Agent-based Research Articles

Abstract A029: Integrated modeling of renal cancer bone metastasis to illuminate tumor progression and therapy response

Abstract Bone metastatic clear cell renal cell carcinoma (BM ccRCC) is a persistent clinical challenge and a source of significant morbidity and lethality for up to 40% of metastatic patients. Despite the recent approval of life-prolonging agents (antiangiogenic agents, immunotherapy) patients with BM ccRCC will eventually progress, due to emerging resistance. Bidirectional communication between tumor cells and bone stroma has emerged as a key determinant of disease progression and recurrence. However, a major challenge in addressing the black box of therapy response in bone is the lack of efficient and informative systems that allow us to analyze the heterotypic stromal-epithelial interaction, further complicated by the anatomical inaccessibility of bone. For these reasons, the evolution of the metastatic niche and its implications in therapy response remain elusive. In order to improve clinical results, it is critical to establish biologically informed pre-clinical models to provide a mechanistic understanding of tumor progression in bone and treatment outcomes. To this purpose, we have established clinically relevant models of ccRCC bone metastasis that span in vivo and ex vivo multiphoton microscopy (iMPM and eMPM), tissue engineered bone window systems, and computational oncology. iMPM displays both sensitivity and time-resolution to identify dynamic interactions between ccRCC cells and bone 3D adaptive niches, which support therapy response and resistance. However, iMPM in bone is limited due to its cortical thickness, increased light scattering, and complex topology. As a novel alternative, we created a tissue-engineered bone construct (TEBC) that, after direct implantation of cancer cells, is combined with an adjacent skin window, allowing for non- destructive intravital examination of tumor growth. To flank these in vivo dynamic analyses, we established a pipeline for ex vivo extraction of topological information related to the molecular and cellular niches involved in tumor progression and response to treatment (eMPM). This method allows reconstruction of whole lesions, including zones more distant from the bone interface, paired with an extensive panel of molecular markers. To further investigate the effects of a broad multi-parameter space on tumor regression or persistence upon treatment, in silico, we developed an in vivo-inspired agent-based model (ABM) of ccRCC in bone. A computational approach combined to ad hoc biological experimentation can refine the experimental design, test clinically relevant hypotheses (including impact of treatments on disease progression) and predict scenarios that guide biological testing towards more successful outcomes. Consequently, by integrating these approaches, we are currently studying the progression of the bone metastatic niche and its response to therapy, with the ultimate goal of overcoming therapy failure. Citation Format: Sergio Barrios, Luca Marsilio, Stefan Maksimovic, Matthew Campbell, Stefano Casarin, Eleonora Dondossola. Integrated modeling of renal cancer bone metastasis to illuminate tumor progression and therapy response [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A029.

An Agent-Based Model of Human–Whale Interactions in the North Atlantic

We created an agent-based model (ABM; a computational model) for human–whale interactions in the North Atlantic area. We specifically looked at boats, lobster lines, and rogue fishnet interactions with the North Atlantic right whales (Eubalaena glacialis), and we assessed the conditions under which such interactions are more likely to result in injuries and fatalities for this critically endangered whale, such as the density of ships in the water. ABMs are a methodology particularly useful in data poor problems (where machine learning cannot be used), and are based on rules of interactions between various agents (human and nonhuman) and/or a specific physical environment; they can showcase and assess potential future scenarios. We informed our model with current data from the North Atlantic Right Whale Consortium and the National Oceanic and Atmospheric Administration and present our preliminary findings here.

Influenza Vaccination, Household Composition, and Race-Based Differences in Influenza Incidence: An Agent-Based Modeling Study.

Objectives. To estimate the effect of influenza vaccination disparities. Methods. We compared symptomatic influenza cases between Black and White races in 2 scenarios: (1) race- and age-specific vaccination coverage and (2) equal vaccination coverage. We also compared differences in household composition between races. We used the Framework for Reconstructing Epidemiological Dynamics, an agent-based model that assigns US Census‒based age, race, households, and geographic location to agents (individual people), in US counties of varying racial and age composition. Results. Influenza cases were highest in counties with higher proportions of children. Cases were up to 30% higher in Black agents with both race-based and race-equal vaccination coverage. Compared with corresponding categories of White households, cases in Black households without children were lower and with children were higher. Conclusions. Racial disparities in influenza cases persisted after equalizing vaccination coverage. The proportion of children in the population contributed to the number of influenza cases regardless of race. Differences in household composition may provide insight into racial differences and offer an opportunity to improve vaccination coverage to reduce influenza burden for both races. (Am J Public Health. Published online ahead of print November 14, 2024:e1-e8. https://doi.org/10.2105/AJPH.2024.307878).

The power of social networks and social media’s filter bubble in shaping polarisation: an agent-based model

AbstractTthe role social media platforms play on the emergence of polarisation is an ongoing debate in the political communication literature. Social media’s filter bubbles and online echo chambers shape people’s opinions by curating the information they have available. However, the extent to which this is the case remains unclear. Social simulation scholars have provided valuable insights into the subject through opinion dynamics models and agent-based modelling approaches. This article proposes a social simulation approach to the topic of opinion dynamics from a political communication perspective to understand how social network configurations and the media environment contribute to the emergence of national identity polarisation. We built an agent-based simulation model of national identity dynamics with a multilayer multiplex network of interacting agents in a hybrid media environment of both, traditional media and social media platforms. We use the Catalan secessionist movement to ground, contextualise and empirically inform parts of our model. We found that the initial social network setup conditions had a large impact on the emergence of polarisation amongst agents. In particular, homophily-based social networks composed of a majority of like-minded individuals produced greater polarisation compared to random networks. This was aggravated in the presence of social media filtering algorithms, selectively exposing agents to supportive information. These results emphasise the importance of both the selective exposure by social media filtering algorithms and one’s social networks (echo chambers) for polarisation to emerge. This interaction reinforces the influence of social media platforms and social networks have on the emergence of polarisation.

Identification of optimum scopes of environmental drivers for schistosome-transmitting Oncomelania hupensis using agent-based model in Dongting Lake Region, China.

Oncomelania hupensis (O. hupensis), the sole intermediate host of Schistosoma japonicum, greatly influence the prevalence and distribution of schistosomiasis japonica. The distribution area of O. hupensis has remained extensive for numerous years. This study aimed to establish a valid agent-based model of snail density and further explore the environmental conditions suitable for snail breeding. A marshland with O. hupensis was selected as a study site in Dongting Lake Region, and snail surveys were monthly conducted from 2007 to 2016. Combined with the data from historical literature, an agent-based model of snail density was constructed in NetLogo 6.2.0 and validated with the collected survey data. BehaviorSpace was used to identify the optimal ranges of soil temperature, pH, soil water content, and vegetation coverage for snail growth, development and reproduction. An agent-based model of snail density was constructed and showed a strong agreement with the monthly average snail density from the field surveys. As soil temperature increased, the snail density initially rose before declining, reaching its peak at around 21°C. There were similar variation patterns for other environmental factors. The findings from the model suggested that the optimum ranges of soil temperature, pH, soil water content and vegetation coverage were 19°C to 23 °C, 6.4 to 7.6, 42% to 75%, and 70% to 93%, respectively. A valid agent-based model of snail density was constructed, providing more objective information about the optimum ranges of environmental factors for snail growth, development and reproduction.

An integrated empirical and computational study to decipher help-seeking behaviors and vocal stigma

BackgroundProfessional voice users often experience stigma associated with voice disorders and are reluctant to seek medical help. This study deployed empirical and computational tools to (1) quantify the experience of vocal stigma and help-seeking behaviors in performers; and (2) predict their modulations with peer influences in social networks.MethodsExperience of vocal stigma and information-motivation-behavioral (IMB) skills were prospectively profiled using online surveys from a total of 403 Canadians (200 singers and actors and 203 controls). Data were used to formulate an agent-based network model of social interactions on vocal stigma (self-stigma and social-stigma) and help-seeking behaviors. Network analysis was performed to evaluate the effect of social network structure on the flow of IMB among virtual agents.ResultsLarger social networks are more likely to contribute to an increase in vocal stigma. For small social networks, total stigma is reduced with higher total IMB but not much so for large networks. For agents with high social-stigma and risk for voice disorder, their vocal stigma is resistant to large changes in IMB ( > 2 standard deviations). Agents with extreme IMB and stigma values are likely to polarize their networks faster in larger social groups.ConclusionsWe integrated empirical surveys and computational techniques to contextualize vocal stigma and IMB beyond theory and to quantify the interaction among stigma, health-seeking behavior and influence of social interactions. This work establishes an effective, predictable experimental platform to provide scientific evidence in developing interventions to reduce health stigma in voice disorders and other medical conditions.

Malaria treatment for prevention: a modelling study of the impact of routine case management on malaria prevalence and burden

BackgroundTesting and treating symptomatic malaria cases is crucial for case management, but it may also prevent future illness by reducing mean infection duration. Measuring the impact of effective treatment on burden and transmission via field studies or routine surveillance systems is difficult and potentially unethical. This project uses mathematical modeling to explore how increasing treatment of symptomatic cases impacts malaria prevalence and incidence.MethodsLeveraging the OpenMalaria stochastic agent-based transmission model, we first simulated an array of transmission intensities with baseline effective treatment coverages of 28%, 44%, and 54% incorporated to reflect the 2023 coverage distribution across Africa, as estimated by the Malaria Atlas Project. We assessed the impact of increasing coverage to as high as 60%, the highest 2023 estimate on the continent. Subsequently, we performed simulations resembling the specific subnational endemicities of Kenya, Mozambique, and Benin, using the Malaria Atlas Project estimates of intervention coverages to reproduce historical subnational prevalence. We estimated the impact of increasing effective treatment coverage in these example settings in terms of prevalence reduction and clinical cases averted in children under 5 years old and the total population.ResultsThe most significant prevalence reduction – up to 50% – was observed in young children from lower transmission settings (prevalence below 0.2), alongside a 35% reduction in incidence, when increasing effective treatment from 28% to 60%. A nonlinear relationship between baseline transmission intensity and the impact of treatment was observed. Increasing effective treatment coverage to 60% reduced the risk in high-risk areas (prevalence in children under 5 years old > 0.3), affecting 39% of young children in Benin and 20% in Mozambique previously living in those areas. In Kenya where most of the population lives in areas with prevalence below 0.15, and case management is fairly high (53.9%), 0.39% of children were estimated to transition to lower-risk areas.ConclusionsImproving case management directly reduces the burden of illness, but these results suggest it also reduces transmission, especially for young children. With vector control interventions, enhancing case management can be an important tool for reducing transmission intensity over time.

Model-inferred timing and infectious period of the chickenpox outbreak source

BackgroundIn May 2024, a chickenpox outbreak was reported at Xiasha Primary School located in Nanshan District, Shenzhen City, China, with a total of 12 cases identified. Despite thorough on-site investigations, the source of infection remained undetected. The purpose of our study was to infer the timing and duration of the infectious period of the initial case using modeling techniques, thereby deducing the identity of the source.MethodsWe conducted an individual contact survey within the class affected by the epidemic and utilized an agent-based model (ABM) to estimate the key parameters related to the timing of the infectious source’s emergence and the duration of its infectiousness. The point estimates derived from the ABM served as prior information for a subsequent Bayesian analysis, which in turn provided the posterior distribution for these parameters.ResultsOur models suggested the infection source entered the classroom around April 24th (95% credible interval: April 22nd to April 26th), with an infectious period of approximately two days. Based on these findings, we should aim to detect students who may have been absent due to atypical chickenpox symptoms during this period and closely examine teachers who were present for two consecutive days for any indication of potential infection.ConclusionThis study demonstrates the efficacy of combining contact surveys with mathematical modeling for outbreak source tracing, offering a novel approach to supplement field epidemiological surveys.Clinical trial numberNot applicable.

A hybrid workflow connecting a network and an agent-based model for predictive pedestrian movement modelling

A hybrid workflow connecting a network and an agent-based model for predictive pedestrian movement modelling

Combining sterile insect releases with refuge areas to delay the evolution of resistance to Bt sugarcane: an agent-based modeling approach.

The strategic use of refuge areas is a well-known method for delaying the development of pest resistance to Bacillus thuringiensis (Bt) crop. A lesser-known method to control against resistance development is sterile insect releases. In this article, an agent-based simulation model is used to test the effectiveness of combining the use of Bt sugarcane, refuge areas, and sterile insect releases as an integrated strategy against Eldana saccharina Walker (Lepidoptera: Pyralidae) infestation and resistance development. Individual insects are modeled with their own genetic traits on a simulated sugarcane field that represents either Bt or refuge area. The model is applied to 2 hypothetical case studies. In the first experiment, resistance development and infestation dynamics in Bt sugarcane without refuge areas are considered using various sterile:wild sterile release ratios, and different release distributions. In the second experiment, the inclusion of a refuge area in Bt sugarcane is considered, using various sterile:wild releases ratios and different release distributions. A trade-off between sterile insect releases and the use of the refuge area was observed, and could, in some cases, work against each other.

Contact Risk Assessment in Dynamic Indoor Settings through Agent‐Based Modeling: A Spatially Explicit and Reproducible Approach

This study introduces an agent‐based model (ABM) pedestrian simulation tool to assess the risk of close contact (6 feet) in dynamic indoor environments, specifically in urban settings with diverse social activities and spatial structures. Our approach uses machine learning‐based sensitivity analysis (SA) to identify factors impacting the number of individual contacts, such as individual stay time and area. In addition, we conducted an in‐depth quantitative analysis to evaluate how specific factors, such as the strategic placement of obstacles, dwell time, and stay time near the entrances, mitigate the number of contacts. This analysis provides valuable insights for developing practical guidelines to curb contact risks in indoor environments. Lastly, we share the model, validation methods, and associated data as an open‐source Python library, complete with comprehensive documentation. This aims at fostering collaborative research and enables the application of our model across various scenarios, contributing to the development of spatially explicit models. Such efforts enhance the understanding of contact risks in urban indoor settings and promote joint research efforts, thus advancing the field through shared knowledge and tools.

Enhancing Ecological Education: Utilizing Agent-Based Modeling to Simplify the Impacts of Deforestation on Amphibians

Understanding how deforestation and changes in habitat boundaries affect biodiversity is essential for developing conservation solutions. These topics are central to biology and ecology programs, where students learn to apply their knowledge in real-world conservation efforts. Higher education plays a crucial role in strengthening this understanding, particularly in life sciences programs. Given the complexity of ecological processes in altered landscapes, agent-based modeling provides an interactive and engaging way to simplify and visualize the effects of land use changes. In this study, we integrate Amazonian anurans, highly sensitive to temperature and humidity fluctuations, with an Agent-based model to simulate the impacts of deforestation, habitat restoration, and land abandonment on species survival and movement. Their ectothermic nature and dependence on pulmocutaneous respiration make them especially vulnerable to the drier and more variable conditions caused by deforestation. Integrating this model into conservation biology courses has enhanced learning by encouraging independent exploration, both in and out of the classroom. This tool, an agent-based model, is particularly suited for university-level ecology and conservation courses, and can also serve as an effective awareness tool in environmental education and decision-making workshops, highlighting the negative effects of human-made habitat changes on biodiversity.

CANVAS: A Canadian behavioral agent-based model for monetary policy

We develop the Canadian behavioral Agent-Based Model (CANVAS) that complements traditional macroeconomic models for forecasting and monetary policy analysis. CANVAS represents a next-generation modeling effort featuring enhancements in three dimensions: introducing household and firm heterogeneity, departing from rational expectations, and modeling price and quantity setting heuristics within a production network. The expanded modeling capacity is achieved by harnessing large-scale Canadian micro- and macroeconomic datasets and incorporating adaptive learning and simple heuristics. The out-of-sample forecasting performance of CANVAS is found to be competitive with a benchmark vector auto-regressive (VAR) model and a DSGE model. When applied to analyze the COVID-19 pandemic episode, our model helps explain both the macroeconomic movement and the interplay between expectation formation and cost-push shocks. CANVAS is one of the first macroeconomic agent-based models applied by a central bank to support projection and alternative scenarios, marking an advancement in the toolkit of central banks and enriching monetary policy analysis.

Forecasting SARS-CoV-2 epidemic dynamic in Poland with the pDyn agent-based model

We employ pDyn (derived from “pandemics dynamics”), an agent-based epidemiological model, to forecast the fourth wave of the SARS-CoV-2 epidemic, primarily driven by the Delta variant, in Polish society. The model captures spatiotemporal dynamics of the epidemic spread, predicting disease-related states based on pathogen properties and behavioral factors. We assess pDyn’s validity, encompassing pathogen variant succession, immunization level, and the proportion of vaccinated among confirmed cases. We evaluate its predictive capacity for pandemic dynamics, including wave peak timing, magnitude, and duration for confirmed cases, hospitalizations, ICU admissions, and deaths, nationally and regionally in Poland. Validation involves comparing pDyn’s estimates with real-world data (excluding data used for calibration) to evaluate whether pDyn accurately reproduced the epidemic dynamics up to the simulation time. To assess the accuracy of pDyn’s predictions, we compared simulation results with real-world data acquired after the simulation date. The findings affirm pDyn’s accuracy in forecasting and enhancing our understanding of epidemic mechanisms.

Population-level impact of switching to 1-dose human papillomavirus vaccination in high-income countries: examining uncertainties using mathematical modeling.

A concern in high-income countries is that switching to 1-dose human papillomavirus (HPV) vaccination could cause a rebound in HPV infection and cervical cancer if 1-dose efficacy or duration were inferior to 2 doses. Using mathematical modeling and up-to-date trial-based data, we projected the population-level effectiveness of switching from 2-dose to 1-dose vaccination under different vaccine efficacy and duration assumptions in high-income countries. We used HPV-ADVISE (Agent-based Dynamic model for VaccInation and Screening Evaluation), a transmission-dynamic model of HPV infection and cervical cancer, varying key model assumptions to identify those with the greatest impact on projections of HPV-16 and cervical cancer incidence over time: 1) 1-dose vaccine efficacy and vaccine duration, 2) mechanisms of vaccine efficacy and duration over time, 3) midadult (>30 years of age) sexual behavior, 4) progression to cervical cancer among midadults, and 5) vaccination coverage and programs. In high-income countries, 1-dose vaccination would cause no appreciable rebound in HPV-16 infection, except for a limited rebound under the most pessimistic assumptions of vaccine duration (average, 25 years), because 1) the switch would occur when HPV prevalence is low because of high 2-dose vaccination coverage and 2) individuals would be protected during their peak ages of sexual activity (<35 to 40 years of age). Our model projects a more limited rebound in cervical cancer because of a shift to older age at infection, resulting in fewer life-years left to potentially develop cancer. Projections were robust when varying key model assumptions. High protection during peak ages of sexual activity in high-income countries would likely mitigate any potential rebounds in HPV infection and cervical cancer under the most pessimistic assumptions of 1-dose efficacy and duration.

An evacuation model considering pedestrian group behavior under violent attacks

In recent years, violent attacks have garnered significant attention from the international community and gradually evolved into a global issue. In responding to such emergencies, effective emergency evacuation is an essential means to ensure public safety. In order to explore the pedestrian evacuation dynamics in violent attacks and understand the behavioral characteristics of individuals and the dynamic interactions of groups during the evacuation process, this paper proposes an agent-based cellular automata evacuation model. First, each agent is assigned several internal attributes such as life value, panic value, and movement speed, and the assignments fully take into account the heterogeneity of individuals. Next, by setting the attack strategy of attackers and the evacuation mechanism of pedestrians, the complex system is modeled from a microscopic perspective. Finally, the effects of critical factors on the pedestrian evacuation process, such as panic coefficient, crowd structure, group ratio, attack distance and intensity, attacker location and number, and counterattack probability, are discussed through simulation. The results show that the tension caused by moderate panic is beneficial, but excessive panic will lead to increased evacuation time and fatalities. Meanwhile, the group behavior among pedestrians causes them to move relatively lagging, and the evacuation efficiency decreases as the group scale increases. Additionally, increases in attack intensity, attack distance, and attacker number all result in more casualties, and the consequences are most severe when multiple attackers operate separately, but counterattacks by pedestrians can significantly improve the overall safety of evacuation. Our study can provide some reference and basis for emergency management under violent attacks.

Mitigating the Charging Rush Hour

Great effort is put into making our mobility system more sustainable in order to mitigate climate change. One corner stone of this endeavour is the transition from internal combustion engines to electric engines for private cars. This transition, however, introduces new challenges, especially regarding the demand for electrical energy from renewable sources. One emerging phenomenon is the so-called charging rush hour, i.e. a sharp demand spike when many people arrive home and begin charging their electric cars. In this study we use an agent-based model calibrated with empirical data on mobility behaviour to investigate strategies for mitigating this charging rush hour. Studied counter strategies include telecommuting and the possibility to charge the car at work. Our findings show that the baseline peak of 65 MW per 100 000 people can only be reduced to 55 MW per 100 000 people even when combining multiple strategies. Thus the small incentives and policy changes investigated here are not enough to solve the problem of the charging rush hour and more disruptive changes to our mobility system are required.

Modelling plausible scenarios for the Omicron SARS-CoV-2 variant from early-stage surveillance

We used a spatially explicit agent-based model of SARS-CoV-2 transmission combined with spatially fine-grained COVID-19 observation data from Public Health Scotland to investigate the initial rise of the Omicron (BA.1) variant of concern. We evaluated plausible scenarios for transmission rate advantage and vaccine immune escape relative to the Delta variant based on the data that would have been available at that time. We also explored possible outcomes of different levels of imposed non-pharmaceutical intervention. The initial results of these scenarios were used to inform the Scottish Government in the early outbreak stages of the Omicron variant.Using the model with parameters fit over the Delta variant epidemic, some initial assumptions about Omicron transmission rate advantage and vaccine escape, and a simple growth rate fitting procedure, we were able to capture the initial outbreak dynamics for Omicron. We found that the modelled dynamics hold up to retrospective scrutiny. The modelled imposition of extra non-pharmaceutical interventions planned by the Scottish Government at the time would likely have little effect in light of the transmission rate advantage held by the Omicron variant and the fact that the planned interventions would have occurred too late in the outbreak’s trajectory. Finally, we found that any assumptions made about the projected distribution of vaccines in the model population had little bearing on the outcome, in terms of outbreak size and timing. Instead, it was the landscape of prior immunity that was most important.

A multi-agent system to dynamically devise an LCA framework weighting system taking into account socio-technical and environmental consideration

This research article explores the need to adapt the weighting system of a life cycle assessment (LCA) framework to a wide range of socio-technical and environmental considerations, which are complex and sensitive to external stressors. The study demonstrates the potential of agent-based systems to adapt a weighting system to dynamic conditions, and suggests integrating an agent-based model to handle uncertainties of weighting in environmental impact assessment. Generative adversarial networks (GANs) and multi-agent systems (MAS) are utilized to address data limitations and simulate diverse scenarios. We confirm the potential of agent-based systems to analyse the effective management of uncertainties and customization of weighting systems in environmental impact assessments to improve decision making. Furthermore, the study emphasizes the importance of continuous adaptation and recalibration to ensure the system remains relevant in dynamic environments. The results confirm that MAS is a powerful tool for managing uncertainty, customizing weighting systems and improving decision making in environmental assessments. Moreover, the study acknowledges challenges and sets the groundwork for future research.

Life history traits of the target pest and transmission routes of the biocide are critical for the success of the boosted Sterile Insect Technique

The sterile insect technique (SIT) is an environmentally friendly pest control strategy that consists of inundative releases of mass-reared sterilized males over defined areas, where they mate with wild females, resulting in no offspring and a declining pest population. The technique has effectively managed many crop pests and vector-borne diseases worldwide. A new approach, called boosted SIT, has been proposed to gain efficiency. It combines SIT with the contamination of wild females by sterile males previously coated with biocides. The present study investigated to what extent life history traits of the target pest and biocides can make the boosted SIT more efficient than the classical SIT. We built a generic agent-based model (SIT++) that simulates the population dynamics of insect pests. We then explored parameters related to the mating system, spermatic competition, and fecundity, taking examples from the biology of three well-known Dipteran pest species (Bactrocera dorsalis, Ceratitis capitata, and Glossina palpalis gambiensis). We found that for boosted SIT to be more beneficial than SIT, horizontal transmission of the biocide to the same generation and to the progeny must be very high. Female fecundity was the other key parameter behind the success of boosted SIT, which was more efficient with insect pests having low reproduction rates. In particular, vertical transmission and late killing time were critical parameters. We also observed that a high level of virulence can help, but only when the boosted SIT is already advantageous; otherwise, it becomes detrimental. The boosted SIT might be advantageous depending on the life history traits of the target pest and transmission routes. For a more extensive exploration, the model can easily be tailored to pests with very different life history traits.