence mitigation costs. Moreover, the ideal sampling strategy to detect the infection in a given time frame was compared between both models. We found the simplified, direct transmission model to underestimate necessary sample size by up to one order of magnitude but to overestimate the area put under control measures. Thus, the model predictions underestimated surveillance efforts but overestimated mitigation costs. We discuss parameterization of IT models and related knowledge gaps. We conclude that the explicit incorporation of IT mechanisms in epidemiological modeling may reward by adapting surveillance and mitigation efforts.