Explicit Agent-based Model Research Articles

Combinational Recommendation of Vaccinations, Mask-Wearing, and Home-Quarantine to Control Influenza in Megacities: An Agent-Based Modeling Study With Large-Scale Trajectory Data.

The outbreak of COVID-19 stimulated a new round of discussion on how to deal with respiratory infectious diseases. Influenza viruses have led to several pandemics worldwide. The spatiotemporal characteristics of influenza transmission in modern cities, especially megacities, are not well-known, which increases the difficulty of influenza prevention and control for populous urban areas. For a long time, influenza prevention and control measures have focused on vaccination of the elderly and children, and school closure. Since the outbreak of COVID-19, the public's awareness of measures such as vaccinations, mask-wearing, and home-quarantine has generally increased in some regions of the world. To control the influenza epidemic and reduce the proportion of infected people with high mortality, the combination of these three measures needs quantitative evaluation based on the spatiotemporal transmission characteristics of influenza in megacities. Given that the agent-based model with both demographic attributes and fine-grained mobility is a key planning tool in deploying intervention strategies, this study proposes a spatially explicit agent-based influenza model for assessing and recommending the combinations of influenza control measures. This study considers Shenzhen city, China as the research area. First, a spatially explicit agent-based influenza transmission model was developed by integrating large-scale individual trajectory data and human response behavior. Then, the model was evaluated across multiple intra-urban spatial scales based on confirmed influenza cases. Finally, the model was used to evaluate the combined effects of the three interventions (V: vaccinations, M: mask-wearing, and Q: home-quarantining) under different compliance rates, and their optimal combinations for given control objectives were recommended. This study reveals that adults were a high-risk population with a low reporting rate, and children formed the lowest infected proportion and had the highest reporting rate in Shenzhen. In addition, this study systematically recommended different combinations of vaccinations, mask-wearing, and home-quarantine with different compliance rates for different control objectives to deal with the influenza epidemic. For example, the “V45%-M60%-Q20%” strategy can maintain the infection percentage below 5%, while the “V20%-M60%-Q20%” strategy can maintain the infection percentage below 15%. The model and policy recommendations from this study provide a tool and intervention reference for influenza epidemic management in the post-COVID-19 era.

Modeling the impact of invasive species litter on conditions affecting its spread and potential regime shift

Many introduced plants pose invasion risks globally and threaten the biodiversity of native ecosystems. Such non-native plants can become invasive when they have advantages over native plants, such as having fewer natural enemies. Invasive plants often have the ability to alter ecosystem properties after they have become established, which can make it difficult to eliminate the invasive. In principle, this can cause a regime shift that may not be reversed through intense control efforts that increase mortality and reduce growth of the invasive species. Here we use spatially explicit agent-based modeling to simulate the invasion of an introduced tree species into a habitat occupied by a native species. The model describes an invasive tree with fast growth and high seed production and, in addition, produces litter that has a suppressive effect on native seedlings. These are properties, for example, shared by the invasive Melaleuca quinquenervia in southern Florida habitats. We use simulation modeling to test the following logical hypotheses: Partial suppression of native tree seedlings by the invasive tree's litter (1) will accelerate the spread of the invasive tree into native vegetation, (2) will impede efforts to control invasive spread through biocontrol, and (3) can cause a regime shift that is not reversed even if the biocontrol lowers invasive growth and reproduction to levels substantially lower than those of the native species. Additionally, (4) the earlier in the invasion biocontrol is introduced, the more effective it will be in reversing the invasion. The simulations support all four hypotheses. While these results highlight the potential for biocontrol of invasive tree species, our findings also suggest that successful elimination of positive litter feedbacks and invasive spread may critically depend on the timing of control efforts within the invasion process.

Context-dependent host dispersal and habitat fragmentation determine heterogeneity in infected tick burdens: an agent-based modelling study.

As the incidence of tick-borne diseases has sharply increased over the past decade, with serious consequences for human and animal health, there is a need to identify ecological drivers contributing to heterogeneity in tick-borne disease risk. In particular, the relative importance of animal host dispersal behaviour in its three context-dependent phases of emigration, transfer and settlement is relatively unexplored. We built a spatially explicit agent-based model to investigate how the host dispersal process, in concert with the tick and host demographic processes, habitat fragmentation and the pathogen transmission process, affects infected tick distributions among hosts. A sensitivity analysis explored the impacts of different input parameters on infected tick burdens on hosts and infected tick distributions among hosts. Our simulations indicate that ecological predictors of infected tick burdens differed among the post-egg life stages of ticks, with tick attachment and detachment, tick questing activity and pathogen transmission dynamics identified as key processes, in a coherent way. We also found that the type of host settlement strategy and the proportion of habitat suitable for hosts determined super-spreading of infected ticks. We developed a theoretical mechanistic framework that can serve as a first step towards applied studies of on-the-ground public health intervention strategies.

An Integrated Ecological-Social Simulation Model of Farmer Decisions and Cropping System Performance in the Rolling Pampas (Argentina)

Agricultural systems experience land-use changes that are driven by population growth and intensification of technological inputs. This results in land-use and cover change (LUCC) dynamics representing a complex landscape transformation process. In order to study the LUCC process we developed a spatially explicit agent-based model in the form of a Cellular Automata implemented with the Cell-DEVS formalism. The resulting model called AgroDEVS is used for predicting LUCC dynamics along with their associated economic and environmental changes. AgroDEVS is structured using behavioral rules and functions representing a) crop yields, b) weather conditions, c) economic profit, d) farmer preferences, e) technology level adoption and f) natural resources consumption based on embodied energy accounting. Using data from a typical location of the Pampa region (Argentina) for the 1988-2015 period, simulation exercises showed that the economic goals were achieved, on average, each 6 out of 10 years, but the environmental thresholds were only achieved in 1.9 out of 10 years. In a set of 50-years simulations, LUCC patterns quickly converge towards the most profitable crop sequences, with no noticeable tradeoff between the economic and environmental conditions.

Particle Swarm Optimization for Calibration in Spatially Explicit Agent-Based Modeling

Particle Swarm Optimization for Calibration in Spatially Explicit Agent-Based Modeling

Developing an agent-based model of oral healthcare utilization by Chinese Americans in New York City.

Many Chinese Americans experience certain barriers (e.g., low income, English as a second language, lack of insurance, cultural differences, discrimination) when they seek oral healthcare services. These barriers may contribute to health disparities by discouraging use (leading to reduced utilization) of preventive and treatment services. This research adopts a modeling approach to develop theory that accounts for dynamic relationships operating at multiple levels, from individuals to families to communities. A multi-method and multi-level modeling approach allows for the interaction of factors at different levels of aggregation. This research applies spatially explicit agent-based modeling to examine how demographic, socioeconomic, and geographic factors shape access to oral healthcare for low-income Chinese Americans in New York City. The simulation model developed in this research was used to test different intervention scenarios involving community health workers who facilitate care coordination and other health promotion activities. In addition to demographic characteristics and socioeconomic factors, this study also considers geographic factors and spatial behavior, such as distance and activity space. The overarching contribution of this study is to provide a complex systems science framework to better understand access to oral healthcare for urban Chinese Americans, toward adapting it for other racial/ethnic minority groups, by integrating health-seeking behaviors at the individual level, barriers to care at multiple levels, and opportunities for health promotion at the community level.

Using Multiple Scale Space-Time Patterns to Determine the Number of Replicates and Burn-In Periods in Spatially Explicit Agent-Based Modeling of Vector-Borne Disease Transmission

(1) Background: The stochastic nature of agent-based models (ABMs) may be responsible for the variability of simulated outputs. Multiple simulation runs (i.e., replicates) need to be performed to have enough sample size for hypothesis testing and validating simulations. The simulation outputs in the early-stage of simulations from non-terminating ABMs may be underestimated (or overestimated). To avoid this initialization bias, the simulations need to be run for a burn-in period. This study proposes to use multiple scale space-time patterns to determine the number of required replicates and burn-in periods in spatially explicit ABMs, and develop an indicator for these purposes. (2) Methods: ABMs of vector-borne disease transmission were used as the case study. Particularly, we developed an index, D, which enables to take into consideration a successive coefficient of variance (CV) over replicates and simulation years. The comparison between the number of replicates and the burn-in periods determined by D and those chosen by CV was performed. (3) Results: When only a single pattern was used to determine the number of replicates and the burn-in periods, the results varied depending on the pattern. (4) Conclusions: As multiple scale space-time patterns were used for the purposes, the simulated outputs after the burn-in periods with a proper number of replicates would well reproduce multiple patterns of phenomena. The outputs may also be more useful for hypothesis testing and validation.

Modelling the effects of the repellent scent marks of pollinators on their foraging efficiency and the plant-pollinator community.

Pollinator insects forage in complex and unpredictable resource landscapes, often using social information from congeneric individuals to acquire knowledge about their environment. It has long been recognized that this process allows them to exploit floral resources more efficiently and thus increase individual fitness. However, by creating correlations between the behaviors of pollinators within a population, this could also indirectly influence the entire plant-pollinator community. One type of social information used by pollinators is the scent mark left on the corolla of flowers by previous visitors, which can be used as a cue to avoid recently depleted resources. We developed a spatially explicit agent-based model to examine the effects, at both individual and community levels, of pollinators using these scent marks. The model simulates a population of pollinators foraging on flowers in a continuous 2D space in which we can vary the density of pollinators. We showed that the use of scent marks as a source of information significantly increased the foraging efficiency of pollinators except when competition between pollinators was very low. At the community level, this also resulted in a marked homogenization between floral resources within the landscape: in the absence of scent marks, the coefficient of variation of the remaining nectar quantity per flower strongly increased with greater pollinator competition, but it remained low at all levels of competition when scent marks were used by the pollinators. Finally, the use of scent marks markedly decreased the number of pollinator flower visits, especially at high levels of pollinator competition, which can potentially reduce the pollination service.

An integrated framework of global sensitivity analysis and calibration for spatially explicit agent‐based models

AbstractCalibration of agent‐based models (ABMs) is a major challenge due to the complex nature of the systems being modeled, the heterogeneous nature of geographical regions, the varying effects of model inputs on the outputs, and computational intensity. Nevertheless, ABMs need to be carefully tuned to achieve the desirable goal of simulating spatiotemporal phenomena of interest, and a well‐calibrated model is expected to achieve an improved understanding of the phenomena. To address some of the above challenges, this article proposes an integrated framework of global sensitivity analysis (GSA) and calibration, called GSA‐CAL. Specifically, variance‐based GSA is applied to identify input parameters with less influence on differences between simulated outputs and observations. By dropping these less influential input parameters in the calibration process, this research reduces the computational intensity of calibration. Since GSA requires many simulation runs, due to ABMs' stochasticity, we leverage the high‐performance computing power provided by the advanced cyberinfrastructure. A spatially explicit ABM of influenza transmission is used as the case study to demonstrate the utility of the framework. Leveraging GSA, we were able to exclude less influential parameters in the model calibration process and demonstrate the importance of revising local settings for an epidemic pattern in an outbreak.

A data driven agent-based model that recommends non-pharmaceutical interventions to suppress Coronavirus disease 2019 resurgence in megacities.

Before herd immunity against Coronavirus disease 2019 (COVID-19) is achieved by mass vaccination, science-based guidelines for non-pharmaceutical interventions are urgently needed to reopen megacities. This study integrated massive mobile phone tracking records, census data and building characteristics into a spatially explicit agent-based model to simulate COVID-19 spread among 11.2 million individuals living in Shenzhen City, China. After validation by local epidemiological observations, the model was used to assess the probability of COVID-19 resurgence if sporadic cases occurred in a fully reopened city. Combined scenarios of three critical non-pharmaceutical interventions (contact tracing, mask wearing and prompt testing) were assessed at various levels of public compliance. Our results show a greater than 50% chance of disease resurgence if the city reopened without contact tracing. However, tracing household contacts, in combination with mandatory mask use and prompt testing, could suppress the probability of resurgence under 5% within four weeks. If household contact tracing could be expanded to work/class group members, the COVID resurgence could be avoided if 80% of the population wear facemasks and 40% comply with prompt testing. Our assessment, including modelling for different scenarios, helps public health practitioners tailor interventions within Shenzhen City and other world megacities under a variety of suppression timelines, risk tolerance, healthcare capacity and public compliance.

Model Input and Optimization: Improving the Speed and Accuracy of Our COVID-19 Hospitalization Forecasts

Background: During the COVID-19 pandemic, public-health decision makers have increasingly relied on hospitalization forecasts that are routinely provided, accurate, and based on timely input data to inform pandemic planning. In North Carolina, we adapted an existing agent-based model (ABM) to produce 30-day hospitalization forecasts of COVID-19 and non–COVID-19 hospitalizations for use by public-health decision makers. We sought to continually improve model speed and accuracy during forecasting. Methods: The geospatially explicit ABM included movement of agents (ie, patients) among 104 short-term acute-care hospitals, 10 long-term acute-care hospitals, 421 licensed nursing homes, and the community in North Carolina. Agents were based on a synthetic population of North Carolina residents (ie, >10.4 million agents). We assigned SARS-CoV-2 infections to agents according to county-level susceptible, exposed, infectious, recovered (SEIR) models informed by reported COVID-19 cases by county. Agents’ COVID-19 severity and probability of hospitalization were determined using agent-specific characteristics (eg, age, comorbidities). During May 2020–December 2020, we produced weekly 30-day forecasts of intensive care unit (ICU) and non-ICU bed occupancy for COVID-19 agents and non–COVID-19 agents statewide and by region under a range of SARS-CoV-2 effective reproduction numbers. During the reporting period, we identified optimizations for faster results turnaround. We evaluated the incorporation of real-time hospital-level occupancy data at model initialization on forecast accuracy using mean absolute percent error (MAPE). Results: During May 2020–December 2020, we provided 31 weekly reports of 30-day hospitalization forecasts with a 1-day turnaround time. Reports included (1) raw and smoothed 7-day average values for 42 model output variables; (2) static visuals of ICU and non-ICU bed demand and capacity; and (3) an interactive Tableau workbook of hospital demand variables. Identifying code efficiencies reduced a single model runtime from ~100 seconds to 28 seconds. The use of cloud computing reduced simulation runtime from ~20 hours to 15 minutes. Across forecasts, the average MAPEs were 21.6% and 7.1% for ICU and non-ICU bed demand, respectively. By incorporating hospital-level occupancy data, we reduced the average MAPE to 6.5% for ICU bed demand and 3.9% for non-ICU bed demand, indicating improved accuracy. Conclusions: We adapted an ABM and continually improved it during COVID-19 forecasting by optimizing code and computing resources and including real-time hospital-level occupancy data. Planned SEIR model updates for enhanced forecasts include the addition of compartments for undocumented infections and recoveries as well as permission of reinfection from recovered compartments.Funding: NoDisclosures: None

Agent-based modelling of the social-ecological nature of poverty traps in southwestern Madagascar

CONTEXTIn Madagascar land use pressure on natural resources has increased drastically over the last decades. For subsistence farmers this situation often leads to an accelerated unsustainable exploitation of resources with negative consequences for their livelihood, which can be described as a social-ecological trap (SET). OBJECTIVEOur paper seeks to understand the dynamics of SETs and demonstrates a model-driven scenario on how farmers may escape the trap. For this, we developed a spatially explicit empirical agent-based model (SEALM) to simulate possible future trends of farmers' crop management and the effects of these trends on the environment, household (HH) economy, food self-sufficiency (FSS) and HH coping strategies to food insecurity. METHODSUsing links between social-ecological system (SES) components that depict real-world conditions, SEALM focuses on three village communities for which extensive data were available from transdisciplinary research comprising surveys, remote sensing analysis, and field-based validation. To identify the most significant parameters and to quantify how parameter uncertainty influences model outcomes, local and global sensitivity analyses were performed. Simulations were conducted in annual time steps from 2015 to 2045 to depict two contrasting scenarios: A baseline scenario (A) and an improved crop management scenario to increase FSS (B). For scenario assessments we used direct simulation outputs (FSS, HH income, land cover) and further indices calculated based on model outcomes (coping strategy index, landscape fragmentation, and the Gini coefficient). RESULTS AND CONCLUSIONSScenario A predicted expansion of cropland and fragmentation of remaining forest areas. Simulations at each time step displayed how SET situations occurred due to self-reinforcing ecological and social constraints and affected HHs future coping capacity to food shortages. Here, FSS was insufficient (< 1.0) for most HHs and was <0.5 for years with rainfall deficits. Scenario B demonstrated a way to navigate out of the SET by increasing crop yields reflecting improved management and resulting in significantly higher FSS values and a lower coping strategy index. SEALM considers the heterogeneity in farmers' abilities, constraints, behaviour and decision-making and depicts the dynamic interplay of SES components contributing to SET. SIGNIFICANCESEALM serves as a discussion support with local stakeholders and experts and it allows to better tailor site specific land use recommendations due to its empirical driven and spatially explicit character. However, strategies and interventions that lower the poverty rate in the long term and encourage exit out of trap situations require major policy changes.

Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement

African elephants (Loxodonta africana) are well-studied and inhabit diverse landscapes that are being transformed by both humans and natural forces. Most tools currently in use are limited in their ability to predict how elephants will respond to novel changes in the environment. Individual-, or agent-based modeling (ABM), may extend current methods in addressing and predicting spatial responses to environmental conditions over time. We developed a spatially explicit agent-based model to simulate elephant space use and validated the model with movement data from elephants in Kruger National Park (KNP) and Chobe National Park (CNP). We simulated movement at an hourly scale, as this scale can reflect switches in elephant behavior due to changes in internal states and short-term responses to the local availability and distribution of critical resources, including forage, water, and shade. Known internal drivers of elephant movement, including perceived temperature and the time since an individual last visited a water source, were linked to the external environment through behavior-based movement rules. Simulations were run on model landscapes representing the wet season and the hot, dry season for both parks. The model outputs, including home range size, daily displacement distance, net displacement distance, and maximum distance traveled from a permanent water source, were evaluated through qualitative and quantitative comparisons to actual elephant movement data from both KNP and CNP. The ABM was successful in reproducing the differences in daily displacements between seasons in each park, and in distances traveled from a permanent water source between parks and seasons. Other movement characteristics, including differences in home range sizes and net daily displacements, were partially reproduced. Out of the all the statistical comparisons made between the empirical and simulated movement patterns, the majority were classified as discrepancies of medium or small effect size. We have shown that a resource-driven model with relatively simple decision rules generates trajectories with movement characteristics that are mostly comparable to those calculated from empirical data. Simulating hourly movement (as our model does) may be useful in predicting how finer-scale patterns of space use, such as those created by foraging movements, are influenced by finer spatio-temporal changes in the environment.

Simulating Urban Shrinkage in Detroit via Agent-Based Modeling

While the world’s total urban population continues to grow, not all cities are witnessing such growth—some are actually shrinking. This shrinkage has caused several problems to emerge, including population loss, economic depression, vacant properties and the contraction of housing markets. Such issues challenge efforts to make cities sustainable. While there is a growing body of work on studying shrinking cities, few explore such a phenomenon from the bottom-up using dynamic computational models. To fill this gap, this paper presents a spatially explicit agent-based model stylized on the Detroit Tri-County area, an area witnessing shrinkage. Specifically, the model demonstrates how the buying and selling of houses can lead to urban shrinkage through a bottom-up approach. The results of the model indicate that, along with the lower level housing transactions being captured, the aggregated level market conditions relating to urban shrinkage are also denoted (i.e., the contraction of housing markets). As such, the paper demonstrates the potential of simulation for exploring urban shrinkage and potentially offers a means to test policies to achieve urban sustainability.

Quantifying Uncertainty in Exposure to Coastal Hazards Associated with Both Climate Change and Adaptation Strategies: A U.S. Pacific Northwest Alternative Coastal Futures Analysis

Coastal communities face heightened risk to coastal flooding and erosion hazards due to sea-level rise, changing storminess patterns, and evolving human development pressures. Incorporating uncertainty associated with both climate change and the range of possible adaptation measures is essential for projecting the evolving exposure to coastal flooding and erosion, as well as associated community vulnerability through time. A spatially explicit agent-based modeling platform, that provides a scenario-based framework for examining interactions between human and natural systems across a landscape, was used in Tillamook County, OR (USA) to explore strategies that may reduce exposure to coastal hazards within the context of climate change. Probabilistic simulations of extreme water levels were used to assess the impacts of variable projections of sea-level rise and storminess both as individual climate drivers and under a range of integrated climate change scenarios through the end of the century. Additionally, policy drivers, modeled both as individual management decisions and as policies integrated within adaptation scenarios, captured variability in possible human response to increased hazards risk. The relative contribution of variability and uncertainty from both climate change and policy decisions was quantified using three stakeholder relevant landscape performance metrics related to flooding, erosion, and recreational beach accessibility. In general, policy decisions introduced greater variability and uncertainty to the impacts of coastal hazards than climate change uncertainty. Quantifying uncertainty across a suite of coproduced performance metrics can help determine the relative impact of management decisions on the adaptive capacity of communities under future climate scenarios.

Understanding the Effects of China's Agro-Environmental Policies on Rural Households' Labor and Land Allocation with a Spatially Explicit Agent-Based Model.

Understanding household labor and land allocation decisions under agro-environmental policies is challenging due to complex human-environment interactions. Here, we develop a spatially explicit agent-based model based on spatial and socioeconomic data to simulate households' land and labor allocation decisions and investigate the impacts of two forest restoration and conservation programs and one agricultural subsidy program in rural China. Simulation outputs reveal that the forest restoration program accelerates labor out-migration and cropland shrink, while the forest conservation program promotes livelihood diversification via increasing non-farm employment. Meanwhile, the agricultural subsidy program keeps labor for cultivation on land parcels with good quality, but appears less effective for preventing marginal croplands from being abandoned. The policy effects on labor allocation substantially differ between rules based on bounded rational and empirical knowledge of defining household decisions, particularly on sending labor out-migrants and engaging in local off-farm jobs. Land use patterns show that the extent to which households pursue economic benefits through shrinking cultivated land is generally greater under bounded rationality than empirical knowledge. Findings demonstrate nonlinear social-ecological impacts of the agro-environmental policies through time, which can deviate from expectations due to complex interplays between households and land. This study also suggests that the spatial agent-based model can represent adaptive decision-making and interactions of human agents and their interactions in dynamic social and physical environments.

How do structural and agent-based factors influence the effectiveness of incentive policies? A spatially explicit agent-based model to optimize woodland-for-water PES policy design at the local level

How do structural and agent-based factors influence the effectiveness of incentive policies? A spatially explicit agent-based model to optimize woodland-for-water PES policy design at the local level

Modelling interventions to control COVID-19 outbreaks in a refugee camp

BackgroundIn the absence of effective treatments or vaccines, non-pharmaceutical interventions are the mainstay of control in the COVID-19 pandemic. Refugee populations in displacement camps live under adverse conditions that are...

Modelling the interaction between policies and international trade flows for liquid biofuels: an agent-based modelling approach

World biofuel production and trade have grown exponentially in the last decade. Nevertheless, the interaction between the markets for oilseeds (food/feed/bioenergy) and liquid vegetable oil-based biofuels is overwhelmingly complex and thus not well understood. In this study, we developed a spatially explicit agent-based model to provide insights into the effect of farmers' behaviour on trade flows and biodiesel production and to shed light on the influence of import tariffs for both palm oil and biodiesel on system behaviour. This new approach enables us to assess different types of rational economic behaviour for the adoption of crops by farmers. Results show that model outcomes can vary substantially based on the assumptions made concerning the behaviour of farmers. Moreover, we found that biodiesel trade and production are more sensitive to a change in the EU-28's biodiesel import tariff than to a change in the EU-28's palm oil import tariff. Overall, our results show that social processes, actors' heterogeneity, and institutions play an important role in the behaviour of the system.

Understanding transmission and control of the pork tapeworm with CystiAgent: a spatially explicit agent-based model

BackgroundThe pork tapeworm, Taenia solium, is a serious public health problem in rural low-resource areas of Latin America, Africa and Asia, where the associated conditions of nuerocysticercosis (NCC) and porcine cysticercosis cause substantial health and economic harms. An accurate and validated transmission model for T. solium would serve as an important new tool for control and elimination, as it would allow for comparison of available intervention strategies, and prioritization of the most effective strategies for control and elimination efforts.MethodsWe developed a spatially-explicit agent-based model (ABM) for T. solium (“CystiAgent”) that differs from prior T. solium models by including a spatial framework and behavioral parameters such as pig roaming, open human defecation, and human travel. In this article, we introduce the structure and function of the model, describe the data sources used to parameterize the model, and apply sensitivity analyses (Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC)) to evaluate model parameters.ResultsLHS-PRCC analysis of CystiAgent found that the parameters with the greatest impact on model uncertainty were the roaming range of pigs, the infectious duration of human taeniasis, use of latrines, and the set of “tuning” parameters defining the probabilities of infection in humans and pigs given exposure to T. solium.ConclusionsCystiAgent is a novel ABM that has the ability to model spatial and behavioral features of T. solium transmission not available in other models. There is a small set of impactful model parameters that contribute uncertainty to the model and may impact the accuracy of model projections. Field and laboratory studies to better understand these key components of transmission may help reduce uncertainty, while current applications of CystiAgent may consider calibration of these parameters to improve model performance. These results will ultimately allow for improved interpretation of model validation results, and usage of the model to compare available control and elimination strategies for T. solium.