One Health 17 (2023) 100598 Contents lists available at ScienceDirect One Health journal homepage: www.elsevier.com/locate/onehlt Assessing the epidemiological risk at the human-wild boar interface through a one health approach using an agent-based model in Barcelona, Spain González-Crespo Carlos a, b, Martínez-López Beatriz b, Conejero Carles a, Castillo- Contreras Raquel a, Serrano Emmanuel a, López-Martín Josep Maria c, Serra-Cobo Jordi d, Lavín Santiago a, López-Olvera Jorge Ramón a, * a Wildlife Ecology & Health Group and Servei d’Ecopatologia de Fauna Salvatge (SEFaS), Departament de Medicina i Cirurgia Animals, Universitat Autònoma de Barcelona (UAB), Bellaterra, Barcelona, Spain b Center for Animal Disease Modeling and Surveillance, Department of Medicine and Epidemiology, University of California, Davis, Davis, CA, USA. c Department of Climatic Action, Food and Rural Agenda, Generalitat de Catalunya, Barcelona, Spain d Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Universitat de Barcelona, Institut de Recerca de la Biodiversitat, Barcelona, Spain A R T I C L E I N F O A B S T R A C T Keywords: Wild boar (WB, Sus scrofa) populations are increasing in urban areas, posing an epidemiological risk for zoonotic African swine fever virus pathogens such as hepatitis E virus (HEV) and antimicrobial-resistant Campylobacter (AMR-CAMP), as well as Agent-based model non-zoonotic pathogens such as African swine fever virus (ASFV). An epidemiological extension of a validated Campylobacter Agent-Based Model (ABM) was developed to assess the one-year epidemiological scenarios of HEV, AMR-CAMP, Hepatitis E virus Sus scrofa and ASFV in the synurbic WB-human interface in Barcelona, Spain. The predicted citizen exposure was similar Synurbization for HEV and AMR-CAMP, at 0.79% and 0.80% of the human population in Barcelona, respectively, despite AMR- CAMP being more prevalent in the WB population than HEV. This suggests a major role of faeces in pathogen transmission to humans in urban areas, resulting in a non-negligible public health risk. The ASFV model pre­ dicted that the entire WB population would be exposed to the virus through carcasses (87.6%) or direct contact (12.6%) in 51–71 days after the first case, with an outbreak lasting 71–124 days and reducing the initial WB population by 95%. The ABM predictions are useful for animal and public health risk assessments and to support risk-based decision-making. The study underscores the need for interdisciplinary cooperation among animal, public, and environmental health managers, and the implementation of the One Health approach to address the epidemiological and public health risks posed by the synurbization of WB in urban areas. The spatially explicit epidemiological predictions of the ABM can be adapted to other diseases and scenarios at the wildlife-livestock- human interface. 1. Introduction endemic, posing major public health concerns and severely impacting human health and economy. Integrated wildlife monitoring, which Zoonotic diseases account for 60.3% of human diseases and 71.8% of combines wildlife health and host community monitoring, is essential emerging infectious diseases originate from wildlife [1]. Outbreaks and for early detection of emerging infections, tracking changes in disease epidemics of new human infectious diseases have demonstrated the risk dynamics, and evaluating the effectiveness of interventions in complex posed by biological agents to public health and animal breeding [2], and multi-host and multi-pathogen networks [3]. recent highly pathogenic avian influenza and SARS-CoV-2 outbreaks Eurasian wild boar (WB, Sus scrofa) has a major epidemiological role have shown how rapidly emerging diseases can spread and become as host and reservoir for zoonotic and non-zoonotic pathogens shared Abbreviations: ABM, Agent-based model; AMR-CAMP, Antimicrobial-resistant Campylobacter; ASFV, African swine fever virus; BCN, Barcelona; CNP, Collserola Natural Park; HEV, Hepatitis E virus; MAB, Metropolitan area of Barcelona; Swb, Synurbic or habituated wild boar; WB, Wild boar; WIA, Wild boar interaction area; Wwb, Wild or non-habituated wild boar. * Corresponding author. E-mail address: Jordi.Lopez.Olvera@uab.cat (L.-O. Jorge Ramón). https://doi.org/10.1016/j.onehlt.2023.100598 Received 22 March 2023; Received in revised form 17 June 2023; Accepted 30 June 2023 Available online 9 July 2023 2352-7714/© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by- nc-nd/4.0/). G.-C. Carlos et al. One Health 17 (2023) 100598 Fig. 1. A) Study area. Collserola Natural Park and the five districts of Barcelona included in the model (Les Corts, Sarrià-Sant Gervasi, Gràcia, Horta-Guinardó, and Nou Barris). Human population density and wild boar interaction areas (WIA, including feeding and resting areas) are shown; B) Initial location of the susceptible and infected synurbic-wild boar (Swb) and non-urban wild boar (Wwb) agents in the (a) AMR-CAMP, antimicrobial resistant Campylobacter scenario, (b) HEV, hepatitis E virus scenario, and initial location of the infected WIA-F agent in the AFSV, African swine fever virus scenario. The number of citizen agents by sub-type (RC, regular citizen; PO, pet owner; FD, feeder citizen) modelled in each district is also provided. with livestock, companion animals and humans, participating in the (CNP), located within the MAB, are attracted by anthropogenic food maintenance of multi-host pathogens [4–8]. The increase in WB popu­ resources to the (peri)urban area [10–12,17,23,25], where they contact lation abundance and distribution supposes a sanitary risk for game with the urban environment and human citizens. Urban development meat, swine production and public health [4,5,9]. Furthermore, WB is and transport networks limit genetic exchange between the WB in CNP also colonizing urban areas [10–13], where they exploit anthropogenic using the (peri)urban area of Barcelona and their rural counterparts food resources [10–12], in a process known as synurbization [14]. Host [26], although WB move in and out of the CNP using riparian areas and aggregation and tolerance promoted by anthropogenic resources may dry riverbeds as corridors [10,26]. Previous studies on the WB popula­ amplify pathogen spread in urban areas [15–21], enhancing the epide­ tion in the MAB have detected zoonotic hepatitis E virus (HEV) [8], miological role of WB. Streptococcus suis [6], tick-borne pathogens such as Rickettsia spp. [27], According to the One Health framework, the dynamics of a zoonotic and antimicrobial resistant bacteria such as Campylobacter spp. (AMR- agent in a multi-host pathogen system involve transmission among host CAMP), Salmonella sp., Escherichia coli and Clostridioides difficile species (including humans), where contact, recovery, and mortality [17–19]. rates determine reservoir and disease emergence dynamics [7,15–21]. Early warning systems to predict and detect emerging and re- The risk of spillover to humans depends on infection prevalence in host emerging diseases are crucial for both economy and public health [2]. population, contact rate between humans and other infected animal Accurate health risk assessments allowing effective mitigation strategies hosts, and infection probability upon contact [7,22]. Wild boar syn­ rely on better understanding of wildlife-pathogen dynamics at the urbization has modified the social-ecological system involved in human- human-wildlife-livestock interface. Since such understanding cannot be wild boar direct and indirect interactions in densely populated areas achieved through in vitro and field studies alone, theoretical epidemi­ [14,23], increasing contact rates between synurbic WB and citizens ology and simulated models allow testing hypotheses concerning envi­ [10,13,15,16] and consequently the potential of WB as a source for ronment, social structure, behaviour, and other factors [28]. Spatial pathogens and emerging human diseases [1,4,5,7]. models consider adaptive surveillance strategies for disease emergence The Metropolitan Area of Barcelona (MAB) harbours 3.2 million [29], including the effects of anthropogenic resources on local dynamics people in 636 km2 (population density of 5000 people per km2) [24]. and movement connectivity in order to understand the persistence and The synurbic WB from the 80 km2 Natura 2000 Collserola Natural Park spatial spread of pathogens [15,16]. Although different epidemiological 2 G.-C. Carlos et al. One Health 17 (2023) 100598 Fig. 1. (continued). models have been proposed, spatially explicit Agent-Based Modeling ticks [33], and has devastating economic impact for the pork industry (ABM) with a GIS approach can fully represent heterogeneous agents [9]. ASFV has recently emerged and spread in Eastern and Central and their environment [28]. According to the previous reports of their Europe associated to movement of WB and transport of anthropogenic presence in the study area [8,17,18], and/or to the relevance and im­ food resources [34], which potentially could originate an ASFV outbreak plications to public health and national and regional economy, (1) in the growing human-wild boar interface at the MAB. hepatitis E virus (HEV), (2) antimicrobial-resistant Campylobacter (AMR- This study develops an epidemiological sub-model (BCNWB-EPI) for CAMP), and (3) African swine fever virus (ASFV) have been selected for the previously developed the BCNWB prototype [35], an ABM of the WB this study. synurbization process, the use of the urban ecosystem of the Barcelona HEV is a single-stranded RNA virus of the family Hepeviridae that city by synurbic WB, and the social-ecological system of human-WB can infect mammals [30]. Genotypes HEV3 and HEV4, which are shared interactions in the MAB. The prototype is a calibrated and validated with pigs worldwide, may be transmitted from wild boar to humans via ABM simulation that accurately predicts (multiple-resolution-goodness- the faecal-oral route [5,8]. HEV infections are generally self-limiting of-fit = 0.75) the magnitude and location of wild boar movements with low mortality, but immunocompromised individuals infected compared to reported wild boar presences in Barcelona [35]. The with HEV3 can develop chronic infections with rapid progression of BCNWB-EPI model aims to (1) identify high-risk areas for zoonotic liver disease and cirrhosis [30]. Prevalence of HEV infection in the MAB pathogens (HEV and AMR-CAMP); (2) evaluate the potential risk of WB population is 20%, indicating that zoonotic transmission between ASFV transmission through contaminated anthropogenic food re­ wild boars and humans may be common [8]. sources, which could be a potential entry point of ASFV in the Spanish WB is considered a reservoir of Campylobacter species [18], a com­ WB population; and (3) inform to support decision-making for priori­ mon bacterial cause of human gastroenteritis worldwide [31]. Although tizing risk-mitigation strategies to reduce the exposure of BCN citizens to Campylobacter commonly causes self-limiting disease, it also can pro­ zoonotic pathogens. duce severe symptoms and up to 10% of cases may require medical intervention [32]. Transmission can occur from animals or animal 2. Material and methods products to humans, particularly through faeces [31,32]. In the MAB, 60.8% of wild boars carried Campylobacter spp., with 35% of isolates 2.1. Study area having high virulence potential and 68.2% being multidrug resistant [17]. As described in the BCNWB prototype model [35], the synurbic CNP ASFV is a highly contagious virus affecting domestic pigs and wild WB forage essentially in the five districts of the city bordering the CNP boar, with up to 100% morbidity and mortality. ASFV is transmitted [10], namely Les Corts (6.02 km2 and 81,974 inhabitants); Sarrià-Sant through direct contact, ingestion of contaminated feedstuffs, and by Gervasi (19.91 km2 and 149,260 inhabitants); Gràcia (4.19 km2 and 3 G.-C. Carlos et al. One Health 17 (2023) 100598 Fig. 2. A) Mean daily number of WB faeces contaminated with hepatitis E virus (HEV) in a modelled year; B) Number of citizens exposed to HEV-contaminated faeces over the study period, as predicted by the HEV scenario. Cells are 100 m side. 121,798 inhabitants); Horta-Guinardó (11.96 km2 and 171,495 in­ Supplementary material 1. This Epidemiological sub-model simulated habitants); and Nou Barris (8.05 km2 and 170,669 inhabitants) [36] the spread of the three aforementioned pathogens among the WB pop­ (Fig. 1A). These districts include the main public hospital in Barcelona, ulations in two ecosystems (natural and urban), assessing the risk of and synurbic WB are actively fed by patients and visitors in the imme­ infection of the two zoonotic pathogens (HEV and AMR-CAMP) for the diate surroundings of the hospital facilities, creating a chance for human population in Barcelona. Three independent epidemiological pathogen transmission in this new human-WB interface. scenarios (one for each pathogen) were run following the SEIR (Sus­ ceptible, Exposed, Infected, and Recovered or Removed) model with WB-WB, human-WB, and environmental transmissions in the MAB over 2.2. Model description one year (Supplementary material 3). The BCNWB-EPI model used actual data for synurbic WB (Swb) lo­ This study is an epidemiological expansion of the BCNWB prototype cations gathered by the BCN Local Police in 2019, simulating an initial [35], an integrated ABM encompassing an Ecological sub-model and a population of 639 WB agents, classified as Swb (n = 292) and rural WB Social sub-model. The Ecological sub-model comprises the Environ­ (Wwb, n = 347). The initial locations of the infected and susceptible WB mental and WB modules, which manage the environment of the simu­ agents were based on actual hunting or capture locations (Fig. 1B). Due lation and the WB population from the CNP and BCN, respectively. The to computational limitations, a 10% of the actual human population (i. Social sub-model manages the human population of the city of BCN and e., 57,329 citizen agents) were randomly placed in residential buildings includes three citizen subtypes (feeders, regular citizens, and pet proportionally to the population in each district and initialized inside a owners). The ABM includes stochasticity due to environmental varia­ resting building. Model dynamics were driven by input data represent­ tion. In the Epidemiological sub-model presented in this study (Sup­ ing the initial population distribution, movement patterns of wild boar, plementary material 1 and 2), new agents were incorporated to account and environmental factors, as well as pathogen features such as trans­ for pathogen transmission through environmental sources. These sour­ mission probability and incubation period. All the WB agents that were ces included WB faeces in the HEV and AMR-CAMP scenarios, and WB not exposed, infected or resistant were in a susceptible status, as well as carcasses in the ASFV scenario, as they are key in the transmission of the the citizen agents, who were initially all susceptible. respective pathogens [8,30,37–39]. The spatial resolution was defined by 100 × 100 m grid covering the The BCN-EPI model was implemented in GAML language using MAB area modelled, and temporal resolution by one-hour time steps, GAMA platform, an open-source and ABM multi-platform combined running for 365 days. The model accounted for spatial data hourly, with GIS capabilities [40], and is completely described following the producing 8760 steps for each scenario. The most important processes of standard O.D.D. (Overview, Design concepts, Details) protocol [41] in 4 G.-C. Carlos et al. One Health 17 (2023) 100598 Fig. 2. (continued). the model repeated every time step were i) movement of WB and citizen The BCNWB-EPI model predicted (Fig. 2) exposure to HEV of 452 agents; ii) contact between susceptible and infected agents; iii) differ­ citizen agents (0.79% of the modelled population) in the study area of ential calculation of transmission probability for each pathogen; iv) in­ Barcelona after 365 simulated days, 67 of them considered citizens at cubation period for exposed agents; v) recovery or death for infected risk. The model’s predictions matched World Health Organization agents; and vi) periodic removal of environmental source agents. (WHO)’s estimations of 0.8% of the human population exposed to HEV Model input data for pathogen prevalence were obtained from pre­ annually, equivalent to 480 humans in the modelled population [44]. vious studies in the MAB AMR-CAMP [17,19] and HEV [8], whereas the As for AMR-CAMP scenario (Fig. 3), the model predicted 461 citizens remaining epidemiological data were collected and calculated through (0.80% of the modelled population) exposed after 365 days, with 55 of literature review on WB epidemiology (section A.4 Model verification, them considered to be at risk. These findings fall within the estimated calibration, and validation of O.D.D. protocol in Supplementary mate­ 0.44% to 0.93% annual human exposure range to AMR-CAMP (i.e., 264 rial 1, Supplementary material 4 and 5). to 558 individuals for the modelled population) [31]. To temporally and spatially analyse the model outputs, the BCNWB- The predicted citizen exposure to HEV and AMR-CAMP followed a EPI model recorded the number of environmental sources (faeces or temporal pattern both weekly (Table 1) and over the simulated period carcasses) and contact transmission events in each grid cell. The number (Supplementary material 6), with greater exposure probability during of faeces was corrected by the simulated duration and area, thus and around the weekend and at the beginning of the spring season. This calculating the annual probability of faeces occurring in each district. citizen exposure to HEV and AMR-CAMP also followed a spatial distri­ Additionally, the transmission event data were used to determine the bution pattern, with more overall contaminated faeces and consequently probability of citizen exposure according to the day of the week and higher citizen exposure in Nou Barris, followed by Sarrià-Sant Gervasi district. Spatial and temporal data statistical analyses were conducted and Horta-Guinardó as compared to Gràcia and Les Corts (Table 1). through general linear models (Poisson distribution) and Tukey tests In the ASFV scenario, the entire WB population in the CNP (including using the lme4 package [42] using the R 3.4.3 software [43]. both the Swb and Wwb agents) was exposed to the virus between 51 and 71 days after the index case (Fig. 4). ASFV transmission (Fig. 5) occurred 3. Results mainly through carcasses (87.6%) and to a lesser extent through direct contact (12.4%). The outbreak lasted between 71 and 124 days, The BCNWB-EPI model captured the spatiotemporal dynamics of resulting in a 95% reduction in the initial WB population. The model pathogen transmission within the study area, accounting for pathogen- calculated an R0 value of 16.9 for the simulated ASFV pathogen, in the specific variables, the potential influence of ASFV on WB population high part of the previously reported range from 1 to 17.3 for ASFV dynamics, and the impact of human-WB interactions on public health in outbreaks [38,45–47]. Barcelona. 5 G.-C. Carlos et al. One Health 17 (2023) 100598 Fig. 3. A) Mean daily number of WB faeces contaminated with antimicrobial-resistant Campylobacter (AMR-CAMP) in a modelled year; B) Number of citizens exposed to AMR-CAMP-contaminated faeces over the same period, as predicted by the AMR-CAMP scenario. Cells are 100 m side. 6 G.-C. Carlos et al. One Health 17 (2023) 100598 Fig. 3. (continued). 4. Discussion pathogens [4–6,8,15,16]. The higher occurrence of HEV and AMR-CAMP transmission events The BCNWB-EPI model provided the first-ever modelled information on weekends can be attributed to increased human-WB interactions in on the zoonotic hazards of HEV and AMR-CAMP posed by urban WB, wild boar interaction areas during these days, with a higher frequenta­ and assessed epidemiological and population dynamics consequences of tion of urban green areas and trails in the CNP by humans and a higher an ASFV outbreak in the WB population of Barcelona and CNP. likelihood of interacting with WB and/or encountering an infected Furthermore, the model introduced the use of fine scale spatially explicit environmental source. As for spatial heterogeneity of the predicted risk ABM to study urban wildlife epidemiology and disease and population of exposure to HEV and AMR-CAMP across districts, the variability in 1) management, including environmental, epidemiological, and biological human population size (which was positively correlated with the in­ variables, and successfully capturing the fine-scale epidemiological teractions with pathogen-contaminated faeces) and density; and 2) edge processes of the three pathogens in the human-urban wildlife interface. border between the urban space and CNP determining WB infiltration in As a result, the model not only numerically identified the humans the district, could probably explain the highest exposure probability in exposed to the zoonotic pathogens and the dynamics of potential WB Nou Barris, and the lowest predicted risk of exposure to HEV in Les Corts ASFV epidemics, but also located the most relevant transmission hot­ and Gràcia and to AMR-CAMP in Les Corts and Sarrià-Sant Gervasi. The spots within the WB population and from WB to humans through identification of temporal and geographical hotspots for pathogen environmental sources (Figs. 2, 3, and 5). transmission risks achieved through the ABM should allow to target and The exposure of human citizens to HEV (0.79%) and AMR-CAMP focus management strategies to effectively reduce and mitigate zoonotic (0.80%) after a year of simulation (Figs. 2 and 3) was similar, despite pathogen spread in the urban and (peri)urban environment. the three-fold higher prevalence of AMR-CAMP and the consequent ASFV-contaminated pork products pose a major risk for ASF trans­ higher probability of citizen exposure to AMR-CAMP than to HEV. This boundary emergence and spread [48], and have probably caused suggests that the higher infective viability of HEV in faeces may have anthropogenic ASF outbreaks jumping hundreds of kilometres [49]. WB contributed to the comparable levels of human exposure to both path­ synurbization increased the probability of Swb feeding on ASF- ogens independently of the prevalence in WB. Thus, a key role of faeces contaminated anthropogenic food resources [12] (section C.1 Initiali­ in the indirect transmission of both pathogens from WB to humans in zation of O.D.D. protocol in Supplementary material 1), and favoured urban environments, even though shorter persistence of faeces in urban rapid ASFV spread within the WB population with a high transmission areas was considered in the model. To address epidemiology of zoonotic rate due the high CNP WB population density [38,48], the low migration pathogens of wildlife in urban ecosystems in the human-WB interface, between CNP and other areas [26], and the increased contact rates due management measures should aim to prevent human-WB contact and to aggregation induced by anthropogenic food resources [15,16,20]. As the entry of WB in the urban area to avoid contamination with zoonotic in other ASF outbreaks, WB carcasses were the main source of 7 G.-C. Carlos et al. One Health 17 (2023) 100598 Table 1 transmission, enhancing ASFV survival, attracting WB, and increasing Above: Predicted temporal distribution of the probability of citizen exposure to their exposure to the virus [9,33,37,50]. The higher probability of hepatitis E virus and antimicrobial resistant Campylobacter. Below: Predicted contact with ASFV-contaminated anthropic food sources and the dra­ spatial distribution of the annual probability of presence of faeces contaminated matic epidemiological and demographic consequences in synurbic WB with Campylobacter or hepatitis E virus in the five urban districts of the study populations highlight the importance of effectively managing anthro­ area, and citizen exposure to these faeces. pogenic food waste and establish effective surveillance and control Hepatitis E virus Antimicrobial resistant Campylobacter measures in urban environments, in order to prevent ASFV outbreaks. scenario scenario Higher aggregation and interaction with humans of Swb increased Citizen exposure contact probability between susceptible WB and human hosts, and ES Monday 0.8868a 2.0189a spread pathogens in space and time, agreeing with previous reports of Tuesday 0.1731b 1.1346b the effect of anthropogenic resources on wildlife host-pathogen dy­ Wednesday 0.2500b 1.0192b Thursday 1.4423ac 0.3269c namics [15,16]. Thus, synurbic WB in urban areas pose a potential Friday 1.2692a 0.3846c epidemiological risk as disease reservoirs for both themselves and Saturday 2.2115c 1.0962b spillover events to humans. Food resources, faeces, and carcasses were Sunday 2.4423c 2.8462a key for pathogen maintenance and circulation [5,9,16,33,38,50,51]. p < 0.0001 p < 0.0001 Swb feeding on anthropogenic resources increase their probability of exposure to pathogens [10,12,15,16], and their movements between natural and urban ecosystems exposes other susceptible WB and Daily number Citizen Daily number Citizen of faeces exposure of faeces exposure humans. The temporal and spatial identification through ABM of hot­ spots for these transmission risks increased due to synurbization can Gràcia 0.0878ab 0.0860ab 0.2343a 0.1505ab inform management strategies aimed at reducing the risk of zoonotic Horta- a a b 0.1332 0.1372 0.3809 0.1346ab spillover events and protecting both wildlife and human health. Guinardó Les Corts 0.0379b 0.0707b 0.1293c 0.1195b To mitigate the risk of outbreak and spillover to humans, such stra­ Nou Barris 0.1371a 0.2556c 0.3445b 0.1903a tegies should aim at reducing the presence of WB in urban areas [4,5,7], Sarrià- using collective physical capture in (peri)urban areas where hunting is Sant 0.1359a 0.1441a 0.3864b 0.1300b Gervasi not allowed [52–54], and minimizing interactions between humans and p < 0.0001 p < 0.0001 p < 0.0001 P = 0.0156 WB. Due to their relevance, controlling ES by removing WB faeces from a, b, c urban green areas and raising awareness should the public health risk Different superscripts within each column indicate statistically significant posed by HEV and AMR-CAMP. As for ASF, the main measure to prevent differences (p < 0.05) among days (above) and among districts (below). an outbreak in urban WB populations would be reducing the accessi­ bility of WB to anthropogenic food resources, since Swb preferably feed Fig. 4. Dynamics of the African swine fever virus outbreak in the wild boar (WB) population modelled. Each line shows the number of susceptible, exposed, infected and resistant non-urban (Wwb) and synurbic (Swb) WB agents. 8 G.-C. Carlos et al. One Health 17 (2023) 100598 Fig. 5. Location and number of African swine fever virus (ASFV) transmission events over the study period predicted by the ASFV scenario. A) Direct transmission events between WB agents; B) Indirect transmission events from carcasses to WB agents. Cells are 100 m side. on anthropogenic resources [12,13,16], which are a major concern and design effective surveillance systems and diagnose emerging pathogens have provoked ASF outbreaks in WB populations in the past in urban [2,3]. This study demonstrates the usefulness and efficacy of ABM as an areas [9,48,49,51]. epidemiological decision-making support tool, adapted to the specific ABM do not only provide realistic spatiotemporal framework, but conditions and circumstances of the study area, as well as the specific also enable independent dynamic assessment of the environment. By conditions of human-wildlife interactions in the new urban-wildlife- programming WB and citizen agents accurately, the epidemiological human interface boosting the probability and consequences of disease extension also captured their spatial and temporal activity in the urban outbreaks. Integration of urban biologists with human and animal and natural ecosystems modelled, aligning with previous studies health epidemiologists and healthcare professionals in urban planning [55–57]. Overall, this ABM can be applied beyond the case study area of and management is essential to reduce risks of emerging zoonosis Barcelona as a powerful tool for local administrations to assess and transmission and anthropogenic disease outbreaks in wildlife, engaging manage the epidemiological risks posed by zoonotic pathogens in syn­ in a One Health approach through interdisciplinary cooperation. urbic wildlife populations in urban areas. However, this model has Data availability. calibration and validation limitations. A major challenge was the scar­ The data underlying this article are available in the article and in its city of published data on the epidemiological processes of zoonotic online supplementary material. pathogens, which obliged to extrapolate transmission rates and popu­ Supplementary data to this article can be found online at https://doi. lation exposure [34,58–60]. As a result, the model may have over­ org/10.1016/j.onehlt.2023.100598. estimated transmission rates, leading to an overestimation of citizen exposure and worst-case risk assessment. However, the model’s outputs Funding were consistent with the estimated ranges provided by the WHO, sug­ gesting good model performance despite these constraints. As more and This work was supported by the Ajuntament de Barcelona, who more accurate epidemiological data become available, further model provided funding for sampling [contracts 13/051, 15/0174, 16/0243, recalibration and refinement should improve its accuracy and predictive 16/0243–00-PR/01]. The Secretaria d’Universitats i Recerca del power. Departament d’Economia i Coneixement de la Generalitat de Catalunya As urbanization continues to expand, the risk of outbreaks of and the European Social Fund funded Raquel Castillo-Contreras [grant emerging diseases resulting from increased human-wildlife interactions numbers 2016FI_B 00425, 2017FI_B1 00040, 2018FI_B2_00030]. in the new urban-wildlife-human interface is likely to continue Emmanuel Serrano is supported by the Spanish Ministerio de Ciencia increasing, requiring One Health approach and interdisciplinary coop­ Innovación y Universidades (MICINN) through a Ramon y Cajal agree­ eration among animal, public, and environmental health managers ment [gran number RYC-2016-21120]. [2,3,16]. Improving knowledge on the evolving pathogen-host- environment relationships in each ecosystem and scenario is key to 9 G.-C. Carlos et al. One Health 17 (2023) 100598 Fig. 5. (continued). CRediT authorship contribution statement of the Consorci del Parc Natural de la Serra de Collserola members, and Joan Roldán, from the Departament d’Agricultura, Ramaderia, Pesca i González-Crespo Carlos: Conceptualization, Data curation, Formal Alimentació (Generalitat de Catalunya), for their support in collecting analysis, Investigation, Methodology, Software, Writing – original draft. basic data. The data on Campylobacter prevalence in the MAB was pre­ Martínez-López Beatriz: Conceptualization, Formal analysis, Investi­ viously published in collaboration with Marta Cerdà-Cuéllar, from the gation, Methodology, Software, Supervision, Writing – review & editing. Animal Health Research Centre-Institute of Agrifood Research and Conejero Carles: Data curation, Investigation, Methodology, Writing – Technology (CReSA-IRTA). The authors are also indebted to the Port review & editing. Castillo-Contreras Raquel: Data curation, Investi­ d’Informació Científica of the Universitat Autònoma de Barcelona (PIC- gation, Methodology, Writing – review & editing. Serrano Emmanuel: UAB) for their essential and invaluable computational support. The Conceptualization, Data curation, Investigation, Writing – review & authors are also thankful to the many collaborators and colleagues of the editing. López-Martín Josep Maria: Data curation, Methodology, Servei d’Ecopatologia de Fauna Salvatge (SEFaS) at the UAB and at the Writing – review & editing. Serra-Cobo Jordi: Funding acquisition, Center for Animal Disease Modeling and Surveillance (CADMS) from the Resources, Supervision, Writing – review & editing. Lavín Santiago: University of California at Davis, who helped in the wild boar data and Investigation, Methodology, Writing – review & editing. López-Olvera sample collection and in the computational analyses, respectively. 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