Spatial configuration and the Dilution Effect in Vector-Borne Diseases: Insights from Agent-Based Model

Spatial configuration and the Dilution Effect in Vector-Borne Diseases: Insights from Agent-Based Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial configuration and the Dilution Effect in Vector-Borne Diseases: Insights from Agent-Based Model Fabiola Nieto-Rabiela, Fernando Esponda, Oscar Rico-Chávez, Gerardo Suzán This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6109476/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Biodiversity provides ecosystem services, including health protection by reducing infection risks through the "dilution effect." This mechanism occurs when vertebrate diversity decreases disease prevalence in host populations. Prior SIR (Susceptible-Infected-Recovery) models have explored the dilution effect, but incorporating spatial configuration remains a challenge. We simulated the distances that agents (vectors and hosts) could travel under different movement types. We assessed maximum prevalence ((infected agents/total population) × 100) in each simulation using four Agent-Based Models (ABMs) implemented in NetLogo. These models tested scenarios with and without susceptibility diversity, combined with different agent densities (0.25 to 2) and movement distances. Our findings indicate that movement type has minimal relevance to the dilution effect. However, movement distance significantly influences disease prevalence. Notably, the dilution effect counteracts density-dependent effects on infectious disease prevalence. This study highlights the importance of susceptibility diversity in host communities for the effectiveness of the dilution effect, while vector susceptibility diversity plays a minor role. We identified movement and distance as key parameters influencing the dilution effect, which had not been analyzed before. Future studies should consider movement distance when evaluating the impact of spatial configuration on disease transmission. Our results provide new insights into the complex dynamics of infectious disease prevalence across spatial configurations and ecological settings. Agent-Based models Dilution Effect Spatial Configuration Vector-Borne Diseases Figures Figure 1 Figure 2 Figure 3 Introduction The dilution effect has been studied across a wide variety of ecological and epidemiological contexts, particularly in systems involving vector-borne diseases such as Lyme disease, West Nile Virus, and amphibian chytridiomycosis (Ostfeld and Keesing 2000; Searle et al. 2011 ; Swaddle and Stavros 2008). Dobson ( 2004 ) highlighted the intricate population dynamics of pathogens in systems with multiple host species, emphasizing how variations in host community structure can dramatically influence disease transmission. Incorporating such theoretical insights into applied models is essential to better understand the mechanisms driving the dilution effect. These studies underscore that species diversity reduces disease prevalence by altering host-vector interactions. For instance, Ostfeld and Keesing (2000) demonstrated that increased host diversity disrupts transmission pathways by diluting the pool of competent hosts. Similarly, Swaddle and Calos (2008) observed that in avian communities, higher species richness decreased the prevalence of WNV (West Nile Virus) by limiting vector access to highly competent hosts. Regarding Lyme disease, research has shown that species such as the Virginia opossum reduce tick survival rates by grooming, thereby lowering disease transmission (Ostfeld and Keesing 2012). For WNV, avian diversity acts as a buffer, reducing human exposure by diluting the proportion of highly competent reservoirs (Swaddle and Calos 2008). In this study, hosts are categorized by their species-specific susceptibilities, while vectors are modeled as generalist organisms capable of interacting with multiple host types. By distinguishing these ecological roles, we seek to capture the mechanisms driving the dilution effect in natural settings. Despite this growing body of evidence, few studies have explored how spatial configuration and movement patterns influence these dynamics. Our research seeks to fill this gap by employing Agent-Based Models (ABMs) to examine how spatial arrangement and host susceptibility diversity shape the dilution effect in vector-borne diseases. Biodiversity not only provides numerous ecosystem services (Perrings et al. 2010 ) but also contributes to health protection by mitigating infection risks. The dilution effect is driven by three main mechanisms: generalist vector behavior, diversity in host susceptibility, and interspecies interactions (Ostfeld and Keesing 2000). By integrating spatial variables into ABMs, we aim to extend these foundational concepts and provide novel insights into the spatial ecology of infectious diseases. The dilution effect is triggered by three key mechanisms. Firstly, vectors must exhibit generalist behavior devoid of strong food preferences. Second, the host community should be diverse in terms of species susceptibility. Lastly, interaction between species within the host community is essential (Ostfeld and Keesing 2000). The dilution effect has been investigated across various natural contexts, such as Lyme disease, Batrachochytrium dendrobatids, and West Nile Virus (Ostfeld and Keesing 2000; Searle et al. 2011 ; Swaddle and Calos 2008; Civitello et al. 2015 ). To fully understand how species diversity affects the spread of disease, we need to look at natural examples as well as understand how complex interactions work on a theoretical level. We then need to test these ideas in the real world to fully grasp how they work. The dilution effect was supported inside a mathematical model by earlier research (Roberts and Heesterbeek 2018 ; Chen et al. 2022 ; Occhibove et al. 2022 ; Huang et al. 2015 ; Collins, Bever, and Hersh 2020 ; Miller and Huppert 2013). Most notably, Roche et al., 2013 , who reported a decrease in disease prevalence with increased diversity in a SIR mathematical model. They highlight that is not diversity alone that facilitates the expression of the dilution effect, but instead the diversity in susceptibilities of host and vector communities (the likelihood or potential of contracting a disease). By incorporating spatial configuration into an Agent-Based Model (ABM), we hope to discover whether the dilution effect remains true without changing vector and host abundances. We built a computational model aimed at explaining the dilution effect process. The ABM can simulate the interactions between agents in space, like vectors and hosts, on a per-sample level, giving you control over the types of movements and the distances between them. (Heppenstall et al. 2012 ; Watkins et al. 2014 ; Peck 2014 ). Density, defined as the quantity of agents present in a specific area, plays a critical role in the manifestation of the dilution effect. In ecological and epidemiological contexts, higher population densities typically lead to increased interactions among individuals, which can enhance disease transmission rates. Conversely, lower densities may reduce interaction rates and limit the spread of diseases. This concept is fundamental for understanding and modeling the dynamics of populations and the spread of infectious diseases. Manipulating density in ABMs can impact the probability of contact between vectors and hosts, which is essential for understanding the dilution process. Crucially, no research has explored the impact of spatial arrangement on the dilution effect. While exposure to infectious diseases can influence disease prevalence (Organización Panamericana de la Salud, 2011), the majority of studies employing spatial analysis have focused on epidemiological research, frequently utilizing geolocation and Geographic Information Systems (GIS) (Auchincloss et al. 2012 ). Yet, the impact of movement patterns on vector-borne diseases, whether in wildlife or isolated environments, has remained largely unexplored. As such, we assess the effects of three movement patterns. Our study provides a novel perspective on the dilution effect by integrating spatial configurations into disease dynamics models. Incorporating movement patterns and susceptibility diversity allows a more precise assessment of how host distribution impacts disease spread. These findings contribute to the theoretical framework of emerging infectious disease epidemiology, suggesting that density and spatial movement are key factors in the manifestation of the dilution effect. Furthermore, our model provides evidence that variability in host susceptibilities modulates infection dynamics, emphasizing the importance of heterogeneity in disease ecology. Aims The main objective of this research is to assess how variations in susceptibility among vector and host species, along with spatial configuration, influence the prevalence of infectious diseases within an agent-based model framework. Model description. ODD Protocol (Grimm et al. 2006 ; 2010 ) Purpose The primary aim of our model is to investigate the effect of variations in host and vector susceptibility diversity, in conjunction with spatial configuration and movement patterns, on the prevalence of vector-borne diseases using agent-based modeling (ABM). By understanding these factors, we hope to shed light on the mechanisms driving disease prevalence and the potential for the dilution effect. It is important to note that the model deliberately abstracts away detailed ecological or physiological traits of host and vector species. This approach ensures that the analysis focuses exclusively on the effects of spatial movement patterns and density on disease dynamics, avoiding potential confounding effects from species-specific traits. The model simplifyies the system to general host-vector interactions, isolating the influence of spatial parameters and clarifying their role in the dilution effect. Entities, State Variables, and Scales : Our model includes two types of agents: hosts and vectors. Hosts and vectors can be either susceptible or infected. The state variables of each agent include their current health status, position, and movement pattern. The spatial environment is represented as a grid consisting of 33 by 33 patches. The temporal scale is established at daily intervals. Between the models, agents (hosts and vectors) were differentiated by their probability of becoming infected. Each host species is defined by a unique susceptibility parameter, ranging from 0.2 to 1, which is assigned at initialization. In the absence of diversity, we assign a susceptibility of 1 to all hosts. The diversity of species differs only in their infection probability. This approach allowed us to observe how susceptibility diversity impacted disease prevalence. Process Overview and Scheduling Agents exhibit movement patterns daily, categorized as linear, random, or circular. Interactions among individuals can facilitate the transmission of diseases. New vectors are generated at consistent intervals. The daily schedule facilitates dynamic interactions among agents (Fig. 1 ). Design Concepts The model integrates elements of spatial ecology and is based on SIR (Susceptible-Infected-Recovered) epidemiological principles. The dilution effect and disease prevalence result from interactions among agents. Agents adhere to established behaviors and lack the capacity to adapt, learn, or anticipate future states. They lack explicit goals as a driving force. Agents perceive their immediate surroundings, identifying proximate hosts or vectors. Disease transmission transpires when vectors and hosts inhabit the same or directly adjacent patches. Movement patterns and disease transmission are stochastic, with no collective behaviors; all interactions are individual-based. The model tracks disease prevalence, agent states, and spatial distributions. Initialization The model initiates with a predetermined quantity of hosts and vectors, randomly distributed across the grid. Predefined probabilities establish initial infection states, ensuring variability in the initial conditions of each simulation run. The values are manifested in Table 1 . Table 1 Initial parameters of each simulation. Model input parameters Parameter Species Community species Vector Community vectors Initial hosts infected 0 0 0 0 Initial vectors infected 300 300 100 100 Number of host species 1 10 1 1 Number of vector species 1 1 1 10 Probability to be infected (host susceptibility) 1 0.2-1 1 1 Probability to be infected (vector susceptibility) 1 1 1 0.2-1 Input Data The model sets all parameters and does not use any external data. This approach allows for controlled experimentation and analysis of various hypothetical scenarios. When setting the model parameters, we followed approaches outlined in previous studies using simple epidemiological models, such as SIR models. The transmission, reproduction, and mortality rates of vectors were based on works like Roche et al. ( 2013 ) and other recent studies in disease dynamics (Chen et al. 2022 )( Table 2 ). These parameters were established to ensure that the model's results were comparable with previous studies. The parameters also reflecting the inherent complexity of susceptibility diversity and spatial interactions between hosts and vectors. Table 2 Parameter justification Parameter Values Used Justification References Host and Vector Infection Probability Hosts: 0.2–1 (with diversity) / 1 (without diversity) Vectors: 0.2–1 (with diversity) / 1 (without diversity) Hosts exhibit variability in susceptibility due to immune response and host-pathogen interactions. With diversity, susceptibility varies (e.g., opossums reduce Lyme transmission). Without diversity, all hosts have maximum susceptibility. Vector susceptibility varies but less pronounced. Ostfeld & Keesing (2000) Roche et al. ( 2013 ) Huang et al. ( 2015 ) Initial Number of Infected Hosts and Vectors Infected vectors: 300 (host community models) / 100 (vector community models) Infected hosts: 0 High initial infected vectors ensure transmission without extreme stochastic events. Zero initial infected hosts simulate an emerging outbreak scenario. Roche et al. ( 2013 ) Number of Host and Vector Species Hosts: 10 (with diversity) / 1 (without diversity) Vectors: 10 (with diversity) / 1 (without diversity) Higher diversity reduces disease prevalence (dilution effect). Low diversity can amplify disease transmission. Vector diversity effects depend on species composition. Civitello et al. ( 2015 ) Swaddle & Calos (2008) Occhibove et al. ( 2022 ) Agent Density 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2 Affects contact rates and transmission. Low density: fewer contacts, lower transmission. High density: increased encounters, potential outbreaks. Dobson ( 2004 ) Miller & Huppert (2013) Spatial Grid Size 33 × 33 patches Allows effective spatial interactions while maintaining computational efficiency. Uses toroidal environment to prevent edge effects. Wilensky (1997) Heppenstall et al. ( 2012 ) Movement Types and Distances Types: Linear, random, circular Distances: 1, 5, 10, 20, 30, 50 Organism mobility influences pathogen spread. Simulates local (1–5 patches) and broad dispersal (30–50 patches). Peck ( 2014 ) Vector Mortality and Reproduction Mortality: 15–30 days Reproduction: 5 vectors every 30 days Vector lifespan based on studies (e.g., malaria, dengue). Reproduction rate ensures stable population in simulations. Roche et al. ( 2013 ) Submodels The model includes submodels for movement patterns (linear, random, and circular), density, and susceptibility diversity. Each submodel adds a layer of complexity, allowing us to explore the effects of different variables on disease dynamics. Agents and Environments In our model, the environment is a grid in which each patch can host multiple agents. Hosts and vectors are the agents, each with state variables such as health status (susceptible or infected) and movement capabilities. Simulation Procedures The simulation runs until no susceptible agents remain, with each day representing a time step. During each step, agents move according to their assigned pattern (linear, random, or circular). Hosts and vectors interact, leading to potential disease transmission if an infected vector encounters a susceptible host. Movement Patterns : We investigated three distinct movement patterns: linear, random, and circular. Linear movement is when agents move straight ahead, serving as a control model. Random movement, derived from the wolf-sheep predation model in NetLogo (Wilensky, 1997), is more realistic and accounts for organisms' objectives in their movements. The circular movement represents a macro-scale home range in ecology. Each movement pattern was evaluated for its impact on disease prevalence and the dilution effect. Density Manipulation Density manipulation involves varying densities through the adjustment of agent numbers within a given spatial area. Simulations were conducted at densities of 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, and 2 to investigate density-dependent effects. These values were chosen to explore a wide range of densities, representing different ecological scenarios from low to high host aggregation. The density was determined by dividing the total number of hosts by the total number of grids. The variations enabled observation of the impact of agent density on disease dynamics. Spatial Configuration : Spatial configuration modifications were implemented, including the type of movement and the distance covered by agents. Three distinct movement types were tested to simulate different ecological scenarios: linear, random, and circular. The distances traversed ranged from − 16 to 16, corresponding with a 33x33 grid in NetLogo. NetLogo operates within a toroidal environment, allowing agents that reach the grid´s edge to reappears on the opposite side. The evaluated movement distances were 1, 5, 10, 20, 30, and 50 patches, which corresponded to surface area percentages crossed by agents of 0.09%, 0.45%, 0.91%, 1.83%, 2.75%, and 4.59%, respectively. These distances represent different scales of host movement, from local interactions to broad dispersal across the landscape. Evaluation The evaluation involved executing each of the four models using combinations of three movement forms, six distances, and eight densities, resulting in 392 model variations. Each model was executed 1000 times for every ABM configuration, with a maximum duration of 100 days. The variable of interest, the prevalence of infected agents, was calculated daily by dividing the total number of infected agents by the total number of agents and multiplying the result by 100. Within each model run, we extracted the maximum prevalence value over the 100 days to analyse. Model Environment Using NetLogo 6.1.1., we evaluated four distinct ABM models. Each model comprised 1000 hosts and 300 vectors, with agents categorized as either susceptible or infected (Table 1 ). Infection probabilities differed according to community susceptibilities. The vector mortality rate (15–30 days) and reproduction (5 vectors per 30 days) were based on Roche et al. ( 2013 ), ensuring comparability with prior studies on vector-borne diseases. Values are obtained from the SIR model as outlined by Roche et al. ( 2013 ). The initial percentage of infected vectors was set to 100% in the host models to minimize variability related with vector lifespan and reproduction. In the vector models, one-third of the population was initially infected, influenced by variables related to lifespan and reproduction variables. An evaluation of the maximum prevalence behavior concerning changes in agents’ susceptibility was conducted (Supplementary material). Results The analysis evaluated the impact of susceptibility diversity by calculating the 1000 maximum prevalence results for the ABM species alongside the ABM community and the ABM vector in conjunction with the ABM community vector, identifying significant differences. Because of the non-normal distribution and diverse variances, we used the Wilcoxon Mann-Whitney test to compare the populations (Fay and Proschan 2010 ). There were notable variations in both comparisons, with the statistical U values of 18.38 and 5.1537, respectively. Both U values ​​have a p-value of < 2.2×10 − 16 . This suggests that an increase in the U value corresponds to stronger evidence against the equality of medians between the two samples (Fig. 2 ). The increased diversity in agent susceptibility led to a decrease in prevalence in both cases. The distances of host movement were approximately three times greater than those of vectors, consistent with the mathematical model proposed by Roche et al. ( 2013 ). This suggests that the two models can be compared. Spatial In each model, incorporating the diversity of susceptibilities results in a decrease in prevalence; however, the differences among types of movement (circular, linear, random) are minimal (Supplementary material). Density The density tests concentrated on four models employing random movement, as no significant differences were observed among the movement types, with ten distinct movement distances ranging from 1 to 10 patches. No significant changes in maximal prevalence were observed across most models, excep for the scenario where susceptibility diversity was absent (Fig. 3). Figure 3. Maximal prevalence in the random movement model without diversity testing density. The X-axis represents the distance (patches) moved through by the agents, while the Y-axis indicates the maximal prevalence. Each block evaluates the density specified at the top of the block. Discussion This study represents an initial evaluation of the impact of spatial configuration on the dilution effect. Our findings suggest that while the type of movement does not appear to significantly affect the dilution effect, however, the distance moved by agents does affect prevalence. Our results reinforce and expand upon previous studies on the dilution effect. Mathematical models such as those by Roche et al. ( 2013 ) and Roberts and Heesterbeek ( 2018 ) have demonstrated that species diversity can reduce disease prevalence through competition mechanisms among hosts. However, these models have traditionally assumed homogeneous spatial distributions and have not accounted for vector and host mobility. In contrast, our agent-based model (ABM) reveals that host movement distance can be a determining factor in disease prevalence, even in the absence of changes in vector or host abundance. Research conducted by Chen et al. ( 2022 ) suggests that spatial configuration affects transmission dynamics; however, our results highlight that landscape structure and agent mobility can either mitigate or enhance the dilution effect depending on the scale at which they occur. Furthermore, while differential equation-based models (Huang et al., 2015 ) have shown that connectivity between patches affects disease transmission, our agent-based model enables the assesment of the impact of realistic and heterogeneous movement patterns on transmission. Various grid measures were not evaluated, as the transmission of vector-borne diseases relies on contact, with density serving as a significant factor influencing contact probability, independent of surface area. Modifying variables like the number of daily bites will only affect the time to reach maximum prevalence without significantly changing the prevalence values. Variables such as mortality, the capacity of host reinfection, and vector reproduction have the potential to significantly alter transmission dynamics. Our model confirms that increased species diversity and host community susceptibilities facilitates the effective operation of the dilution effect. That is consistent with the findings of Chen et al., ( 2022 ), which indicate that a decrease in the abundance of competent hosts correlate with a reduction in disease prevalence. This occurs naturally as the susceptibilities of the host community diversify. Differences in vector susceptibilities have a negligible impact on disease prevalence and the dilution effect. This result is elucidated by the observations of Occhibove et al. ( 2022 ) who indicate that the interspecific dynamics of communities, due to their complexity, exhibit varying degrees of the dilution effect contingent upon the generalism of the vector. Consequently, it is advisable to prioritize the diversity of host susceptibilities over the vectors when implementing control policies. Furthermore, our findings are consistent with earlier research (Roche et al. 2013 ) indicating that the vector abundance increase prevalence. Constant vector abundances do not yield equivalent amplification effects. It is essential to exercise caution when drawing broad generalizations regarding movement effects, given specific assumptions inherent in our model. It presumes a high transmission rate and a limited surface area. Factors influencing vector movement, such as food preferences, are excluded. Additional research with modified variables is essential for improving understanding of the influence of movement patterns on the dilution effect. Our study indicates that movement distance significantly influences prevalence. The change in prevalence is modest, yet remains consistent until agents cover approximately 1% of the total surface area. This threshold may provide a significant for comprehending the impact of movement on epidemiological management strategies. The ABM model utilized three movement patterns (linear, circular, and random) to represent host and vector behaviors, considering the complexity of actual movements. These patterns facilitate the abstraction of a variety ecological scenarios and provide a basis for comparison and validation with other studies. Integrating more complex movement patterns could increase computational complexity without providing benefits to the outcomes. This lays the foundation with fundamental movement types, facilitating subsequent research that integrates greater complexity. The diversity of susceptibilities mitigates the impact of density on prevalence. Increasing density significantly increases prevalence in scenarios devoid of susceptibility diversity. This suggests that variations in susceptibility and the manifestation of the dilution effect may provide protection or mitigate the prevalence of infectious diseases as population density increases. This suggests that the diversity of susceptibilities and the expression of the dilution effect may provide protection or mitigate the prevalence of infectious diseases as population density increases. Furthermore, the study conducted by (Roberts and Heesterbeek 2018 ), is relevant as it investigated density in a similar context. The findings suggest an increase in system complexity; however, further investigation is required. This presents an intriguing area that warrants further exploration, both in practical application and theoretical modeling. The spatial movement in our model illustrates the benefits of computational models: accurate representation of agent behavior. It is essential to exercise caution and thoroughly understand each modified parameter's implications. Parsimony is essential; the simplicity of mathematical models enables the efficient resolution of different challenges. Our findings underscore the importance of incorporating spatial configuration when evaluating epidemiological models in disease ecology. Diversity in species susceptibilities, both among hosts and vectors, is key to the expression of the dilution effect. Host susceptibility diversity mitigates the impact of density on disease transmission, dampening the density-dependent effects on disease prevalence. These results suggest that disease control strategies should consider the composition and spatial organization of host and vector communities. Beyond specific vector-borne disease systems, our model can be applied to other ecological and epidemiological scenarios. The ability to assess the interaction between mobility, density, and susceptibility makes it useful for studying emerging diseases in fragmented landscapes, as well as for designing conservation strategies where biodiversity plays a role in mitigating zoonotic diseases. This approach offers valuable insights into the influence of landscape structure and organism mobility on infection prevalence, which could be relevant for designing control strategies in both agricultural and urban contexts. Furthermore, the model can be extended to evaluate other infectious diseases in different spatial contexts, such as virus transmission in wildlife communities or direct-contact disease spread in human populations. While this study provides key insights into the role of spatial configuration in the dilution effect, several avenues remain for future research. One important next step is to incorporate environmental heterogeneity, such as habitat fragmentation, to assess how landscape structure influences host-vector interactions. Additionally, extending the model to include seasonal variations in host and vector populations could provide a more realistic representation of disease dynamics. Further exploration of vector behavioral plasticity and host preference dynamics could refine predictions on disease spread. Integrating empirical data from field studies into agent-based models could improve validation and applicability across different ecological contexts. Finally, expanding this framework to study multi-host and multi-pathogen systems would help develop more comprehensive strategies for controlling infectious diseases in diverse ecological settings. Conclusion The model indicates that increased species diversity in host susceptibilities increases the dilution effect. The diversity in vector susceptibilities has an insignificant effect. It is suggested to prioritize the diversity of host susceptibilities over the vectors in the design of control policies. Movement patterns had little effect on the dilution effect, whereas movement distance significantly influenced disease prevalence. This suggests that spatial dynamics and host diversity should be carefully considered in disease management and conservation strategies. Our results indicate that the dilution effect mitigates density-dependent prevalence effects. The findings highlight the significance of movement distance in relation to disease prevalence. The change in prevalence, while modest, remains consistent until agents cover approximately 1% of the total surface area. This threshold may act as a reference for comprehending the impact of movement on epidemiological management. The results highlight the importance of incorporating agent mobility into epidemiological models and the design of disease control strategies. In conclusion, spatiality factors affect the prevalence of infectious diseases, notably through movement distance and density, which elevate contact rates. The dilution effect can reduce the increase in the prevalence of infectious diseases associated with density. Declarations Conflict of interest None declared. Funding: None Author Contribution FNR and FE conceived the ideas and designed methodology; FNR collected the data and analysed the data; GS and ORC led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication. Acknowledgement We are grateful to CONAHCyT and l’Ambassade de France au Mexique. Data Availability Access to the code used for analysis is provided at https://github.com/Nieto-Rabiela/ABM-Spatial-Configuration-Dilution-Effect.git. For further information on data availability, please contact the corresponding author. References Auchincloss, Amy H., Samson Y. Gebreab, Christina Mair, and Ana V. 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Calos. 2008. “Increased Avian Diversity Is Associated with Lower Incidence of Human West Nile Infection: Observation of the Dilution Effect.” PLoS ONE 3 (6). https://doi.org/10.1371/journal.pone.0002488. Watkins, a, J Noble, R J Foster, B J Harmsen, and C P Doncaster. 2014. “A Spatially Explicit Agent-Based Model of the Interactions between Jaguar Populations and Their Habitats.” Ecological Modelling . https://doi.org/10.1016/j.ecolmodel.2014.10.038. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6109476","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439307463,"identity":"9894955b-9fc8-4c36-a673-d6df417b30f4","order_by":0,"name":"Fabiola Nieto-Rabiela","email":"","orcid":"","institution":"Facultad de Medicina Veterinaria y Zootecnia, UNAM","correspondingAuthor":false,"prefix":"","firstName":"Fabiola","middleName":"","lastName":"Nieto-Rabiela","suffix":""},{"id":439307465,"identity":"8812268d-8671-4886-8cda-1e939b301673","order_by":1,"name":"Fernando Esponda","email":"","orcid":"","institution":"Instituto Tecnológico Autónomo de México (ITAM)","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"","lastName":"Esponda","suffix":""},{"id":439307467,"identity":"526cf7f0-fecb-4307-bc3e-924553a68104","order_by":2,"name":"Oscar Rico-Chávez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAl0lEQVRIiWNgGAWjYFACxgZmhgrStZwh1R5mxjZSlPNLH278XDjvcB7/tAOsm3mI0SLZl9gsPXPb4WKJ2wlsN2cQo8XgDGMbM++2w4kNQC03PhCvZc7hxPkgLQnEa2k4nLiBaFskexibpXmOpSduvJ3YRpxf+HnYH37mqbFOnHc7+dhtokIMCTA2kKhhFIyCUTAKRgFOAACSnTLNSOFE6AAAAABJRU5ErkJggg==","orcid":"","institution":"Facultad de Medicina Veterinaria y Zootecnia, UNAM","correspondingAuthor":true,"prefix":"","firstName":"Oscar","middleName":"","lastName":"Rico-Chávez","suffix":""},{"id":439307469,"identity":"f81a0e6e-2b7e-406b-9743-a68565df5bcd","order_by":3,"name":"Gerardo Suzán","email":"","orcid":"","institution":"Facultad de Medicina Veterinaria y Zootecnia, UNAM","correspondingAuthor":false,"prefix":"","firstName":"Gerardo","middleName":"","lastName":"Suzán","suffix":""}],"badges":[],"createdAt":"2025-02-26 03:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6109476/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6109476/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80557056,"identity":"af4c2126-c0f7-4c94-9417-5fa4ace1966d","added_by":"auto","created_at":"2025-04-14 15:53:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36747,"visible":true,"origin":"","legend":"\u003cp\u003eAgent-based model decision diagram.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6109476/v1/951117563fff34a5a3cd715f.png"},{"id":80558167,"identity":"799f240b-6fac-416d-ab4e-9b943bfbde72","added_by":"auto","created_at":"2025-04-14 16:09:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58754,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in prevalence when modifying the diversity of susceptibilities.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6109476/v1/a939e88b434571ea20ac9814.png"},{"id":80557061,"identity":"ab247cd1-f89f-4ca1-a63f-338a85c72956","added_by":"auto","created_at":"2025-04-14 15:53:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":162689,"visible":true,"origin":"","legend":"\u003cp\u003eMaximal prevalence in the random movement model without diversity testing density. The X-axis represents the distance (patches) moved through by the agents, while the Y-axis indicates the maximal prevalence. Each block evaluates the density specified at the top of the block.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6109476/v1/16be68c53d59128885d91a5f.png"},{"id":81651279,"identity":"b3897499-bd64-49e6-834f-d8ba8abeb072","added_by":"auto","created_at":"2025-04-29 16:16:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":872643,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6109476/v1/cbb25745-71f2-4429-b8fa-61a9054af0ce.pdf"},{"id":80557924,"identity":"0d8c7b2d-c7a8-4891-ba49-12eef76ff290","added_by":"auto","created_at":"2025-04-14 16:01:02","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1501524,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6109476/v1/73b428d50d04c91827d78fa6.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial configuration and the Dilution Effect in Vector-Borne Diseases: Insights from Agent-Based Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe dilution effect has been studied across a wide variety of ecological and epidemiological contexts, particularly in systems involving vector-borne diseases such as Lyme disease, West Nile Virus, and amphibian chytridiomycosis (Ostfeld and Keesing 2000; Searle et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Swaddle and Stavros 2008). Dobson (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) highlighted the intricate population dynamics of pathogens in systems with multiple host species, emphasizing how variations in host community structure can dramatically influence disease transmission. Incorporating such theoretical insights into applied models is essential to better understand the mechanisms driving the dilution effect.\u003c/p\u003e \u003cp\u003eThese studies underscore that species diversity reduces disease prevalence by altering host-vector interactions. For instance, Ostfeld and Keesing (2000) demonstrated that increased host diversity disrupts transmission pathways by diluting the pool of competent hosts. Similarly, Swaddle and Calos (2008) observed that in avian communities, higher species richness decreased the prevalence of WNV (West Nile Virus) by limiting vector access to highly competent hosts. Regarding Lyme disease, research has shown that species such as the Virginia opossum reduce tick survival rates by grooming, thereby lowering disease transmission (Ostfeld and Keesing 2012). For WNV, avian diversity acts as a buffer, reducing human exposure by diluting the proportion of highly competent reservoirs (Swaddle and Calos 2008).\u003c/p\u003e \u003cp\u003eIn this study, hosts are categorized by their species-specific susceptibilities, while vectors are modeled as generalist organisms capable of interacting with multiple host types. By distinguishing these ecological roles, we seek to capture the mechanisms driving the dilution effect in natural settings.\u003c/p\u003e \u003cp\u003eDespite this growing body of evidence, few studies have explored how spatial configuration and movement patterns influence these dynamics. Our research seeks to fill this gap by employing Agent-Based Models (ABMs) to examine how spatial arrangement and host susceptibility diversity shape the dilution effect in vector-borne diseases. Biodiversity not only provides numerous ecosystem services (Perrings et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) but also contributes to health protection by mitigating infection risks. The dilution effect is driven by three main mechanisms: generalist vector behavior, diversity in host susceptibility, and interspecies interactions (Ostfeld and Keesing 2000). By integrating spatial variables into ABMs, we aim to extend these foundational concepts and provide novel insights into the spatial ecology of infectious diseases.\u003c/p\u003e \u003cp\u003eThe dilution effect is triggered by three key mechanisms. Firstly, vectors must exhibit generalist behavior devoid of strong food preferences. Second, the host community should be diverse in terms of species susceptibility. Lastly, interaction between species within the host community is essential (Ostfeld and Keesing 2000).\u003c/p\u003e \u003cp\u003eThe dilution effect has been investigated across various natural contexts, such as Lyme disease, Batrachochytrium dendrobatids, and West Nile Virus (Ostfeld and Keesing 2000; Searle et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Swaddle and Calos 2008; Civitello et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To fully understand how species diversity affects the spread of disease, we need to look at natural examples as well as understand how complex interactions work on a theoretical level. We then need to test these ideas in the real world to fully grasp how they work.\u003c/p\u003e \u003cp\u003eThe dilution effect was supported inside a mathematical model by earlier research (Roberts and Heesterbeek \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Occhibove et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Collins, Bever, and Hersh \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Miller and Huppert 2013). Most notably, Roche et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, who reported a decrease in disease prevalence with increased diversity in a SIR mathematical model. They highlight that is not diversity alone that facilitates the expression of the dilution effect, but instead the diversity in susceptibilities of host and vector communities (the likelihood or potential of contracting a disease). By incorporating spatial configuration into an Agent-Based Model (ABM), we hope to discover whether the dilution effect remains true without changing vector and host abundances.\u003c/p\u003e \u003cp\u003eWe built a computational model aimed at explaining the dilution effect process. The ABM can simulate the interactions between agents in space, like vectors and hosts, on a per-sample level, giving you control over the types of movements and the distances between them. (Heppenstall et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Watkins et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Peck \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Density, defined as the quantity of agents present in a specific area, plays a critical role in the manifestation of the dilution effect. In ecological and epidemiological contexts, higher population densities typically lead to increased interactions among individuals, which can enhance disease transmission rates. Conversely, lower densities may reduce interaction rates and limit the spread of diseases. This concept is fundamental for understanding and modeling the dynamics of populations and the spread of infectious diseases. Manipulating density in ABMs can impact the probability of contact between vectors and hosts, which is essential for understanding the dilution process. Crucially, no research has explored the impact of spatial arrangement on the dilution effect.\u003c/p\u003e \u003cp\u003eWhile exposure to infectious diseases can influence disease prevalence (Organizaci\u0026oacute;n Panamericana de la Salud, 2011), the majority of studies employing spatial analysis have focused on epidemiological research, frequently utilizing geolocation and Geographic Information Systems (GIS) (Auchincloss et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Yet, the impact of movement patterns on vector-borne diseases, whether in wildlife or isolated environments, has remained largely unexplored. As such, we assess the effects of three movement patterns.\u003c/p\u003e \u003cp\u003eOur study provides a novel perspective on the dilution effect by integrating spatial configurations into disease dynamics models. Incorporating movement patterns and susceptibility diversity allows a more precise assessment of how host distribution impacts disease spread. These findings contribute to the theoretical framework of emerging infectious disease epidemiology, suggesting that density and spatial movement are key factors in the manifestation of the dilution effect. Furthermore, our model provides evidence that variability in host susceptibilities modulates infection dynamics, emphasizing the importance of heterogeneity in disease ecology.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAims\u003c/strong\u003e \u003cp\u003eThe main objective of this research is to assess how variations in susceptibility among vector and host species, along with spatial configuration, influence the prevalence of infectious diseases within an agent-based model framework.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eModel description.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eODD Protocol (Grimm et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePurpose\u003c/strong\u003e \u003cp\u003eThe primary aim of our model is to investigate the effect of variations in host and vector susceptibility diversity, in conjunction with spatial configuration and movement patterns, on the prevalence of vector-borne diseases using agent-based modeling (ABM). By understanding these factors, we hope to shed light on the mechanisms driving disease prevalence and the potential for the dilution effect.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIt is important to note that the model deliberately abstracts away detailed ecological or physiological traits of host and vector species. This approach ensures that the analysis focuses exclusively on the effects of spatial movement patterns and density on disease dynamics, avoiding potential confounding effects from species-specific traits. The model simplifyies the system to general host-vector interactions, isolating the influence of spatial parameters and clarifying their role in the dilution effect.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEntities, State Variables, and Scales\u003c/b\u003e: Our model includes two types of agents: hosts and vectors. Hosts and vectors can be either susceptible or infected. The state variables of each agent include their current health status, position, and movement pattern. The spatial environment is represented as a grid consisting of 33 by 33 patches. The temporal scale is established at daily intervals.\u003c/p\u003e \u003cp\u003eBetween the models, agents (hosts and vectors) were differentiated by their probability of becoming infected. Each host species is defined by a unique susceptibility parameter, ranging from 0.2 to 1, which is assigned at initialization. In the absence of diversity, we assign a susceptibility of 1 to all hosts. The diversity of species differs only in their infection probability. This approach allowed us to observe how susceptibility diversity impacted disease prevalence.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProcess Overview and Scheduling\u003c/strong\u003e \u003cp\u003eAgents exhibit movement patterns daily, categorized as linear, random, or circular. Interactions among individuals can facilitate the transmission of diseases. New vectors are generated at consistent intervals. The daily schedule facilitates dynamic interactions among agents (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDesign Concepts\u003c/strong\u003e \u003cp\u003eThe model integrates elements of spatial ecology and is based on SIR (Susceptible-Infected-Recovered) epidemiological principles. The dilution effect and disease prevalence result from interactions among agents. Agents adhere to established behaviors and lack the capacity to adapt, learn, or anticipate future states. They lack explicit goals as a driving force. Agents perceive their immediate surroundings, identifying proximate hosts or vectors. Disease transmission transpires when vectors and hosts inhabit the same or directly adjacent patches. Movement patterns and disease transmission are stochastic, with no collective behaviors; all interactions are individual-based. The model tracks disease prevalence, agent states, and spatial distributions.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInitialization\u003c/strong\u003e \u003cp\u003eThe model initiates with a predetermined quantity of hosts and vectors, randomly distributed across the grid. Predefined probabilities establish initial infection states, ensuring variability in the initial conditions of each simulation run. The values are manifested in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eInitial parameters of each simulation.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eModel input parameters\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommunity species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCommunity vectors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial hosts infected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial vectors infected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of host species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of vector species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProbability to be infected (host susceptibility)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProbability to be infected (vector susceptibility)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInput Data\u003c/strong\u003e \u003cp\u003eThe model sets all parameters and does not use any external data. This approach allows for controlled experimentation and analysis of various hypothetical scenarios.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eWhen setting the model parameters, we followed approaches outlined in previous studies using simple epidemiological models, such as SIR models. The transmission, reproduction, and mortality rates of vectors were based on works like Roche et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and other recent studies in disease dynamics (Chen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)( Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ). These parameters were established to ensure that the model's results were comparable with previous studies. The parameters also reflecting the inherent complexity of susceptibility diversity and spatial interactions between hosts and vectors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameter justification\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValues Used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJustification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHost and Vector Infection Probability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHosts: 0.2\u0026ndash;1 (with diversity) / 1 (without diversity)\u003c/p\u003e \u003cp\u003eVectors: 0.2\u0026ndash;1 (with diversity) / 1 (without diversity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHosts exhibit variability in susceptibility due to immune response and host-pathogen interactions.\u003c/p\u003e \u003cp\u003eWith diversity, susceptibility varies (e.g., opossums reduce Lyme transmission).\u003c/p\u003e \u003cp\u003eWithout diversity, all hosts have maximum susceptibility.\u003c/p\u003e \u003cp\u003eVector susceptibility varies but less pronounced.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOstfeld \u0026amp; Keesing (2000)\u003c/p\u003e \u003cp\u003eRoche et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eHuang et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial Number of Infected Hosts and Vectors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfected vectors: 300 (host community models) / 100 (vector community models)\u003c/p\u003e \u003cp\u003eInfected hosts: 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh initial infected vectors ensure transmission without extreme stochastic events.\u003c/p\u003e \u003cp\u003eZero initial infected hosts simulate an emerging outbreak scenario.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRoche et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Host and Vector Species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHosts: 10 (with diversity) / 1 (without diversity)\u003c/p\u003e \u003cp\u003eVectors: 10 (with diversity) / 1 (without diversity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher diversity reduces disease prevalence (dilution effect).\u003c/p\u003e \u003cp\u003eLow diversity can amplify disease transmission.\u003c/p\u003e \u003cp\u003eVector diversity effects depend on species composition.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCivitello et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eSwaddle \u0026amp; Calos (2008)\u003c/p\u003e \u003cp\u003eOcchibove et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgent Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAffects contact rates and transmission.\u003c/p\u003e \u003cp\u003eLow density: fewer contacts, lower transmission.\u003c/p\u003e \u003cp\u003eHigh density: increased encounters, potential outbreaks.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDobson (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eMiller \u0026amp; Huppert (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpatial Grid Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 \u0026Atilde;\u0026mdash; 33 patches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAllows effective spatial interactions while maintaining computational efficiency.\u003c/p\u003e \u003cp\u003eUses toroidal environment to prevent edge effects.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWilensky (1997)\u003c/p\u003e \u003cp\u003eHeppenstall et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMovement Types and Distances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTypes: Linear, random, circular\u003c/p\u003e \u003cp\u003eDistances: 1, 5, 10, 20, 30, 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrganism mobility influences pathogen spread.\u003c/p\u003e \u003cp\u003eSimulates local (1\u0026ndash;5 patches) and broad dispersal (30\u0026ndash;50 patches).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeck (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVector Mortality and Reproduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMortality: 15\u0026ndash;30 days\u003c/p\u003e \u003cp\u003eReproduction: 5 vectors every 30 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVector lifespan based on studies (e.g., malaria, dengue).\u003c/p\u003e \u003cp\u003eReproduction rate ensures stable population in simulations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRoche et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSubmodels\u003c/strong\u003e \u003cp\u003eThe model includes submodels for movement patterns (linear, random, and circular), density, and susceptibility diversity. Each submodel adds a layer of complexity, allowing us to explore the effects of different variables on disease dynamics.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAgents and Environments\u003c/strong\u003e \u003cp\u003eIn our model, the environment is a grid in which each patch can host multiple agents. Hosts and vectors are the agents, each with state variables such as health status (susceptible or infected) and movement capabilities.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSimulation Procedures\u003c/strong\u003e \u003cp\u003eThe simulation runs until no susceptible agents remain, with each day representing a time step. During each step, agents move according to their assigned pattern (linear, random, or circular). Hosts and vectors interact, leading to potential disease transmission if an infected vector encounters a susceptible host.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMovement Patterns\u003c/b\u003e: We investigated three distinct movement patterns: linear, random, and circular. Linear movement is when agents move straight ahead, serving as a control model. Random movement, derived from the wolf-sheep predation model in NetLogo (Wilensky, 1997), is more realistic and accounts for organisms' objectives in their movements. The circular movement represents a macro-scale home range in ecology. Each movement pattern was evaluated for its impact on disease prevalence and the dilution effect.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDensity Manipulation\u003c/strong\u003e \u003cp\u003eDensity manipulation involves varying densities through the adjustment of agent numbers within a given spatial area. Simulations were conducted at densities of 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, and 2 to investigate density-dependent effects. These values were chosen to explore a wide range of densities, representing different ecological scenarios from low to high host aggregation. The density was determined by dividing the total number of hosts by the total number of grids. The variations enabled observation of the impact of agent density on disease dynamics.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSpatial Configuration\u003c/b\u003e: Spatial configuration modifications were implemented, including the type of movement and the distance covered by agents. Three distinct movement types were tested to simulate different ecological scenarios: linear, random, and circular. The distances traversed ranged from \u0026minus;\u0026thinsp;16 to 16, corresponding with a 33x33 grid in NetLogo. NetLogo operates within a toroidal environment, allowing agents that reach the grid\u0026acute;s edge to reappears on the opposite side. The evaluated movement distances were 1, 5, 10, 20, 30, and 50 patches, which corresponded to surface area percentages crossed by agents of 0.09%, 0.45%, 0.91%, 1.83%, 2.75%, and 4.59%, respectively. These distances represent different scales of host movement, from local interactions to broad dispersal across the landscape.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEvaluation\u003c/strong\u003e \u003cp\u003eThe evaluation involved executing each of the four models using combinations of three movement forms, six distances, and eight densities, resulting in 392 model variations. Each model was executed 1000 times for every ABM configuration, with a maximum duration of 100 days. The variable of interest, the prevalence of infected agents, was calculated daily by dividing the total number of infected agents by the total number of agents and multiplying the result by 100. Within each model run, we extracted the maximum prevalence value over the 100 days to analyse.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eModel Environment\u003c/strong\u003e \u003cp\u003eUsing NetLogo 6.1.1., we evaluated four distinct ABM models. Each model comprised 1000 hosts and 300 vectors, with agents categorized as either susceptible or infected (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Infection probabilities differed according to community susceptibilities.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe vector mortality rate (15\u0026ndash;30 days) and reproduction (5 vectors per 30 days) were based on Roche et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), ensuring comparability with prior studies on vector-borne diseases.\u003c/p\u003e \u003cp\u003eValues are obtained from the SIR model as outlined by Roche et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The initial percentage of infected vectors was set to 100% in the host models to minimize variability related with vector lifespan and reproduction. In the vector models, one-third of the population was initially infected, influenced by variables related to lifespan and reproduction variables. An evaluation of the maximum prevalence behavior concerning changes in agents\u0026rsquo; susceptibility was conducted (Supplementary material).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe analysis evaluated the impact of susceptibility diversity by calculating the 1000 maximum prevalence results for the ABM species alongside the ABM community and the ABM vector in conjunction with the ABM community vector, identifying significant differences. Because of the non-normal distribution and diverse variances, we used the Wilcoxon Mann-Whitney test to compare the populations (Fay and Proschan \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). There were notable variations in both comparisons, with the statistical U values of 18.38 and 5.1537, respectively. Both U values ​​have a p-value of \u0026lt;\u0026thinsp;2.2\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e. This suggests that an increase in the U value corresponds to stronger evidence against the equality of medians between the two samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The increased diversity in agent susceptibility led to a decrease in prevalence in both cases. The distances of host movement were approximately three times greater than those of vectors, consistent with the mathematical model proposed by Roche et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This suggests that the two models can be compared.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpatial\u003c/p\u003e \u003cp\u003eIn each model, incorporating the diversity of susceptibilities results in a decrease in prevalence; however, the differences among types of movement (circular, linear, random) are minimal (Supplementary material).\u003c/p\u003e \u003cp\u003eDensity\u003c/p\u003e \u003cp\u003eThe density tests concentrated on four models employing random movement, as no significant differences were observed among the movement types, with ten distinct movement distances ranging from 1 to 10 patches. No significant changes in maximal prevalence were observed across most models, excep for the scenario where susceptibility diversity was absent (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eFigure 3. Maximal prevalence in the random movement model without diversity testing density. The X-axis represents the distance (patches) moved through by the agents, while the Y-axis indicates the maximal prevalence. Each block evaluates the density specified at the top of the block.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study represents an initial evaluation of the impact of spatial configuration on the dilution effect. Our findings suggest that while the type of movement does not appear to significantly affect the dilution effect, however, the distance moved by agents does affect prevalence.\u003c/p\u003e \u003cp\u003eOur results reinforce and expand upon previous studies on the dilution effect. Mathematical models such as those by Roche et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Roberts and Heesterbeek (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) have demonstrated that species diversity can reduce disease prevalence through competition mechanisms among hosts. However, these models have traditionally assumed homogeneous spatial distributions and have not accounted for vector and host mobility. In contrast, our agent-based model (ABM) reveals that host movement distance can be a determining factor in disease prevalence, even in the absence of changes in vector or host abundance.\u003c/p\u003e \u003cp\u003eResearch conducted by Chen et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) suggests that spatial configuration affects transmission dynamics; however, our results highlight that landscape structure and agent mobility can either mitigate or enhance the dilution effect depending on the scale at which they occur. Furthermore, while differential equation-based models (Huang et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) have shown that connectivity between patches affects disease transmission, our agent-based model enables the assesment of the impact of realistic and heterogeneous movement patterns on transmission.\u003c/p\u003e \u003cp\u003eVarious grid measures were not evaluated, as the transmission of vector-borne diseases relies on contact, with density serving as a significant factor influencing contact probability, independent of surface area.\u003c/p\u003e \u003cp\u003eModifying variables like the number of daily bites will only affect the time to reach maximum prevalence without significantly changing the prevalence values. Variables such as mortality, the capacity of host reinfection, and vector reproduction have the potential to significantly alter transmission dynamics.\u003c/p\u003e \u003cp\u003eOur model confirms that increased species diversity and host community susceptibilities facilitates the effective operation of the dilution effect. That is consistent with the findings of Chen et al., (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which indicate that a decrease in the abundance of competent hosts correlate with a reduction in disease prevalence. This occurs naturally as the susceptibilities of the host community diversify.\u003c/p\u003e \u003cp\u003eDifferences in vector susceptibilities have a negligible impact on disease prevalence and the dilution effect. This result is elucidated by the observations of Occhibove et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) who indicate that the interspecific dynamics of communities, due to their complexity, exhibit varying degrees of the dilution effect contingent upon the generalism of the vector. Consequently, it is advisable to prioritize the diversity of host susceptibilities over the vectors when implementing control policies.\u003c/p\u003e \u003cp\u003eFurthermore, our findings are consistent with earlier research (Roche et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) indicating that the vector abundance increase prevalence. Constant vector abundances do not yield equivalent amplification effects. It is essential to exercise caution when drawing broad generalizations regarding movement effects, given specific assumptions inherent in our model. It presumes a high transmission rate and a limited surface area. Factors influencing vector movement, such as food preferences, are excluded. Additional research with modified variables is essential for improving understanding of the influence of movement patterns on the dilution effect.\u003c/p\u003e \u003cp\u003eOur study indicates that movement distance significantly influences prevalence. The change in prevalence is modest, yet remains consistent until agents cover approximately 1% of the total surface area. This threshold may provide a significant for comprehending the impact of movement on epidemiological management strategies.\u003c/p\u003e \u003cp\u003eThe ABM model utilized three movement patterns (linear, circular, and random) to represent host and vector behaviors, considering the complexity of actual movements. These patterns facilitate the abstraction of a variety ecological scenarios and provide a basis for comparison and validation with other studies. Integrating more complex movement patterns could increase computational complexity without providing benefits to the outcomes. This lays the foundation with fundamental movement types, facilitating subsequent research that integrates greater complexity.\u003c/p\u003e \u003cp\u003eThe diversity of susceptibilities mitigates the impact of density on prevalence. Increasing density significantly increases prevalence in scenarios devoid of susceptibility diversity. This suggests that variations in susceptibility and the manifestation of the dilution effect may provide protection or mitigate the prevalence of infectious diseases as population density increases. This suggests that the diversity of susceptibilities and the expression of the dilution effect may provide protection or mitigate the prevalence of infectious diseases as population density increases. Furthermore, the study conducted by (Roberts and Heesterbeek \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), is relevant as it investigated density in a similar context. The findings suggest an increase in system complexity; however, further investigation is required. This presents an intriguing area that warrants further exploration, both in practical application and theoretical modeling.\u003c/p\u003e \u003cp\u003eThe spatial movement in our model illustrates the benefits of computational models: accurate representation of agent behavior. It is essential to exercise caution and thoroughly understand each modified parameter's implications. Parsimony is essential; the simplicity of mathematical models enables the efficient resolution of different challenges.\u003c/p\u003e \u003cp\u003eOur findings underscore the importance of incorporating spatial configuration when evaluating epidemiological models in disease ecology. Diversity in species susceptibilities, both among hosts and vectors, is key to the expression of the dilution effect. Host susceptibility diversity mitigates the impact of density on disease transmission, dampening the density-dependent effects on disease prevalence. These results suggest that disease control strategies should consider the composition and spatial organization of host and vector communities.\u003c/p\u003e \u003cp\u003eBeyond specific vector-borne disease systems, our model can be applied to other ecological and epidemiological scenarios. The ability to assess the interaction between mobility, density, and susceptibility makes it useful for studying emerging diseases in fragmented landscapes, as well as for designing conservation strategies where biodiversity plays a role in mitigating zoonotic diseases.\u003c/p\u003e \u003cp\u003eThis approach offers valuable insights into the influence of landscape structure and organism mobility on infection prevalence, which could be relevant for designing control strategies in both agricultural and urban contexts. Furthermore, the model can be extended to evaluate other infectious diseases in different spatial contexts, such as virus transmission in wildlife communities or direct-contact disease spread in human populations.\u003c/p\u003e \u003cp\u003eWhile this study provides key insights into the role of spatial configuration in the dilution effect, several avenues remain for future research. One important next step is to incorporate environmental heterogeneity, such as habitat fragmentation, to assess how landscape structure influences host-vector interactions. Additionally, extending the model to include seasonal variations in host and vector populations could provide a more realistic representation of disease dynamics.\u003c/p\u003e \u003cp\u003eFurther exploration of vector behavioral plasticity and host preference dynamics could refine predictions on disease spread. Integrating empirical data from field studies into agent-based models could improve validation and applicability across different ecological contexts. Finally, expanding this framework to study multi-host and multi-pathogen systems would help develop more comprehensive strategies for controlling infectious diseases in diverse ecological settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe model indicates that increased species diversity in host susceptibilities increases the dilution effect. The diversity in vector susceptibilities has an insignificant effect. It is suggested to prioritize the diversity of host susceptibilities over the vectors in the design of control policies. Movement patterns had little effect on the dilution effect, whereas movement distance significantly influenced disease prevalence. This suggests that spatial dynamics and host diversity should be carefully considered in disease management and conservation strategies. Our results indicate that the dilution effect mitigates density-dependent prevalence effects.\u003c/p\u003e \u003cp\u003eThe findings highlight the significance of movement distance in relation to disease prevalence. The change in prevalence, while modest, remains consistent until agents cover approximately 1% of the total surface area. This threshold may act as a reference for comprehending the impact of movement on epidemiological management. The results highlight the importance of incorporating agent mobility into epidemiological models and the design of disease control strategies. In conclusion, spatiality factors affect the prevalence of infectious diseases, notably through movement distance and density, which elevate contact rates. The dilution effect can reduce the increase in the prevalence of infectious diseases associated with density.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eNone declared.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFNR and FE conceived the ideas and designed methodology; FNR collected the data and analysed the data; GS and ORC led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful to CONAHCyT and l\u0026rsquo;Ambassade de France au Mexique.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAccess to the code used for analysis is provided at https://github.com/Nieto-Rabiela/ABM-Spatial-Configuration-Dilution-Effect.git. For further information on data availability, please contact the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAuchincloss, Amy H., Samson Y. Gebreab, Christina Mair, and Ana V. 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Heesterbeek. 2018. \u0026ldquo;Quantifying the Dilution Effect for Models in Ecological Epidemiology.\u0026rdquo; \u003cem\u003eJournal of the Royal Society Interface\u003c/em\u003e 15 (140). https://doi.org/10.1098/rsif.2017.0791.\u003c/li\u003e\n\u003cli\u003eRoche, Benjamin, Pejman Rohani, Andy P. Dobson, and Jean-Fran\u0026ccedil;ois Gu\u0026eacute;gan. 2013. \u0026ldquo;The Impact of Community Organization on Vector-Borne Pathogens.\u0026rdquo; \u003cem\u003eThe American Naturalist\u003c/em\u003e 181 (1): 1\u0026ndash;11. https://doi.org/10.1086/668591.\u003c/li\u003e\n\u003cli\u003eSearle, Catherine L, Lindsay M Biga, Joseph W Spatafora, and Andrew R Blaustein. 2011. \u0026ldquo;A Dilution Effect in the Emerging Amphibian Pathogen Batrachochytrium Dendrobatidis.\u0026rdquo; \u003cem\u003ePNAS\u003c/em\u003e 108 (39): 16322\u0026ndash;26. https://doi.org/10.1073/pnas.1108490108.\u003c/li\u003e\n\u003cli\u003eSwaddle, John P., and Stavros E. Calos. 2008. \u0026ldquo;Increased Avian Diversity Is Associated with Lower Incidence of Human West Nile Infection: Observation of the Dilution Effect.\u0026rdquo; \u003cem\u003ePLoS ONE\u003c/em\u003e 3 (6). https://doi.org/10.1371/journal.pone.0002488.\u003c/li\u003e\n\u003cli\u003eWatkins, a, J Noble, R J Foster, B J Harmsen, and C P Doncaster. 2014. \u0026ldquo;A Spatially Explicit Agent-Based Model of the Interactions between Jaguar Populations and Their Habitats.\u0026rdquo; \u003cem\u003eEcological Modelling\u003c/em\u003e. https://doi.org/10.1016/j.ecolmodel.2014.10.038.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Agent-Based models, Dilution Effect, Spatial Configuration, Vector-Borne Diseases","lastPublishedDoi":"10.21203/rs.3.rs-6109476/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6109476/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiodiversity provides ecosystem services, including health protection by reducing infection risks through the \"dilution effect.\" This mechanism occurs when vertebrate diversity decreases disease prevalence in host populations.\u003c/p\u003e \u003cp\u003ePrior SIR (Susceptible-Infected-Recovery) models have explored the dilution effect, but incorporating spatial configuration remains a challenge. We simulated the distances that agents (vectors and hosts) could travel under different movement types. We assessed maximum prevalence ((infected agents/total population) \u0026times; 100) in each simulation using four Agent-Based Models (ABMs) implemented in NetLogo. These models tested scenarios with and without susceptibility diversity, combined with different agent densities (0.25 to 2) and movement distances.\u003c/p\u003e \u003cp\u003eOur findings indicate that movement type has minimal relevance to the dilution effect. However, movement distance significantly influences disease prevalence. Notably, the dilution effect counteracts density-dependent effects on infectious disease prevalence. This study highlights the importance of susceptibility diversity in host communities for the effectiveness of the dilution effect, while vector susceptibility diversity plays a minor role.\u003c/p\u003e \u003cp\u003eWe identified movement and distance as key parameters influencing the dilution effect, which had not been analyzed before. Future studies should consider movement distance when evaluating the impact of spatial configuration on disease transmission. Our results provide new insights into the complex dynamics of infectious disease prevalence across spatial configurations and ecological settings.\u003c/p\u003e","manuscriptTitle":"Spatial configuration and the Dilution Effect in Vector-Borne Diseases: Insights from Agent-Based Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-14 15:52:58","doi":"10.21203/rs.3.rs-6109476/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1ca91a1f-c593-4cc6-8257-e32b57d8d5ca","owner":[],"postedDate":"April 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-29T16:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-14 15:52:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6109476","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6109476","identity":"rs-6109476","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}