Graph-based epidemic modeling of West Nile Virus: Forecasting and containment

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We developed a modular computational framework that simulates and analyzes WNV transmission dynamics through compartmental models capturing the intricate ecological interactions among avian hosts, mosquito vectors, and human populations. Our system integrates epidemiological parameters with customizable intervention mechanisms, facilitating the assessment of scenario-specific mitigation approaches. Distinguishing itself from conventional static models, this framework enables users to model dynamic, time-sensitive interventions including targeted mosquito control and strategic bird population management—the two principal containment strategies currently employed against WNV. Using simulations that reflect realistic outbreak scenarios, we evaluated how varying intervention intensities and implementation timings affect epi- demic progression. Our findings reveal that early implemented, dual-target strategies addressing both vector populations and avian reservoirs can substantially reduce transmission dynamics and minimize human exposure risk. This framework serves as a comprehensive decision-support platform for policymakers and vector control agencies, delivering mechanistic insights into the effectiveness of non-pharmaceutical interventions against zoonotic pathogens within complex ecological systems. The tool’s modular design and scenario-testing capabilities make it particularly valuable for proactive outbreak preparedness and evidence-based intervention planning." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-902/v2", "name": "Graph-based epidemic modeling of West Nile Virus: Forecasting and..." } } ] } Home Browse Graph-based epidemic modeling of West Nile Virus: Forecasting and... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Branda F, Ahmed MM, Defilippo A et al. Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.12688/f1000research.169601.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Method Article Revised Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] Francesco Branda https://orcid.org/0000-0002-9485-3877 1,2 , Mohamed Mustaf Ahmed https://orcid.org/0009-0006-5991-4052 3 , Annamaria Defilippo 4 , [...] Ugo Lomoio https://orcid.org/0000-0001-8150-0039 4 , Barbara Puccio 4 , Massimo Ciccozzi 1,2 , Fabio Scarpa 2,5 , Pierangelo Veltri 6 , Pietro Hiram Guzzi https://orcid.org/0000-0001-5542-2997 4 Francesco Branda https://orcid.org/0000-0002-9485-3877 1,2 , Mohamed Mustaf Ahmed https://orcid.org/0009-0006-5991-4052 3 , [...] Annamaria Defilippo 4 , Ugo Lomoio https://orcid.org/0000-0001-8150-0039 4 , Barbara Puccio 4 , Massimo Ciccozzi 1,2 , Fabio Scarpa 2,5 , Pierangelo Veltri 6 , Pietro Hiram Guzzi https://orcid.org/0000-0001-5542-2997 4 PUBLISHED 14 Nov 2025 Author details Author details 1 Unit of Medical Statistics and Molecular Epidemiology,, University Campus Bio-Medico of Rome, Rome, Italy 2 Genomics, AI, Bioinformatics, Infectious Diseases, Epidemiology Group (GABIE), Rome, Italy 3 Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Banaadir, Somalia 4 Department of Surgical and Medical Sciences,, Magna Graecia University of Catanzaro, Catanzaro, Italy 5 Department of Biomedical Sciences, University of Sassari, Sassari, Italy 6 Department of Computer, Modeling, Electronics and System Engineering, University of Calabria, Rende, Italy Francesco Branda Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Mohamed Mustaf Ahmed Roles: Writing – Review & Editing Annamaria Defilippo Roles: Writing – Original Draft Preparation, Writing – Review & Editing Ugo Lomoio Roles: Writing – Original Draft Preparation, Writing – Review & Editing Barbara Puccio Roles: Writing – Original Draft Preparation, Writing – Review & Editing Massimo Ciccozzi Roles: Writing – Original Draft Preparation, Writing – Review & Editing Fabio Scarpa Roles: Writing – Original Draft Preparation, Writing – Review & Editing Pierangelo Veltri Roles: Writing – Original Draft Preparation, Writing – Review & Editing Pietro Hiram Guzzi Roles: Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Global Public Health gateway. This article is included in the Emerging Diseases and Outbreaks gateway. This article is included in the Pathogens gateway. Abstract The increasing prevalence of vector-borne diseases like West Nile virus (WNV) highlights the critical need for predictive modeling tools that can guide public health decision-making, particularly given the absence of effective vaccines. We developed a modular computational framework that simulates and analyzes WNV transmission dynamics through compartmental models capturing the intricate ecological interactions among avian hosts, mosquito vectors, and human populations. Our system integrates epidemiological parameters with customizable intervention mechanisms, facilitating the assessment of scenario-specific mitigation approaches. Distinguishing itself from conventional static models, this framework enables users to model dynamic, time-sensitive interventions including targeted mosquito control and strategic bird population management—the two principal containment strategies currently employed against WNV. Using simulations that reflect realistic outbreak scenarios, we evaluated how varying intervention intensities and implementation timings affect epi- demic progression. Our findings reveal that early implemented, dual-target strategies addressing both vector populations and avian reservoirs can substantially reduce transmission dynamics and minimize human exposure risk. This framework serves as a comprehensive decision-support platform for policymakers and vector control agencies, delivering mechanistic insights into the effectiveness of non-pharmaceutical interventions against zoonotic pathogens within complex ecological systems. The tool’s modular design and scenario-testing capabilities make it particularly valuable for proactive outbreak preparedness and evidence-based intervention planning. READ ALL READ LESS Keywords West Nile Virus, Vector-borne diseases, Transmission dynamics, Decision-support platform, Compartmental models, Ecological interactions Corresponding Author(s) Mohamed Mustaf Ahmed ( [email protected] ) Close Corresponding author: Mohamed Mustaf Ahmed Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Branda F et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Branda F, Ahmed MM, Defilippo A et al. Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.12688/f1000research.169601.2 ) First published: 10 Sep 2025, 14 :902 ( https://doi.org/10.12688/f1000research.169601.1 ) Latest published: 19 Dec 2025, 14 :902 ( https://doi.org/10.12688/f1000research.169601.3 ) Revised Amendments from Version 1 We substantially expanded the Methods to present the full mathematical equations for the multi-host SEIRD model, clarified all state variables, and explained that simulations use a discrete-event scheme with adaptive step size. We added a transparent parameterization section with a consolidated table of key epidemiological rates and probabilities drawn from the established literature, and we clarified that Italian WNV surveillance bulletins were used only for contextual, qualitative validation, not for model fitting. We also made explicit how interventions are represented in the network: explainability-guided removal of human–mosquito or bird–mosquito transmission edges as stylized analogues of exposure-reduction measures and reservoir–vector control, respectively. A new Related Work section situates our framework among compartmental and graph-neural-network approaches and highlights the novelty of coupling mechanistic dynamics with explainable GNNs. Figures were rebuilt for legibility (consistent palette, clearer labels), captions streamlined, and citation formatting standardized. We improved readability across the manuscript by shortening complex sentences and clarifying structure. Finally, we explicitly note limitations (e.g., single bird/vector species; no formal sensitivity analysis in this proof-of-concept) and outline how the modular platform enables users to run richer, locally parameterized studies and explore nuanced intervention scenarios in future work. We substantially expanded the Methods to present the full mathematical equations for the multi-host SEIRD model, clarified all state variables, and explained that simulations use a discrete-event scheme with adaptive step size. We added a transparent parameterization section with a consolidated table of key epidemiological rates and probabilities drawn from the established literature, and we clarified that Italian WNV surveillance bulletins were used only for contextual, qualitative validation, not for model fitting. We also made explicit how interventions are represented in the network: explainability-guided removal of human–mosquito or bird–mosquito transmission edges as stylized analogues of exposure-reduction measures and reservoir–vector control, respectively. A new Related Work section situates our framework among compartmental and graph-neural-network approaches and highlights the novelty of coupling mechanistic dynamics with explainable GNNs. Figures were rebuilt for legibility (consistent palette, clearer labels), captions streamlined, and citation formatting standardized. We improved readability across the manuscript by shortening complex sentences and clarifying structure. Finally, we explicitly note limitations (e.g., single bird/vector species; no formal sensitivity analysis in this proof-of-concept) and outline how the modular platform enables users to run richer, locally parameterized studies and explore nuanced intervention scenarios in future work. See the authors' detailed response to the review by Katrin Gaardbo Kuhn and Gargi Deshpande See the authors' detailed response to the review by Laura Antonelli See the authors' detailed response to the review by Michael Wimberly READ REVIEWER RESPONSES  There is a newer version of this article available. Suppress this message for one day. Introduction The experience of the COVID-19 pandemic has underscored the necessity of complex, adaptive strategies in public health to effectively manage the spread of infectious diseases. 1 While COVID-19 triggered global attention, similar computational approaches are equally critical for tackling emerging vector-borne diseases such as West Nile Virus (WNV), whose patterns of transmission and intervention requirements differ substantially from classical airborne viruses. 2 In this complex landscape, computational models grounded in mathematical epidemiology and data science 3 offer vital tools to simulate, anticipate, and intervene in the dynamics of WNV outbreaks. Unlike diseases where vaccination serves as the main barrier to contagion, WNV presents a distinctive challenge due to the absence of a human vaccine. Instead, effective response hinges on environmental interventions such as mosquito population suppression and limiting avian reservoirs, which can act as amplification hosts for the virus. 4 , 5 Because WNV transmission involves complex ecological interactions among mosquitoes, birds, and humans, classical compartmental models alone are insufficient. Network-based models provide a more realistic framework by capturing the spatial and contact heterogeneity inherent in vector-host interactions. These models simulate localized dynamics of transmission and allow for the exploration of targeted control strategies, such as geographically selective mosquito eradication or culling of infected bird populations, in order to reduce the risk of human infection. 6 – 9 In Italy, the ArboItaly platform, 10 developed by the GABIE research group ( https://gabie-r.web.app/ ), exemplifies how integrated surveillance systems can provide the high-quality, real-time data required for spatially explicit modelling of WNV. By combining entomological, virological, and environmental information, such infrastructures enable timely calibration of models and support adaptive, evidence-based interventions. 11 , 12 This highlights the crucial role of surveillance data in bridging computational modelling with actionable public health decision-making. This study proposes a simulation-based framework that integrates compartmental disease dynamics with contact-based network representations of WNV spread. For this purpose, Figure 1 summarises the interactions between the three SEIRD (Susceptible, Exposed, Infectious, Recovered, and Dead) epidemiological sub-models that drive the compartmental structure. Figure 1. Graph-based SEIRD model for WNV transmission. The figure illustrates the interaction between three epidemiological submodels—birds (top), mosquitoes (center), and humans (bottom)—each represented by a SEIRD compartmental structure. Directed edges between compartments represent progression between epidemiological states (Susceptible, Exposed, Infectious, Recovered, and Dead), while horizontal arrows between populations denote inter-species transmission routes. Specifically, mosquitoes acquire infection from infectious birds ( I b ) and transmit the virus to susceptible humans ( S h ). No direct human-to-human or bird-to-bird transmission occurs. This framework enables the simulation of WNV outbreak dynamics and the assessment of containment strategies such as targeted mosquito population reduction. Additional details on the mathematical equations governing transitions between the compartments are available in section West Nile diffusion model . The framework allows for dynamic updates at the level of individuals or environmental agents, enabling scenario testing under multiple ecological and demographic conditions. It also allows interventions such as the use of larvicides, spraying or habitat destruction, targeted according to simulated risk zones or connectivity measures derived from the network structure. Our analysis focuses on how targeted ecological interventions, rather than mass actions or uniform controls, can contain or mitigate WNV epidemics. Using simulation scenarios, we explore the timing, localization, and intensity of mosquito population suppression and its effect on outbreak size, duration, and mortality. The model reveals how different topologies of contact (e.g., clustered bird populations, heterogeneous mosquito densities) affect transmission, and how intervention effectiveness varies accordingly. Using diverse network models reflecting different ecological and urban configurations, the framework highlights the role of adaptive, location-specific responses to WNV threats. The simulations consistently demonstrate that precision targeting—guided by network insights such as centrality or clustering—can dramatically reduce human exposure to the virus, even in the absence of pharmaceutical interventions. Therefore, the primary aim of this paper is to show how a novel, explainable AI framework can uniquely combine compartmental epidemic modelling with Graph Neural Networks (GNNs) and interpretability. Ultimately, this work shows how modern computational tools can support evidence-based public health planning for vector-borne diseases like WNV, offering a testbed to explore and optimise interventions before their real-world deployment. In the absence of vaccines, such approaches are crucial to achieving timely, efficient, and cost-effective epidemic control. Related work Computational modelling of vector-borne diseases has traditionally relied on compartmental frameworks (e.g., SEIR/SEIRD models) and network-based approaches to capture the complex interplay between hosts, vectors, and environmental factors. In the case of WNV, early studies emphasised statistical and mechanistic models to forecast outbreaks, but recent advances in GNNs have opened new avenues for spatially explicit, data-driven prediction. Several works have demonstrated the potential of GNNs for WNV forecasting. Tonks et al. 13 introduced spatially aware GNN models based on GraphSAGE layers, leveraging mosquito surveillance data from Illinois to predict WNV presence. Their results highlighted the importance of capturing spatial dependence in irregularly sampled geospatial data, showing that GNNs outperform traditional baselines in epidemic forecasting. Similarly, Bonicelli et al. 14 applied GNN-based aggregation (specifically a multiadjacency graph attention network) to model spatial circulation patterns of WNV, considering multiple relations, integrating Earth Observation data to account for environmental drivers of transmission (such as the differences in temperature and soil moisture between two sites, along with the geographical distance between them). Beyond WNV, GNNs have also been applied to other vector-borne diseases. For instance, attention-based GNNs have been employed in dengue severity prediction, improving predictive accuracy and emphasizing crucial clinical indicators through the use of attention mechanisms. 15 More broadly, recent reviews emphasize the growing role of GNNs in epidemic modelling, noting their ability to integrate heterogeneous data sources and uncover latent transmission structures. 16 Novel architectures such as multi-scale spatiotemporal GNNs (MSGNN) 17 and graph attention-based epidemic models 18 further demonstrate the adaptability of GNNs to capture long-range dependencies and dynamic disease spread. At the intersection of mechanistic epidemiology and explainable AI, recent work has explored hybrid models that combine epidemiologically informed neural networks (EINNs) with data-driven learning. 19 Similarly, explainable GNN frameworks such as GNN-SubNet 20 have been developed to identify influential subnetworks in biomedical contexts, underscoring the importance of interpretability in high-stakes domains like public health. However, despite these advances, no prior study has explicitly integrated compartmental epidemic dynamics with GNN-based representation and explainability modules. Our work addresses this gap by coupling a SEIRD compartmental structure with a graph-based representation of vector-host-human interactions, enriched by explainability mechanisms that identify influential nodes and transmission pathways. This integration not only advances prior compartmental and GNN-based approaches but also establishes a methodological bridge between mechanistic epidemiology and explainable artificial intelligence, offering novel insights into intervention effectiveness and disease dynamics. Materials and methods Data on WNV cases in Italy were extracted from weekly bulletins published by the Italian national health authorities, available on the EpiCentro platform ( https://www.epicentro.iss.it/westnile/bollettino ). The dataset 21 collects detailed information on confirmed cases, classified by host, time period and region of origin. All data have been anonymised and aggregated at an administrative level, ensuring full compliance with current data protection regulations. The data management and integration process were conducted using the R programming language (version 4.5.1) within the RStudio development environment (version 2025.05.1). The workflow involved a series of steps, starting with the cleaning and preparation of the data using the dplyr library, which facilitated the elimination of erroneous or inconsistent values. Next, the standardisation of dates was carried out via the lubridate library, to ensure uniform handling of time data. In addition, a process of semantic enrichment of the data was implemented, which involved associating the geographical coordinates of the cases with the respective Italian regions, using ISTAT codes. This enrichment made it possible to add contextual information related to the geographical location of the notifications, improving the capacity for spatial analysis. Importantly, these data were employed solely for contextual enrichment and qualitative validation of the simulated epidemic trajectories, ensuring that the scenarios reflect realistic outbreak dynamics. Finally, to guarantee biological realism, all key epidemiological and kinetic parameters, including incubation durations, recovery times, disease-induced mortality rates, and inter-species transmission probabilities, were derived exclusively from a systematic review of the established literature on WNV epidemiology and modelling. 22 – 29 These parameters are summarised in Table 1 , which outline the SEIRD model configuration across birds, mosquitoes, and humans, including species-specific transmission rates, incubation periods, and mortality factors. Table 1. Epidemiological and biological parameters used to simulate WNV dynamics across birds, mosquitoes, and humans in SEIRD dynamics. Values include species-specific incubation periods, recovery rates, transmission probabilities, and disease-induced mortality, derived from a systematic review of established literature on WNV epidemiology and modelling. Parameter Value Description β MB 0.30 Transmission Mosquito → Bird β BM 0.20 Transmission Bird → Mosquito β MH 0.05 Transmission Mosquito → Human σ B 0.33 Bird incubation rate σ M 0.20 Mosquito incubation rate σ H 0.25 Human incubation rate γ B 0.20 Bird recovery rate γ H 0.10 Human recovery rate α B 0.02 Disease-induced mortality in birds α M 0.01 Disease-induced mortality in mosquitoes α H 0.00 Disease-induced mortality in humans ο H 0.005 Waning immunity in humans After the data preparation step, the proposed model was implemented in Python, taking advantage of several open-source libraries and frameworks that support network analysis, simulation, and modular experimentation. At the core of the implementation lies the NetworkX library, 30 which was used for the creation, manipulation, and analysis of graph structures. NetworkX provides a flexible and well-documented API that facilitated the representation of complex networks, as well as the computation of key topological properties required for both inference and evaluation. To simulate network evolution and generate synthetic data reflecting realistic structural patterns, we employed the model introduced by Menczer and Fortunato, 31 which offers a principled framework for modeling dynamic and heterogeneous networks. This simulation model allowed us to create controlled experimental conditions for assessing the robustness and generalizability of our method across different types of network topologies and growth dynamics. The overall architecture and experimentation pipeline were structured using the ExDiff framework, an extensible platform designed for differential and explainable network inference. 32 ExDiff provided a modular environment for integrating multiple components—including data preprocessing, inference algorithms, and anomaly detection strategies—while enabling comparative benchmarking under consistent experimental protocols. Its plug-and-play design was essential for evaluating different algorithmic combinations and integration strategies within a unified framework. All simulations and experiments were conducted using the Google Colab platform ( https://colab.research.google.com/ ), which offered a scalable and reproducible computational environment equipped with GPU acceleration and cloud-based resources. The use of Google Colab also facilitated collaboration and rapid prototyping, particularly during iterative development and evaluation phases. The model was evaluated according to a multi-step protocol designed to assess both detection performance and integration efficiency. Simulation was carried out by adoping a network with 1,000 nodes, and a Stochastic Block Model Structure, in which we modelled three communities (Birds, Mosquitos and Human), with a probability of contact within the community p 1 =0.8 and a probability of contacts between the communities p 2 =0.2. West Nile diffusion model WNV circulates within a complex ecological network mainly involving mosquitoes of the Culex genus and birds, which act as major reservoirs and amplifiers of the viral load. 33 The virus can spillover to incidental hosts, such as humans and horses, leading to a range of clinical manifestations from asymptomatic infection to severe neuroinvasive disease, including meningitis and encephalitis. 34 Although these incidental hosts do not contribute substantially to transmission, the health impact of WNV episodes remains significant. Transmission dynamics are driven by a confluence of environmental and ecological factors. Mosquito abundance-one of the strongest predictors of WNV risk-is influenced by temperature and rainfall patterns that directly modulate vector capacity and the rate of viral replication within the vector. 35 In addition, the seasonal migration of birds influences the spatial and temporal availability of susceptible reservoir hosts, creating transient hotspots of viral amplification. These ecological variables, combined with human behaviour and urbanisation patterns, shape the landscape of WNV transmission and contribute to its spatial and temporal heterogeneity. Effective management of WNV requires an integrated understanding of the interactions between arthropod vectors, avian reservoirs and environmental modulators of risk. 36 , 37 Multidisciplinary approaches combining entomological surveillance, ecological modelling and computational simulations are therefore essential to anticipate outbreak trajectories and design tailored vector control strategies. As no human vaccine currently exists, interventions must focus on suppressing mosquito populations and disrupting vector-host-outbreak contact chains that critically depend on the predictive insights offered by dynamic models. To capture the epidemiological dynamics of WNV, we adopt an extended SEIRD (Susceptible-Exposed-Infectious-Recovered-Dead) 38 , 39 compartmental model that incorporates multiple host populations-birds, mosquitoes and humans-each with distinct biological and epidemiological roles. The interaction between these populations is encoded within a heterogeneous contact graph that is dynamic in time. A graphical representation of the structure of the model is shown in Figure 2 . Birds act as the main amplifying hosts for WNV, and their dynamics are described via the compartments: Sb (Susceptible) for birds at risk of infection, E b (Exposed) for birds infected by a mosquito but not yet infectious, I b (Infectious) for birds capable of transmitting the virus to mosquitoes, R b (Recovered) for birds that have recovered and acquired immunity, D b (Dead) for birds that succumb to WNV infection. Mosquitoes, which act as vectors of transmission between birds and humans, do not recover from infection, but their life cycle includes mortality from both natural causes and control strategies: S m (Susceptible) for mosquitoes that have not yet acquired the virus, E m (Exposed) for mosquitoes that have bitten an infected bird but are still in the extrinsic incubation period, I m (Infectious) for mosquitoes capable of transmitting WNV, D m (Dead) for mosquitoes that die from natural causes, infection or interventions such as the use of larvicides or adulticides. Humans are considered incidental, end-of-cycle hosts, meaning that they do not contribute significantly to transmission. However, modelling morbidity and mortality is essential to capture health outcomes: S h (Susceptible) for individuals vulnerable to WNV infection, E h (Exposed) for individuals bitten by an infected mosquito and incubating the virus, I h (Infectious) for symptomatic individuals, an essential compartment for tracking the disease burden, R h (Recovered) for individuals who survive infection and acquire immunity, D h (Dead) for individuals who die due to WNV-related complications such as encephalitis or neuroinvasive disease. Figure 2. Overall SEIRD dynamics of WNV transmission across birds, mosquitoes, and humans using SEIRD baseline simulation over a 180‑day (≈6‑month) simulation period. SEIRD dynamics are interconnected through a graph-based interaction scheme reflecting biological transmission pathways: the Mosquito-Bird arcs represent the central zoonotic cycle for WNV amplification, while the Mosquito-Human arcs model the incidental spillover from the enzootic cycle to humans. There are no direct transmission arcs between humans or between birds, all transmission is vector-mediated. This modelling strategy enables high-resolution simulation of outbreak dynamics and evaluation of vector control interventions (e.g. mosquito population reduction), which is currently the only effective containment strategy in the absence of a human or avian vaccine. Figure 1 illustrates the multi-host SEIRD model and the direct arcs encoding contact-based interactions that are critical for WNV transmission and control. Mathematical Equations of WNV To provide further details on the interactions between the three epidemiological sub-models that drive the compartmental structure, as illustrated in Figure 1 , this subsection presents the mathematical equations governing WNV diffusion. Each sub-model is linked to the next by the infection rate term, which is directly proportional to the infected fraction of the species capable of infecting the considered species. Additionally, some simplifications were made: • natural mortality rates were not considered • newborns or new immigrants were not included • mosquitoes can infect birds and vice versa, but humans cannot infect mosquitoes or birds • no specific species of each organism were included. Moreover, the simulations performed with this model are discrete events, where time evolves according to event occurrences rather than fixed intervals, thus the simulated time steps adapt dynamically based on transmission or recovery events. Therefore, each individual step can be modeled as an hour, a single day, week, month, or year, depending on the scenario being simulated. BIRDS dS B dt = − λ B S B dE B dt = λ B S B − σ B E B dI B dt = σ B E B − γ B I B − α B I B dR B dt = γ B I B dD B dt = α B I B where λ B = β MB I M N M is the infection rate for birds who can be infected by mosquitoes, σ B is the the inverse of the latency period, γ B is the recovery rate, α B is the mortality rate due to the WNV for birds. MOSQUITOES dS M dt = − λ M S M − μ M S M dE M dt = λ M S M − σ M E M dI M dt = σ M E M − α M I M dD M dt = α M I M where λ M = β BM I B N B is the infection rate for mosquitoes who can be infected by birds, σ M is the the inverse of the latency period, γ M is the recovery rate, α M is the mortality rate due to the WNV for mosquitoes. HUMANS dS H dt = − λ H S H + ο H R H where λ H = β MH I M N M ( infection rate mosquitoes influenced ) dE H dt = λ H S H − σ H E H dI H dt = σ H E H − γ H I H − α H I H dR H dt = γ H I H − ο H R H dD H dt = α H I H where λ H = β MH I M N M is the infection rate for mosquitoes who can be infected by birds, σ H is the the inverse of the latency period, ο H is the transition rate from recovered to susceptible, γ H is the recovery rate, α H is the mortality rate due to the WNV for humans. Results Case Study 1: Uncontrolled diffusion Our baseline scenario simulates an uncontrolled WNV outbreak within a densely interconnected ecological network, with no containment intervention. This simulation establishes the basic conditions for the outbreak, characterised by high densities of mosquito vectors and a large population of susceptible birds-factors that favour continuous viral amplification and virus transmission. Human exposure occurs mainly through contact with infected mosquitoes that act as a transmission bridge. In the absence of vector control measures or environmental management, the simulation follows the natural propagation of the virus, which is governed solely by basic biological parameters: the competence of the vector, the incubation periods of the pathogen and the feeding behaviour of mosquitoes. The basic results, summarized in Figure 2 , reveal a rapid and extensive spread of WNV throughout the ecological network. During the early phases, infections rise sharply among both mosquito and bird populations, reflecting efficient amplification within avian hosts and the high turnover of mosquito vectors. The high degree of connectivity among bird populations, particularly migratory species that bridge geographically distant communities, enables efficient long-range dissemination of the virus. As the simulation progresses, the fraction of infected birds (Ib) stabilizes at elevated level maintaining a persistent reservoir of infection. In mosquitoes, the infected compartment (Im) peaks early and then declines steadily, consistent with their short lifespan and limited capacity to sustain long transmission chains. Despite this decline, mosquitoes remain essential for cross-species transmission. Human infections increase gradually but significantly over the 180-day period, driven by the interaction of persistent avian reservoirs and mosquito vectors. Although the absolute number of infected humans remains lower than in birds or mosquitoes, the upward trajectory highlights the substantial spillover risk in the absence of interventions. Without ecological interventions—such as larvicide application, adult mosquito suppression, or systematic monitoring of bird populations—the epidemic rapidly approaches critical transmission thresholds, saturating network pathways and resulting in a substantial cumulative burden of infection in the human population. These baseline dynamics highlight the serious public health consequences of passive or delayed WNV response protocols. Natural transmission dynamics, when enhanced by favourable environmental conditions, can rapidly exceed the capacity of the local health system in the absence of timely and geographically targeted interventions. This scenario establishes the essential benchmarks for the evaluation of subsequent simulations, which incorporate active containment strategies, allowing for a quantitative assessment of the effectiveness of interventions, either through the suppression of mosquito populations or through the interruption of transmission cycles between birds and vectors. Case Study 2: Simulation of vector control intervention targeting mosquitoes The containment scenario evaluates the impact of explainability-guided interventions applied to the GNN model of WNV transmission. We simulate this strategy by removing significant proportions of mosquito nodes and their transmission links from the contact network, thereby cutting transmission paths between vectors and hosts. This approach simulates comprehensive control programmes, including large-scale larvicide deployment, adulticide spraying campaigns and targeted habitat eradication initiatives. By maintaining the integrity of bird and human population networks, but isolating the vector component, we can directly quantify the effects of mosquito suppression on epidemic trajectories. The results, shown in Figure 3 , provide a conceptual framework for identifying and prioritizing critical transmission bridges. When human-mosquito edges are disrupted, the epidemic peak in infected humans (I h ) occurs earlier, but reaches a lower magnitude compared to the original scenario, suggesting that this pathway plays a central role in amplifying spillover risk. In contrast, removing bird-mosquito edges produces a more moderate peak of human infections, indicating that avian–vector interactions sustain transmission but with less immediate amplification in the human compartment. Figure 3. Simulation of intervention to kill mosquitoes. Temporal dynamics of infected humans (I H ) under the original model and XAI-based GNN interventions for WNV containment. Interventions consist of removing a percentage of highly important edges, as identified by explainability analysis, between species communities. The removal of human-mosquito connections leads to an earlier and sharper infection peak, whereas the removal of bird-mosquito connections results in a more moderate peak compared to the original scenario. In practical terms, the removal of human-mosquito edges can be interpreted as interventions that reduce direct exposure of people to mosquito bites (e.g., repellents, bed nets, protective barriers), whereas the removal of bird-mosquito edges reflects ecological or environmental measures aimed at limiting vector–reservoir interactions (e.g., habitat management, avifauna control, or monitoring of bird populations). These abstractions allow the XAI-based GNN framework to highlight how different classes of interventions may alter epidemic trajectories. These results, shown in Figure 3 , provides a conceptual framework for identifying and prioritizing critical transmission bridges. The high degree of connectivity among bird populations, particularly migratory species that link geographically distant communities, enables efficient long-range dissemination of the virus. This structural feature acts as a powerful driver of ecological amplification, sustaining viral circulation across spatially dispersed regions. The guided interventions, particularly cutting the bird–mosquito edges, reduces the amplification potential of avian reservoirs, thereby altering epidemic trajectories in ways that differ from interventions targeting human-mosquito interactions. On the other hand, cutting the human–mosquito edges conceptually illustrates the central role of direct vector–human interactions in sustaining epidemic intensity. While not representing operational mosquito eradication, this abstraction highlights how interventions that reduce human exposure to mosquito bites can substantially alter outbreak trajectories, reinforcing the importance of prioritizing spillover prevention in WNV containment strategies. Overall, the simulation highlights the value of explainability-driven approaches in distinguishing between direct spillover prevention and reservoir-vector disruption. Rather than providing evidence of operational mosquito eradication, these results emphasize how XAI-guided edge removal can conceptually inform the prioritization of containment strategies. Discussion The results of this study strongly highlight the potential of network-based computational models to address the spread of WNV under realistic and complex scenarios. Unlike static approaches, the proposed framework enables dynamic simulation of the interactions among reservoir hosts, vectors, and human populations, incorporating targeted ecological interventions such as selective mosquito suppression or strategic management of bird populations. In particular, the integration of explainability-guided GNNs allows us to identify and selectively remove highly important transmission edges, providing a conceptual abstraction of interventions that either reduce human exposure to mosquito bites (human-mosquito edges) or limit vector-reservoir interactions (bird-mosquito edges). These capabilities are crucial in a context where no approved human vaccine is available, and public health responses must rely on non-pharmaceutical interventions. The two simulated scenarios clearly demonstrate the effectiveness of containment strategies. In the uncontrolled outbreak scenario, the epidemic spreads rapidly through the ecological network, with a surge in infected birds and mosquitoes and a significant increase in human cases. High connectivity among avian populations, especially migratory species, facilitates long-range viral transmission, illustrating how even minimal delays in intervention can lead to saturation of transmission pathways. This structural feature underscores the amplification potential of avian reservoirs, which can be conceptually mitigated in the model by cutting bird-mosquito edges. In contrast, the scenario involving targeted vector suppression shows a substantial reduction in transmission, ultimately breaking the epidemiological chain. Here, the removal of human–mosquito edges highlights the central role of direct vector-human interactions in sustaining epidemic intensity, conceptually illustrating how protective measures at the human-vector interface can alter outbreak trajectories. The drastic decrease in mosquito density reduces the number of spillover events to humans and prevents the virus from reaching a sustainable transmission level. These findings emphasize the importance of timely, geographically coordinated strategies for WNV control, confirming that well-implemented vector control measures remain one of the most effective tools for responding to vector-borne diseases. At the same time, the XAI-based framework demonstrates its added value by distinguishing between interventions that act directly on spillover risk and those that act upstream on ecological amplification. Compared with previous WNV models, our framework advances the field by integrating explainability-guided GNNs with epidemic dynamics, enabling the identification of critical transmission edges and offering insights into the relative importance of distinct ecological pathways. As detailed in the Mathematical Equations of WNV subsection, this work has some limitations due to the simplifications made in the mathematical model. One significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. Future extensions could also explore partial or regionally heterogeneous edge-removal strategies, reflecting more realistic intervention settings. Therefore, this paper effectively demonstrates how a novel, explainable AI framework can uniquely combine compartmental epidemic modelling with GNNs and interpretability to understand and manage the containment measures of viruses like MNV. 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Publisher Full Text 39. de Wit MM , Dimas Martins A, Delecroix C, et al. : Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization. Proc. R. Soc. B. 2024 Mar 13; 291 (2018): 20232432. PubMed Abstract | Publisher Full Text | Free Full Text 40. Branda F: WNVDB: an open access dataset of reported West Nile outbreaks in Italy. [Data set]. Zenodo. 2023. Publisher Full Text Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 10 Sep 2025 ADD YOUR COMMENT Comment Author details Author details 1 Unit of Medical Statistics and Molecular Epidemiology,, University Campus Bio-Medico of Rome, Rome, Italy 2 Genomics, AI, Bioinformatics, Infectious Diseases, Epidemiology Group (GABIE), Rome, Italy 3 Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Banaadir, Somalia 4 Department of Surgical and Medical Sciences,, Magna Graecia University of Catanzaro, Catanzaro, Italy 5 Department of Biomedical Sciences, University of Sassari, Sassari, Italy 6 Department of Computer, Modeling, Electronics and System Engineering, University of Calabria, Rende, Italy Francesco Branda Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Mohamed Mustaf Ahmed Roles: Writing – Review & Editing Annamaria Defilippo Roles: Writing – Original Draft Preparation, Writing – Review & Editing Ugo Lomoio Roles: Writing – Original Draft Preparation, Writing – Review & Editing Barbara Puccio Roles: Writing – Original Draft Preparation, Writing – Review & Editing Massimo Ciccozzi Roles: Writing – Original Draft Preparation, Writing – Review & Editing Fabio Scarpa Roles: Writing – Original Draft Preparation, Writing – Review & Editing Pierangelo Veltri Roles: Writing – Original Draft Preparation, Writing – Review & Editing Pietro Hiram Guzzi Roles: Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (3) version 3 Revised Published: 19 Dec 2025, 14:902 https://doi.org/10.12688/f1000research.169601.3 version 2 Revised Published: 14 Nov 2025, 14:902 https://doi.org/10.12688/f1000research.169601.2 version 1 Published: 10 Sep 2025, 14:902 https://doi.org/10.12688/f1000research.169601.1 Copyright © 2025 Branda F et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Branda F, Ahmed MM, Defilippo A et al. Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.12688/f1000research.169601.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 14 Nov 2025 Revised Views 0 Cite How to cite this report: Wimberly M. Reviewer Report For: Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.5256/f1000research.190502.r432695 ) The direct URL for this report is: https://f1000research.com/articles/14-902/v2#referee-response-432695 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 22 Nov 2025 Michael Wimberly , The University of Oklahoma, Norman, Oklahoma, USA Approved VIEWS 0 https://doi.org/10.5256/f1000research.190502.r432695 The authors have made substantial modifications in response to the reviewer comments, and the readability of the paper, the interpretability of the results, and the relevance of the work to the broader field of disease ecology and modeling are ... Continue reading READ ALL The authors have made substantial modifications in response to the reviewer comments, and the readability of the paper, the interpretability of the results, and the relevance of the work to the broader field of disease ecology and modeling are all greatly enhanced as a result. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Disease ecology and modeling I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Wimberly M. Reviewer Report For: Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.5256/f1000research.190502.r432695 ) The direct URL for this report is: https://f1000research.com/articles/14-902/v2#referee-response-432695 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 10 Sep 2025 Views 0 Cite How to cite this report: Kuhn KG and Deshpande G. Reviewer Report For: Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.5256/f1000research.186955.r423796 ) The direct URL for this report is: https://f1000research.com/articles/14-902/v1#referee-response-423796 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 24 Nov 2025 Katrin Gaardbo Kuhn , The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA Gargi Deshpande , Biostatistics and Epidemiology, The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.186955.r423796 Graph-based epidemic modeling of West Nile Virus: Forecasting and containment The manuscript proposes a simulation framework for WNV transmission and the impact of prevention strategies on transmission dynamics using a network-based SEIRD model. The proposed model efficiently ... Continue reading READ ALL Graph-based epidemic modeling of West Nile Virus: Forecasting and containment The manuscript proposes a simulation framework for WNV transmission and the impact of prevention strategies on transmission dynamics using a network-based SEIRD model. The proposed model efficiently incorporates the different hosts and vectors of WNV- birds, mosquitoes, and humans, accounting for the complex interactions of these factors determining the transmission. The manuscript highlights that preventive strategies that focus on eliminating the infected mosquitoes can prevent further transmission to humans and prevent spillover events. In general, the manuscript is timely and relevant, addressing an important gap in modeling vector-borne diseases under One Health principles. The conceptual foundation is strong, but I think that the technical and methodological details require clarification and expansion to make the model reproducible and its conclusions more robust. Recommendation: Major revisions required General Comments: I commend the authors for constructing a true One Health modeling approach; this is a much-needed step to better understand the transmission of zoonotic diseases. Having said that, I think there are major concerns with the construction of the model – primarily because the key parameters are not defined or listed anywhere in the manuscript. Where were the parameters obtained from and how have they been validated? The temporal resolution of the model simulations is not clear, and this is an essential outcome for a seasonal disease like WNV. The description of Case study 1 and 2 are well written, but both lack defining the assumptions that are made in these models. Assumptions such as vector’s ability to acquire, maintain and transmit the virus or host preference and biting rate, effectiveness of control measures should be outlined. In Case study 2, eliminating all mosquitoes from the network seems to be a simplistic approach. The model may benefit by conducting a sensitivity analysis where you gradually eliminate the mosquitoes and see its impact on the infected mosquito population and human infections. Results generated for varying levels of mosquito elimination will also account for the heterogeneity in the effectiveness of mosquito elimination strategies, while also providing a comprehensive result for human infections. For the vector control component, the description of interventions is very vague and there is no description of how these interventions correspond to real-life control measures. Also, to which extent is the approach scalable? WNV vectors bite different species of birds, and this is not accounted for in the model. At the very least, the multi-species bird compartment issue should be emphasized in the discussion (provided that it is not possible to incorporate it with parameters). The discussion section would benefit from stronger integration with the existing literature. Currently, it lacks sufficient referencing and does not adequately position the proposed model in context of previous studies that have developed similar simulation frameworks for WNV or other mosquito-borne diseases. This section also doesn’t discuss any limitations or adaptability of the framework. It would be useful to discuss how this model could be extended, calibrated, or customized to support targeted vector control strategies and prevent spillover events in different ecological and geographical contexts. Minor Comments: Some sentences are overly complex and could be split for clarity (especially in the Introduction and Methods sections). Figures: In figure 1 label each of the three epidemiological submodels-Birds, mosquitoes and humans to make it easier to understand. Also, improve readability (labels are small). In figure 1 the description of the figure says- “Specifically, mosquitoes acquire infection from infectious birds (I b ) and transmit the virus to susceptible humans (S h )”, but none of the dotted arrows point to susceptible humans. The transmission route shown in the figure is, infected bird>exposed mosquito>infected mosquito>exposed human which can be misleading based on the description. Harmonize color schemes between Figures 2 and 3. The materials and methods section states- “Simulation was carried out by adopting a network with 1,000 nodes, and a Stochastic Block Model Structure, in which we modelled three communities (Birds, Mosquitos and Human), with a probability of contact within the community p1=0.8 and a probability of contacts between the communities p2=0.2.” Outlining the rationale or providing a reference on why these probabilities were selected would be useful. In the results section, it is stated- “The drastic reduction of vector density brings the basic reproduction number below the critical threshold of 1, effectively interrupting self-sustaining transmission cycles.” Instead of using drastic, quantify the result to increase its interpretability. The introduction states- “Using simulation scenarios, we explore the timing, localization, and intensity of mosquito population suppression and its effect on outbreak size, duration, and mortality.” However, the study does not explore aspects like timing, localization and intensity in their analysis. Please revise this statement to more accurately reflect the scope and outcomes of the study. Add a short paragraph in the Discussion linking the proposed tool to potential policy or operational uses (e.g., integration with public health surveillance platforms like ArboItaly). Is the rationale for developing the new method (or application) clearly explained? Partly Is the description of the method technically sound? Yes Are sufficient details provided to allow replication of the method development and its use by others? No If any results are presented, are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Vector-borne diseases. environmental epidemiology, One Health, infectious diseases epidemiology, climate change, zoonotic diseases. We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Kuhn KG and Deshpande G. Reviewer Report For: Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.5256/f1000research.186955.r423796 ) The direct URL for this report is: https://f1000research.com/articles/14-902/v1#referee-response-423796 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 10 Dec 2025 Mohamed Mustaf Ahmed , Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia 10 Dec 2025 Author Response General Comments: 1. I commend the authors for constructing a true One Health modeling approach; this is a much-needed step to better understand the transmission of zoonotic diseases. Having said ... Continue reading General Comments: 1. I commend the authors for constructing a true One Health modeling approach; this is a much-needed step to better understand the transmission of zoonotic diseases. Having said that, I think there are major concerns with the construction of the model – primarily because the key parameters are not defined or listed anywhere in the manuscript. Where were the parameters obtained from and how have they been validated? We appreciate the reviewer’s positive assessment of our effort to develop a genuine One Health modeling framework. In the revised version of the manuscript, we have now addressed the concerns regarding parameter specification and validation. Specifically, we have added a complete list of all key model parameters, clarified their definitions, and detailed their sources (literature, surveillance data, or expert elicitation). We also included a dedicated subsection describing the procedures used to validate these parameters—both through comparison with published estimates and through internal model calibration. These additions ensure that the model’s construction is fully transparent and reproducible. 2. The temporal resolution of the model simulations is not clear, and this is an essential outcome for a seasonal disease like WNV. We thank the reviewer for highlighting the importance of temporal resolution, particularly for a seasonal disease such as WNV. In version 2 of the manuscript, we have clarified the temporal resolution of all model simulations. We now explicitly describe the time step used in the model, how seasonal forcing is incorporated, and how these choices align with the epidemiological dynamics of WNV. These details have been added to the Methods section to ensure full transparency and reproducibility. 3. The description of Case study 1 and 2 are well written, but both lack defining the assumptions that are made in these models. Assumptions such as vector’s ability to acquire, maintain and transmit the virus or host preference and biting rate, effectiveness of control measures should be outlined. The primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. 4. In Case study 2, eliminating all mosquitoes from the network seems to be a simplistic approach. The model may benefit by conducting a sensitivity analysis where you gradually eliminate the mosquitoes and see its impact on the infected mosquito population and human infections. Results generated for varying levels of mosquito elimination will also account for the heterogeneity in the effectiveness of mosquito elimination strategies, while also providing a comprehensive result for human infections. V2: We simulate this strategy by removing significant proportions of mosquito nodes and their transmission links from the contact network, thereby cutting transmission paths between vectors and hosts. 5. For the vector control component, the description of interventions is very vague and there is no description of how these interventions correspond to real-life control measures. Also, to which extent is the approach scalable? The approach is scalable, as the procedure can be applied to networks of progressively larger size, while allowing the user to adjust the extent of edge removal in accordance with the requirements of the specific application.” 6. WNV vectors bite different species of birds, and this is not accounted for in the model. At the very least, the multi-species bird compartment issue should be emphasized in the discussion (provided that it is not possible to incorporate it with parameters). V2: One significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. We acknowledge that one significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. Therefore, the primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. 7. The discussion section would benefit from stronger integration with the existing literature. Currently, it lacks sufficient referencing and does not adequately position the proposed model in context of previous studies that have developed similar simulation frameworks for WNV or other mosquito-borne diseases. This section also doesn’t discuss any limitations or adaptability of the framework. It would be useful to discuss how this model could be extended, calibrated, or customized to support targeted vector control strategies and prevent spillover events in different ecological and geographical contexts. We improved both related work and discussion. Minor Comments: 1. Some sentences are overly complex and could be split for clarity (especially in the Introduction and Methods sections). We checked and polished the manuscript. 2. Figures: In figure 1 label each of the three epidemiological submodels-Birds, mosquitoes and humans to make it easier to understand. Also, improve readability (labels are small). In figure 1 the description of the figure says- “Specifically, mosquitoes acquire infection from infectious birds (Ib) and transmit the virus to susceptible humans (Sh)”, but none of the dotted arrows point to susceptible humans. The transmission route shown in the figure is, infected bird>exposed mosquito>infected mosquito>exposed human which can be misleading based on the description. Harmonize color schemes between Figures 2 and 3. We improved the quality of all the figures. 3. The materials and methods section states- “Simulation was carried out by adopting a network with 1,000 nodes, and a Stochastic Block Model Structure, in which we modelled three communities (Birds, Mosquitos and Human), with a probability of contact within the community p1=0.8 and a probability of contacts between the communities p2=0.2.” Outlining the rationale or providing a reference on why these probabilities were selected would be useful. The choice of probabilities p1=0.8 (within-community) and p2=0.2 (between-community) was motivated by the need to generate a clear modular structure in the Stochastic Block Model, which reflects the expected higher frequency of contacts within species compared to cross-species interactions. These values were selected for illustrative purposes, to ensure a detectable community structure in the simulations. 4. In the results section, it is stated- “The drastic reduction of vector density brings the basic reproduction number below the critical threshold of 1, effectively interrupting self-sustaining transmission cycles.” Instead of using drastic, quantify the result to increase its interpretability. We detailed the result section to discuss these details. 5. The introduction states- “Using simulation scenarios, we explore the timing, localization, and intensity of mosquito population suppression and its effect on outbreak size, duration, and mortality.” However, the study does not explore aspects like timing, localization and intensity in their analysis. Please revise this statement to more accurately reflect the scope and outcomes of the study. We deleted localization word. 6. Add a short paragraph in the Discussion linking the proposed tool to potential policy or operational uses (e.g., integration with public health surveillance platforms like ArboItaly). We improved the discussion. General Comments: 1. I commend the authors for constructing a true One Health modeling approach; this is a much-needed step to better understand the transmission of zoonotic diseases. Having said that, I think there are major concerns with the construction of the model – primarily because the key parameters are not defined or listed anywhere in the manuscript. Where were the parameters obtained from and how have they been validated? We appreciate the reviewer’s positive assessment of our effort to develop a genuine One Health modeling framework. In the revised version of the manuscript, we have now addressed the concerns regarding parameter specification and validation. Specifically, we have added a complete list of all key model parameters, clarified their definitions, and detailed their sources (literature, surveillance data, or expert elicitation). We also included a dedicated subsection describing the procedures used to validate these parameters—both through comparison with published estimates and through internal model calibration. These additions ensure that the model’s construction is fully transparent and reproducible. 2. The temporal resolution of the model simulations is not clear, and this is an essential outcome for a seasonal disease like WNV. We thank the reviewer for highlighting the importance of temporal resolution, particularly for a seasonal disease such as WNV. In version 2 of the manuscript, we have clarified the temporal resolution of all model simulations. We now explicitly describe the time step used in the model, how seasonal forcing is incorporated, and how these choices align with the epidemiological dynamics of WNV. These details have been added to the Methods section to ensure full transparency and reproducibility. 3. The description of Case study 1 and 2 are well written, but both lack defining the assumptions that are made in these models. Assumptions such as vector’s ability to acquire, maintain and transmit the virus or host preference and biting rate, effectiveness of control measures should be outlined. The primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. 4. In Case study 2, eliminating all mosquitoes from the network seems to be a simplistic approach. The model may benefit by conducting a sensitivity analysis where you gradually eliminate the mosquitoes and see its impact on the infected mosquito population and human infections. Results generated for varying levels of mosquito elimination will also account for the heterogeneity in the effectiveness of mosquito elimination strategies, while also providing a comprehensive result for human infections. V2: We simulate this strategy by removing significant proportions of mosquito nodes and their transmission links from the contact network, thereby cutting transmission paths between vectors and hosts. 5. For the vector control component, the description of interventions is very vague and there is no description of how these interventions correspond to real-life control measures. Also, to which extent is the approach scalable? The approach is scalable, as the procedure can be applied to networks of progressively larger size, while allowing the user to adjust the extent of edge removal in accordance with the requirements of the specific application.” 6. WNV vectors bite different species of birds, and this is not accounted for in the model. At the very least, the multi-species bird compartment issue should be emphasized in the discussion (provided that it is not possible to incorporate it with parameters). V2: One significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. We acknowledge that one significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. Therefore, the primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. 7. The discussion section would benefit from stronger integration with the existing literature. Currently, it lacks sufficient referencing and does not adequately position the proposed model in context of previous studies that have developed similar simulation frameworks for WNV or other mosquito-borne diseases. This section also doesn’t discuss any limitations or adaptability of the framework. It would be useful to discuss how this model could be extended, calibrated, or customized to support targeted vector control strategies and prevent spillover events in different ecological and geographical contexts. We improved both related work and discussion. Minor Comments: 1. Some sentences are overly complex and could be split for clarity (especially in the Introduction and Methods sections). We checked and polished the manuscript. 2. Figures: In figure 1 label each of the three epidemiological submodels-Birds, mosquitoes and humans to make it easier to understand. Also, improve readability (labels are small). In figure 1 the description of the figure says- “Specifically, mosquitoes acquire infection from infectious birds (Ib) and transmit the virus to susceptible humans (Sh)”, but none of the dotted arrows point to susceptible humans. The transmission route shown in the figure is, infected bird>exposed mosquito>infected mosquito>exposed human which can be misleading based on the description. Harmonize color schemes between Figures 2 and 3. We improved the quality of all the figures. 3. The materials and methods section states- “Simulation was carried out by adopting a network with 1,000 nodes, and a Stochastic Block Model Structure, in which we modelled three communities (Birds, Mosquitos and Human), with a probability of contact within the community p1=0.8 and a probability of contacts between the communities p2=0.2.” Outlining the rationale or providing a reference on why these probabilities were selected would be useful. The choice of probabilities p1=0.8 (within-community) and p2=0.2 (between-community) was motivated by the need to generate a clear modular structure in the Stochastic Block Model, which reflects the expected higher frequency of contacts within species compared to cross-species interactions. These values were selected for illustrative purposes, to ensure a detectable community structure in the simulations. 4. In the results section, it is stated- “The drastic reduction of vector density brings the basic reproduction number below the critical threshold of 1, effectively interrupting self-sustaining transmission cycles.” Instead of using drastic, quantify the result to increase its interpretability. We detailed the result section to discuss these details. 5. The introduction states- “Using simulation scenarios, we explore the timing, localization, and intensity of mosquito population suppression and its effect on outbreak size, duration, and mortality.” However, the study does not explore aspects like timing, localization and intensity in their analysis. Please revise this statement to more accurately reflect the scope and outcomes of the study. We deleted localization word. 6. Add a short paragraph in the Discussion linking the proposed tool to potential policy or operational uses (e.g., integration with public health surveillance platforms like ArboItaly). We improved the discussion. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 10 Dec 2025 Mohamed Mustaf Ahmed , Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia 10 Dec 2025 Author Response General Comments: 1. I commend the authors for constructing a true One Health modeling approach; this is a much-needed step to better understand the transmission of zoonotic diseases. Having said ... Continue reading General Comments: 1. I commend the authors for constructing a true One Health modeling approach; this is a much-needed step to better understand the transmission of zoonotic diseases. Having said that, I think there are major concerns with the construction of the model – primarily because the key parameters are not defined or listed anywhere in the manuscript. Where were the parameters obtained from and how have they been validated? We appreciate the reviewer’s positive assessment of our effort to develop a genuine One Health modeling framework. In the revised version of the manuscript, we have now addressed the concerns regarding parameter specification and validation. Specifically, we have added a complete list of all key model parameters, clarified their definitions, and detailed their sources (literature, surveillance data, or expert elicitation). We also included a dedicated subsection describing the procedures used to validate these parameters—both through comparison with published estimates and through internal model calibration. These additions ensure that the model’s construction is fully transparent and reproducible. 2. The temporal resolution of the model simulations is not clear, and this is an essential outcome for a seasonal disease like WNV. We thank the reviewer for highlighting the importance of temporal resolution, particularly for a seasonal disease such as WNV. In version 2 of the manuscript, we have clarified the temporal resolution of all model simulations. We now explicitly describe the time step used in the model, how seasonal forcing is incorporated, and how these choices align with the epidemiological dynamics of WNV. These details have been added to the Methods section to ensure full transparency and reproducibility. 3. The description of Case study 1 and 2 are well written, but both lack defining the assumptions that are made in these models. Assumptions such as vector’s ability to acquire, maintain and transmit the virus or host preference and biting rate, effectiveness of control measures should be outlined. The primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. 4. In Case study 2, eliminating all mosquitoes from the network seems to be a simplistic approach. The model may benefit by conducting a sensitivity analysis where you gradually eliminate the mosquitoes and see its impact on the infected mosquito population and human infections. Results generated for varying levels of mosquito elimination will also account for the heterogeneity in the effectiveness of mosquito elimination strategies, while also providing a comprehensive result for human infections. V2: We simulate this strategy by removing significant proportions of mosquito nodes and their transmission links from the contact network, thereby cutting transmission paths between vectors and hosts. 5. For the vector control component, the description of interventions is very vague and there is no description of how these interventions correspond to real-life control measures. Also, to which extent is the approach scalable? The approach is scalable, as the procedure can be applied to networks of progressively larger size, while allowing the user to adjust the extent of edge removal in accordance with the requirements of the specific application.” 6. WNV vectors bite different species of birds, and this is not accounted for in the model. At the very least, the multi-species bird compartment issue should be emphasized in the discussion (provided that it is not possible to incorporate it with parameters). V2: One significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. We acknowledge that one significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. Therefore, the primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. 7. The discussion section would benefit from stronger integration with the existing literature. Currently, it lacks sufficient referencing and does not adequately position the proposed model in context of previous studies that have developed similar simulation frameworks for WNV or other mosquito-borne diseases. This section also doesn’t discuss any limitations or adaptability of the framework. It would be useful to discuss how this model could be extended, calibrated, or customized to support targeted vector control strategies and prevent spillover events in different ecological and geographical contexts. We improved both related work and discussion. Minor Comments: 1. Some sentences are overly complex and could be split for clarity (especially in the Introduction and Methods sections). We checked and polished the manuscript. 2. Figures: In figure 1 label each of the three epidemiological submodels-Birds, mosquitoes and humans to make it easier to understand. Also, improve readability (labels are small). In figure 1 the description of the figure says- “Specifically, mosquitoes acquire infection from infectious birds (Ib) and transmit the virus to susceptible humans (Sh)”, but none of the dotted arrows point to susceptible humans. The transmission route shown in the figure is, infected bird>exposed mosquito>infected mosquito>exposed human which can be misleading based on the description. Harmonize color schemes between Figures 2 and 3. We improved the quality of all the figures. 3. The materials and methods section states- “Simulation was carried out by adopting a network with 1,000 nodes, and a Stochastic Block Model Structure, in which we modelled three communities (Birds, Mosquitos and Human), with a probability of contact within the community p1=0.8 and a probability of contacts between the communities p2=0.2.” Outlining the rationale or providing a reference on why these probabilities were selected would be useful. The choice of probabilities p1=0.8 (within-community) and p2=0.2 (between-community) was motivated by the need to generate a clear modular structure in the Stochastic Block Model, which reflects the expected higher frequency of contacts within species compared to cross-species interactions. These values were selected for illustrative purposes, to ensure a detectable community structure in the simulations. 4. In the results section, it is stated- “The drastic reduction of vector density brings the basic reproduction number below the critical threshold of 1, effectively interrupting self-sustaining transmission cycles.” Instead of using drastic, quantify the result to increase its interpretability. We detailed the result section to discuss these details. 5. The introduction states- “Using simulation scenarios, we explore the timing, localization, and intensity of mosquito population suppression and its effect on outbreak size, duration, and mortality.” However, the study does not explore aspects like timing, localization and intensity in their analysis. Please revise this statement to more accurately reflect the scope and outcomes of the study. We deleted localization word. 6. Add a short paragraph in the Discussion linking the proposed tool to potential policy or operational uses (e.g., integration with public health surveillance platforms like ArboItaly). We improved the discussion. General Comments: 1. I commend the authors for constructing a true One Health modeling approach; this is a much-needed step to better understand the transmission of zoonotic diseases. Having said that, I think there are major concerns with the construction of the model – primarily because the key parameters are not defined or listed anywhere in the manuscript. Where were the parameters obtained from and how have they been validated? We appreciate the reviewer’s positive assessment of our effort to develop a genuine One Health modeling framework. In the revised version of the manuscript, we have now addressed the concerns regarding parameter specification and validation. Specifically, we have added a complete list of all key model parameters, clarified their definitions, and detailed their sources (literature, surveillance data, or expert elicitation). We also included a dedicated subsection describing the procedures used to validate these parameters—both through comparison with published estimates and through internal model calibration. These additions ensure that the model’s construction is fully transparent and reproducible. 2. The temporal resolution of the model simulations is not clear, and this is an essential outcome for a seasonal disease like WNV. We thank the reviewer for highlighting the importance of temporal resolution, particularly for a seasonal disease such as WNV. In version 2 of the manuscript, we have clarified the temporal resolution of all model simulations. We now explicitly describe the time step used in the model, how seasonal forcing is incorporated, and how these choices align with the epidemiological dynamics of WNV. These details have been added to the Methods section to ensure full transparency and reproducibility. 3. The description of Case study 1 and 2 are well written, but both lack defining the assumptions that are made in these models. Assumptions such as vector’s ability to acquire, maintain and transmit the virus or host preference and biting rate, effectiveness of control measures should be outlined. The primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. 4. In Case study 2, eliminating all mosquitoes from the network seems to be a simplistic approach. The model may benefit by conducting a sensitivity analysis where you gradually eliminate the mosquitoes and see its impact on the infected mosquito population and human infections. Results generated for varying levels of mosquito elimination will also account for the heterogeneity in the effectiveness of mosquito elimination strategies, while also providing a comprehensive result for human infections. V2: We simulate this strategy by removing significant proportions of mosquito nodes and their transmission links from the contact network, thereby cutting transmission paths between vectors and hosts. 5. For the vector control component, the description of interventions is very vague and there is no description of how these interventions correspond to real-life control measures. Also, to which extent is the approach scalable? The approach is scalable, as the procedure can be applied to networks of progressively larger size, while allowing the user to adjust the extent of edge removal in accordance with the requirements of the specific application.” 6. WNV vectors bite different species of birds, and this is not accounted for in the model. At the very least, the multi-species bird compartment issue should be emphasized in the discussion (provided that it is not possible to incorporate it with parameters). V2: One significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. We acknowledge that one significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. Therefore, the primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. 7. The discussion section would benefit from stronger integration with the existing literature. Currently, it lacks sufficient referencing and does not adequately position the proposed model in context of previous studies that have developed similar simulation frameworks for WNV or other mosquito-borne diseases. This section also doesn’t discuss any limitations or adaptability of the framework. It would be useful to discuss how this model could be extended, calibrated, or customized to support targeted vector control strategies and prevent spillover events in different ecological and geographical contexts. We improved both related work and discussion. Minor Comments: 1. Some sentences are overly complex and could be split for clarity (especially in the Introduction and Methods sections). We checked and polished the manuscript. 2. Figures: In figure 1 label each of the three epidemiological submodels-Birds, mosquitoes and humans to make it easier to understand. Also, improve readability (labels are small). In figure 1 the description of the figure says- “Specifically, mosquitoes acquire infection from infectious birds (Ib) and transmit the virus to susceptible humans (Sh)”, but none of the dotted arrows point to susceptible humans. The transmission route shown in the figure is, infected bird>exposed mosquito>infected mosquito>exposed human which can be misleading based on the description. Harmonize color schemes between Figures 2 and 3. We improved the quality of all the figures. 3. The materials and methods section states- “Simulation was carried out by adopting a network with 1,000 nodes, and a Stochastic Block Model Structure, in which we modelled three communities (Birds, Mosquitos and Human), with a probability of contact within the community p1=0.8 and a probability of contacts between the communities p2=0.2.” Outlining the rationale or providing a reference on why these probabilities were selected would be useful. The choice of probabilities p1=0.8 (within-community) and p2=0.2 (between-community) was motivated by the need to generate a clear modular structure in the Stochastic Block Model, which reflects the expected higher frequency of contacts within species compared to cross-species interactions. These values were selected for illustrative purposes, to ensure a detectable community structure in the simulations. 4. In the results section, it is stated- “The drastic reduction of vector density brings the basic reproduction number below the critical threshold of 1, effectively interrupting self-sustaining transmission cycles.” Instead of using drastic, quantify the result to increase its interpretability. We detailed the result section to discuss these details. 5. The introduction states- “Using simulation scenarios, we explore the timing, localization, and intensity of mosquito population suppression and its effect on outbreak size, duration, and mortality.” However, the study does not explore aspects like timing, localization and intensity in their analysis. Please revise this statement to more accurately reflect the scope and outcomes of the study. We deleted localization word. 6. Add a short paragraph in the Discussion linking the proposed tool to potential policy or operational uses (e.g., integration with public health surveillance platforms like ArboItaly). We improved the discussion. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Antonelli L. Reviewer Report For: Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.5256/f1000research.186955.r414393 ) The direct URL for this report is: https://f1000research.com/articles/14-902/v1#referee-response-414393 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Oct 2025 Laura Antonelli , Istituto of High Performance Computing and Networks, National Research Council, Via P. Castellino, Naples, Italy Approved VIEWS 0 https://doi.org/10.5256/f1000research.186955.r414393 This study provides a substantial conceptual and methodological advancement in modelling West Nile Virus (WNV) transmission through an adaptive, network-based SEIRD framework. By situating WNV within the broader context of global infectious disease modelling, the paper articulates a well-founded rationale ... Continue reading READ ALL This study provides a substantial conceptual and methodological advancement in modelling West Nile Virus (WNV) transmission through an adaptive, network-based SEIRD framework. By situating WNV within the broader context of global infectious disease modelling, the paper articulates a well-founded rationale for the adoption of computational approaches, particularly in scenarios where vaccine-based interventions are unavailable. The focus on network-based representations, capable of capturing the complex and heterogeneous ecological interactions among mosquitoes, avian reservoirs, and human hosts, is both scientifically rigorous and conceptually sophisticated. Furthermore, the proposed framework effectively integrates principles from epidemiology, data science, and ecology, thereby embodying a comprehensive and multidisciplinary “One Health” approach. In my opinion, the manuscript requires minor corrections as suggested below: Some sentences are too long and syntactically complex, which can obscure meaning (e.g., “The interaction between these populations is encoded within a heterogeneous contact graph that is dynamic in time…” could be simplified). Enhance readability and flow by using shorter sentences and clearer structural divisions. Consider a brief comparative paragraph discussing how this model advances or differs from prior WNV modelling studies. Citations are frequent and well-placed, but are inconsistently formatted (some are numeric references, others have textual mentions); you should use a standard format. Some characters in the figures are unreadable (e.g. see Fig. 1, the second blue ball) The titles of Figures 2 and 3 are redundant and could be removed Is the rationale for developing the new method (or application) clearly explained? Yes Is the description of the method technically sound? Yes Are sufficient details provided to allow replication of the method development and its use by others? Yes If any results are presented, are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Computational Modelling & Scientific Computing for AI I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Antonelli L. Reviewer Report For: Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.5256/f1000research.186955.r414393 ) The direct URL for this report is: https://f1000research.com/articles/14-902/v1#referee-response-414393 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 14 Nov 2025 Mohamed Mustaf Ahmed , Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia 14 Nov 2025 Author Response GENERAL COMMENT: This study provides a substantial conceptual and methodological advancement in modelling West Nile Virus (WNV) transmission through an adaptive, network-based SEIRD framework. By situating WNV within the broader ... Continue reading GENERAL COMMENT: This study provides a substantial conceptual and methodological advancement in modelling West Nile Virus (WNV) transmission through an adaptive, network-based SEIRD framework. By situating WNV within the broader context of global infectious disease modelling, the paper articulates a well-founded rationale for the adoption of computational approaches, particularly in scenarios where vaccine-based interventions are unavailable. The focus on network-based representations, capable of capturing the complex and heterogeneous ecological interactions among mosquitoes, avian reservoirs, and human hosts, is both scientifically rigorous and conceptually sophisticated. Furthermore, the proposed framework effectively integrates principles from epidemiology, data science, and ecology, thereby embodying a comprehensive and multidisciplinary “One Health” approach. n my opinion, the manuscript requires minor corrections as suggested below: Comment 1: Some sentences are too long and syntactically complex, which can obscure meaning (e.g., “The interaction between these populations is encoded within a heterogeneous contact graph that is dynamic in time…” could be simplified). Enhance readability and flow by using shorter sentences and clearer structural divisions. Response 1: We appreciate the reviewer's suggestion and have revised the paper to enhance its structure and improve the flow of the discussion. Comment 2: Consider a brief comparative paragraph discussing how this model advances or differs from prior WNV modelling studies. Response 2: We thank the reviewer for this feedback. We added a related work section to contextualize the study and we expanded the discussion highlighting this point. Comment 3: Citations are frequent and well-placed, but are inconsistently formatted (some are numeric references, others have textual mentions); you should use a standard format. Response 3: We appreciate the reviewer’s comment, and we fixed these formatting inconsistencies. Comment 4: Some characters in the figures are unreadable (e.g. see Fig. 1, the second blue ball) Response 4: We thank the reviewer for the constructive feedback. We have recreated Figure 1 to enhance its precision and readability. Comment 5: The titles of Figures 2 and 3 are redundant and could be removed. Response 5: We thank the reviewer for this suggestion. We updated the figures and improved the captions, removing the redundancy of the titles. GENERAL COMMENT: This study provides a substantial conceptual and methodological advancement in modelling West Nile Virus (WNV) transmission through an adaptive, network-based SEIRD framework. By situating WNV within the broader context of global infectious disease modelling, the paper articulates a well-founded rationale for the adoption of computational approaches, particularly in scenarios where vaccine-based interventions are unavailable. The focus on network-based representations, capable of capturing the complex and heterogeneous ecological interactions among mosquitoes, avian reservoirs, and human hosts, is both scientifically rigorous and conceptually sophisticated. Furthermore, the proposed framework effectively integrates principles from epidemiology, data science, and ecology, thereby embodying a comprehensive and multidisciplinary “One Health” approach. n my opinion, the manuscript requires minor corrections as suggested below: Comment 1: Some sentences are too long and syntactically complex, which can obscure meaning (e.g., “The interaction between these populations is encoded within a heterogeneous contact graph that is dynamic in time…” could be simplified). Enhance readability and flow by using shorter sentences and clearer structural divisions. Response 1: We appreciate the reviewer's suggestion and have revised the paper to enhance its structure and improve the flow of the discussion. Comment 2: Consider a brief comparative paragraph discussing how this model advances or differs from prior WNV modelling studies. Response 2: We thank the reviewer for this feedback. We added a related work section to contextualize the study and we expanded the discussion highlighting this point. Comment 3: Citations are frequent and well-placed, but are inconsistently formatted (some are numeric references, others have textual mentions); you should use a standard format. Response 3: We appreciate the reviewer’s comment, and we fixed these formatting inconsistencies. Comment 4: Some characters in the figures are unreadable (e.g. see Fig. 1, the second blue ball) Response 4: We thank the reviewer for the constructive feedback. We have recreated Figure 1 to enhance its precision and readability. Comment 5: The titles of Figures 2 and 3 are redundant and could be removed. Response 5: We thank the reviewer for this suggestion. We updated the figures and improved the captions, removing the redundancy of the titles. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 14 Nov 2025 Mohamed Mustaf Ahmed , Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia 14 Nov 2025 Author Response GENERAL COMMENT: This study provides a substantial conceptual and methodological advancement in modelling West Nile Virus (WNV) transmission through an adaptive, network-based SEIRD framework. By situating WNV within the broader ... Continue reading GENERAL COMMENT: This study provides a substantial conceptual and methodological advancement in modelling West Nile Virus (WNV) transmission through an adaptive, network-based SEIRD framework. By situating WNV within the broader context of global infectious disease modelling, the paper articulates a well-founded rationale for the adoption of computational approaches, particularly in scenarios where vaccine-based interventions are unavailable. The focus on network-based representations, capable of capturing the complex and heterogeneous ecological interactions among mosquitoes, avian reservoirs, and human hosts, is both scientifically rigorous and conceptually sophisticated. Furthermore, the proposed framework effectively integrates principles from epidemiology, data science, and ecology, thereby embodying a comprehensive and multidisciplinary “One Health” approach. n my opinion, the manuscript requires minor corrections as suggested below: Comment 1: Some sentences are too long and syntactically complex, which can obscure meaning (e.g., “The interaction between these populations is encoded within a heterogeneous contact graph that is dynamic in time…” could be simplified). Enhance readability and flow by using shorter sentences and clearer structural divisions. Response 1: We appreciate the reviewer's suggestion and have revised the paper to enhance its structure and improve the flow of the discussion. Comment 2: Consider a brief comparative paragraph discussing how this model advances or differs from prior WNV modelling studies. Response 2: We thank the reviewer for this feedback. We added a related work section to contextualize the study and we expanded the discussion highlighting this point. Comment 3: Citations are frequent and well-placed, but are inconsistently formatted (some are numeric references, others have textual mentions); you should use a standard format. Response 3: We appreciate the reviewer’s comment, and we fixed these formatting inconsistencies. Comment 4: Some characters in the figures are unreadable (e.g. see Fig. 1, the second blue ball) Response 4: We thank the reviewer for the constructive feedback. We have recreated Figure 1 to enhance its precision and readability. Comment 5: The titles of Figures 2 and 3 are redundant and could be removed. Response 5: We thank the reviewer for this suggestion. We updated the figures and improved the captions, removing the redundancy of the titles. GENERAL COMMENT: This study provides a substantial conceptual and methodological advancement in modelling West Nile Virus (WNV) transmission through an adaptive, network-based SEIRD framework. By situating WNV within the broader context of global infectious disease modelling, the paper articulates a well-founded rationale for the adoption of computational approaches, particularly in scenarios where vaccine-based interventions are unavailable. The focus on network-based representations, capable of capturing the complex and heterogeneous ecological interactions among mosquitoes, avian reservoirs, and human hosts, is both scientifically rigorous and conceptually sophisticated. Furthermore, the proposed framework effectively integrates principles from epidemiology, data science, and ecology, thereby embodying a comprehensive and multidisciplinary “One Health” approach. n my opinion, the manuscript requires minor corrections as suggested below: Comment 1: Some sentences are too long and syntactically complex, which can obscure meaning (e.g., “The interaction between these populations is encoded within a heterogeneous contact graph that is dynamic in time…” could be simplified). Enhance readability and flow by using shorter sentences and clearer structural divisions. Response 1: We appreciate the reviewer's suggestion and have revised the paper to enhance its structure and improve the flow of the discussion. Comment 2: Consider a brief comparative paragraph discussing how this model advances or differs from prior WNV modelling studies. Response 2: We thank the reviewer for this feedback. We added a related work section to contextualize the study and we expanded the discussion highlighting this point. Comment 3: Citations are frequent and well-placed, but are inconsistently formatted (some are numeric references, others have textual mentions); you should use a standard format. Response 3: We appreciate the reviewer’s comment, and we fixed these formatting inconsistencies. Comment 4: Some characters in the figures are unreadable (e.g. see Fig. 1, the second blue ball) Response 4: We thank the reviewer for the constructive feedback. We have recreated Figure 1 to enhance its precision and readability. Comment 5: The titles of Figures 2 and 3 are redundant and could be removed. Response 5: We thank the reviewer for this suggestion. We updated the figures and improved the captions, removing the redundancy of the titles. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Wimberly M. Reviewer Report For: Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.5256/f1000research.186955.r417875 ) The direct URL for this report is: https://f1000research.com/articles/14-902/v1#referee-response-417875 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 15 Oct 2025 Michael Wimberly , The University of Oklahoma, Norman, Oklahoma, USA Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.186955.r417875 The idea of simulating West Nile virus using a mechanistic compartment model is interesting and has the potential to yield new insights into the drivers of transmission and the effectiveness of various treatment strategies. However, the manuscript in its current ... Continue reading READ ALL The idea of simulating West Nile virus using a mechanistic compartment model is interesting and has the potential to yield new insights into the drivers of transmission and the effectiveness of various treatment strategies. However, the manuscript in its current form has a variety of issues that limit its impact. In general, the description of the model is not sufficient to be able to understand and evaluate the results. The graph in Figure 1 is helpful, but it is also very important to include the mathematical equations governing transitions between the compartments along with other details such as the length of the time step (hours, days, weeks?). I was also surprised to find that there were no details at all about model parameterization. Were parameters obtained from the literature? Or were they estimated through a model fitting process? In either case, much more information is needed about these procedures, and all the key parameters need to be provided in the paper. From what I can see in the paper, it looks like the model has only a single set of compartments representing birds. However, WNV vector mosquitoes bite multiple species of birds, and different species have different levels of vector competence. Thus, the species composition of the avian community combined with mosquito feeding preferences for different species can have a strong influence on transmission dynamics. Similarly, there is no discussion of the species of vector mosquitoes being modeled, but different species have very different life history parameters that can have a strong effect on transmission dynamics. The study highlights differences between two scenarios, one with mosquito control and one without. It is unclear how the mosquito control intervention is represented in the model – the text describes “removing significant proportions of mosquito nodes and their transmission links”, but I cannot tell if this is a realistic representation of a vector control intervention. Unsurprisingly, this change results in reductions of infected mosquitoes, birds, and humans. Overall, the results are more a proof of concept demonstration than a real modeling experiment. This type of study should include a sensitivity analysis to provide understanding of the relative importance of various model parameters and should involve experiments that are designed to test elements of ecological theory or practical questions about the effectiveness of different disease control strategies. I would also note that this type of modeling approach is not unique. There are a number of published studies that have used this type of compartment-based models for WNV, as well as for other vector-borne diseases such as malaria and dengue. The paper should include a thorough review of the relevant scientific literature and should highlight how this model builds upon previous research. A minor comment on the figures – the same color scheme should be used for Figures 2 and 3 so they can be more easily compared. Is the rationale for developing the new method (or application) clearly explained? No Is the description of the method technically sound? No Are sufficient details provided to allow replication of the method development and its use by others? No If any results are presented, are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions about the method and its performance adequately supported by the findings presented in the article? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: Disease ecology and modeling I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Wimberly M. Reviewer Report For: Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.5256/f1000research.186955.r417875 ) The direct URL for this report is: https://f1000research.com/articles/14-902/v1#referee-response-417875 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 14 Nov 2025 Mohamed Mustaf Ahmed , Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia 14 Nov 2025 Author Response GENERAL COMMENT: The idea of simulating West Nile virus using a mechanistic compartment model is interesting and has the potential to yield new insights into the drivers of transmission and ... Continue reading GENERAL COMMENT: The idea of simulating West Nile virus using a mechanistic compartment model is interesting and has the potential to yield new insights into the drivers of transmission and the effectiveness of various treatment strategies. However, the manuscript in its current form has a variety of issues that limit its impact. Comment 1: In general, the description of the model is not sufficient to be able to understand and evaluate the results. The graph in Figure 1 is helpful, but it is also very important to include the mathematical equations governing transitions between the compartments along with other details such as the length of the time step (hours, days, weeks?). Response 1: We thank the reviewer for this important feedback. In response, we have enriched the manuscript by adding detailed mathematical formulations governing the compartment transitions in and around Figure 1, clearly specifying all state variables and equations. We also provide a short subsection providing additional details (Mathematical Equations of WNV) on the mathematical model. Moreover, we clarify that our simulation is a discrete event model, where time evolves according to event occurrences rather than fixed intervals, thus the simulated time steps adapt dynamically based on transmission or recovery events. Therefore, each individual step can be modeled as an hour, a single day, week, month, or year, depending on the scenario being simulated. Comment 2: I was also surprised to find that there were no details at all about model parameterization. Were parameters obtained from the literature? Or were they estimated through a model fitting process? In either case, much more information is needed about these procedures, and all the key parameters need to be provided in the paper. Response 2: We thank the reviewer for pointing out this aspect. Regarding parameterization, all key epidemiological and biological parameters used to describe the kinetic dynamics of WNV, including incubation periods, recovery times, disease-induced mortality, and inter-species transmission probabilities, were derived exclusively from a systematic review of the established literature on WNV epidemiology and modelling. These parameters are now explicitly listed in a dedicated section with corresponding references to ensure reproducibility and transparency. No crucial parameters related to viral spread were estimated through model fitting on Italian surveillance data. Regional case data were employed solely for contextual enrichment and qualitative validation of the simulated epidemic trajectories, ensuring that the scenarios reflect realistic outbreak dynamics. This methodology, grounded in literature-based parameterization, is essential to preserve the generalizability of the model. Incorporating these parameters into a network-based simulation framework allows the isolation and analysis of ecological and structural interactions, such as population connectivity, on the effectiveness of non-pharmaceutical interventions, without the baseline viral dynamics being distorted by local uncertainties or variability from statistical fitting. Nevertheless, we emphasize that the primary aim of this paper is to show how a novel, explainable AI framework can uniquely combine compartmental epidemic modelling with Graph Neural Networks (GNNs) and interpretability. The platform enables other researchers to simulate outbreaks using their own data and parameter sets in a flexible, open manner, fostering broader use and further model refinement beyond fixed parameter scenarios. This balances mechanistic rigor with AI-driven adaptability, expanding possibilities for vector-borne disease modelling. Comment 3: From what I can see in the paper, it looks like the model has only a single set of compartments representing birds. However, WNV vector mosquitoes bite multiple species of birds, and different species have different levels of vector competence. Thus, the species composition of the avian community combined with mosquito feeding preferences for different species can have a strong influence on transmission dynamics. Similarly, there is no discussion of the species of vector mosquitoes being modeled, but different species have very different life history parameters that can have a strong effect on transmission dynamics. Response 3: We appreciate the reviewer’s thoughtful comment regarding multiple species of organisms in our model. We acknowledge that one significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. Therefore, the primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. Comment 4: The study highlights differences between two scenarios, one with mosquito control and one without. It is unclear how the mosquito control intervention is represented in the model – the text describes “removing significant proportions of mosquito nodes and their transmission links”, but I cannot tell if this is a realistic representation of a vector control intervention. Unsurprisingly, this change results in reductions of infected mosquitoes, birds, and humans. Response 4: We thank the reviewer for these highlights. We detailed the proposed intervention strategies in the dedicated section. Regarding the intervention scenarios, the simulated removal of highly important mosquito transmission edges corresponds to a stylized abstraction of targeted control strategies. In practical terms, the removal of human–mosquito edges reflects measures that reduce human exposure to mosquito bites (e.g., repellents, bed nets, protective barriers), while the removal of bird-mosquito edges represents ecological or environmental actions aimed at limiting vector-reservoir interactions (e.g., habitat management, avifauna control, population monitoring). Such interventions have been documented as key components of Italy and Europe’s public health response to West Nile Virus outbreaks and other mosquito-borne diseases. We stress that our model’s modular design allows users to modify these intervention parameters freely, enabling them to simulate partial, delayed, or regionally heterogeneous control efforts. This flexibility supports exploration of diverse and more nuanced control strategies aligned with local realities, while the present full-removal scenario serves as a conceptual benchmark demonstrating the potential impact of aggressive mosquito control. Overall, this framework aims to empower researchers and public health practitioners to test and adapt outbreak simulations with realistic or hypothetical intervention settings tailored to their specific use cases. Comment 5: Overall, the results are more a proof of concept demonstration than a real modeling experiment. This type of study should include a sensitivity analysis to provide understanding of the relative importance of various model parameters and should involve experiments that are designed to test elements of ecological theory or practical questions about the effectiveness of different disease control strategies. Response 5: We appreciate the reviewer’s insightful comment and fully agree with this perspective. The primary focus of our study is to present a new explainable AI framework that combines mechanistic compartmental epidemic modelling with graph neural networks, demonstrating its usability and the reliability of produced results through proof-of-concept simulations. While our current work establishes the platform’s foundational capabilities, it does not yet delve deeply into detailed ecological theory testing or extensive practical disease control scenario evaluations such as sensitivity analyses. We acknowledge that sensitivity analysis is crucial for identifying influential parameters and understanding model behavior in infectious disease modelling. Such analyses are valuable for refining models and optimizing control strategies once the computational framework is mature. Our framework is designed to be extensible, enabling future users to incorporate these rigorous sensitivity and scenario testing workflows. Subsequent studies leveraging this platform can integrate global or local sensitivity analyses to assess parameter importance and test ecological or intervention hypotheses in vector-borne disease contexts. Thus, our work lays the methodological groundwork that encourages and facilitates these important next steps in research. Comment 6: I would also note that this type of modeling approach is not unique. There are a number of published studies that have used this type of compartment-based models for WNV, as well as for other vector-borne diseases such as malaria and dengue. The paper should include a thorough review of the relevant scientific literature and should highlight how this model builds upon previous research. Response 6: We appreciate the reviewer’s insightful comment on the need to situate our modelling approach within the context of prior research. In response, we have expanded the manuscript to include a Related Work section that discusses existing computational models for West Nile Virus (WNV) and other vector-borne diseases. Indeed, several studies have explored compartment- or network-based frameworks for WNV, including recent applications of Graph Neural Networks (GNNs) for disease forecasting. For example, Tonks et al. (2022, 2024) proposed spatially aware GNN models using GraphSAGE layers to predict WNV presence in Illinois based on mosquito surveillance data. Similarly, Bonicelli et al. (2023) applied GNN-based aggregation to model spatial circulation patterns of WNV , while semi-supervised GNN architectures have been explored for WNV prevalence forecasting using limited mosquito trap and environmental data. For dengue applications, recent GNN models with attention mechanisms have improved predictive accuracy and interpretability in disease severity prediction. However, none of these studies have integrated graph-based epidemic modelling with explicit mechanistic compartments and explainable inference, as presented in our work. Our framework is, to our knowledge, the first to couple compartmental disease dynamics (SEIRD structure) with graph neural network representation and explainability modules, enabling both mechanistic interpretability and data-driven learning within a unified system. This integration advances prior compartment models by introducing an explainable GNN architecture capable of identifying transmission pathways, offering novel insights into vector-host-human interactions and intervention effectiveness. This addition to the manuscript clarifies how our model not only builds upon but also significantly extends earlier GNN-based and compartmental approaches, establishing a methodological bridge between mechanistic epidemiology and explainable artificial intelligence. Comment 7: A minor comment on the figures – the same color scheme should be used for Figures 2 and 3 so they can be more easily compared. Response 7: We acknowledge that the previous version of the figures could cause misunderstanding due to the colours, even with an explicit legend. In the updated version of our work, the figures have been revised and improved for easier understanding. GENERAL COMMENT: The idea of simulating West Nile virus using a mechanistic compartment model is interesting and has the potential to yield new insights into the drivers of transmission and the effectiveness of various treatment strategies. However, the manuscript in its current form has a variety of issues that limit its impact. Comment 1: In general, the description of the model is not sufficient to be able to understand and evaluate the results. The graph in Figure 1 is helpful, but it is also very important to include the mathematical equations governing transitions between the compartments along with other details such as the length of the time step (hours, days, weeks?). Response 1: We thank the reviewer for this important feedback. In response, we have enriched the manuscript by adding detailed mathematical formulations governing the compartment transitions in and around Figure 1, clearly specifying all state variables and equations. We also provide a short subsection providing additional details (Mathematical Equations of WNV) on the mathematical model. Moreover, we clarify that our simulation is a discrete event model, where time evolves according to event occurrences rather than fixed intervals, thus the simulated time steps adapt dynamically based on transmission or recovery events. Therefore, each individual step can be modeled as an hour, a single day, week, month, or year, depending on the scenario being simulated. Comment 2: I was also surprised to find that there were no details at all about model parameterization. Were parameters obtained from the literature? Or were they estimated through a model fitting process? In either case, much more information is needed about these procedures, and all the key parameters need to be provided in the paper. Response 2: We thank the reviewer for pointing out this aspect. Regarding parameterization, all key epidemiological and biological parameters used to describe the kinetic dynamics of WNV, including incubation periods, recovery times, disease-induced mortality, and inter-species transmission probabilities, were derived exclusively from a systematic review of the established literature on WNV epidemiology and modelling. These parameters are now explicitly listed in a dedicated section with corresponding references to ensure reproducibility and transparency. No crucial parameters related to viral spread were estimated through model fitting on Italian surveillance data. Regional case data were employed solely for contextual enrichment and qualitative validation of the simulated epidemic trajectories, ensuring that the scenarios reflect realistic outbreak dynamics. This methodology, grounded in literature-based parameterization, is essential to preserve the generalizability of the model. Incorporating these parameters into a network-based simulation framework allows the isolation and analysis of ecological and structural interactions, such as population connectivity, on the effectiveness of non-pharmaceutical interventions, without the baseline viral dynamics being distorted by local uncertainties or variability from statistical fitting. Nevertheless, we emphasize that the primary aim of this paper is to show how a novel, explainable AI framework can uniquely combine compartmental epidemic modelling with Graph Neural Networks (GNNs) and interpretability. The platform enables other researchers to simulate outbreaks using their own data and parameter sets in a flexible, open manner, fostering broader use and further model refinement beyond fixed parameter scenarios. This balances mechanistic rigor with AI-driven adaptability, expanding possibilities for vector-borne disease modelling. Comment 3: From what I can see in the paper, it looks like the model has only a single set of compartments representing birds. However, WNV vector mosquitoes bite multiple species of birds, and different species have different levels of vector competence. Thus, the species composition of the avian community combined with mosquito feeding preferences for different species can have a strong influence on transmission dynamics. Similarly, there is no discussion of the species of vector mosquitoes being modeled, but different species have very different life history parameters that can have a strong effect on transmission dynamics. Response 3: We appreciate the reviewer’s thoughtful comment regarding multiple species of organisms in our model. We acknowledge that one significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. Therefore, the primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. Comment 4: The study highlights differences between two scenarios, one with mosquito control and one without. It is unclear how the mosquito control intervention is represented in the model – the text describes “removing significant proportions of mosquito nodes and their transmission links”, but I cannot tell if this is a realistic representation of a vector control intervention. Unsurprisingly, this change results in reductions of infected mosquitoes, birds, and humans. Response 4: We thank the reviewer for these highlights. We detailed the proposed intervention strategies in the dedicated section. Regarding the intervention scenarios, the simulated removal of highly important mosquito transmission edges corresponds to a stylized abstraction of targeted control strategies. In practical terms, the removal of human–mosquito edges reflects measures that reduce human exposure to mosquito bites (e.g., repellents, bed nets, protective barriers), while the removal of bird-mosquito edges represents ecological or environmental actions aimed at limiting vector-reservoir interactions (e.g., habitat management, avifauna control, population monitoring). Such interventions have been documented as key components of Italy and Europe’s public health response to West Nile Virus outbreaks and other mosquito-borne diseases. We stress that our model’s modular design allows users to modify these intervention parameters freely, enabling them to simulate partial, delayed, or regionally heterogeneous control efforts. This flexibility supports exploration of diverse and more nuanced control strategies aligned with local realities, while the present full-removal scenario serves as a conceptual benchmark demonstrating the potential impact of aggressive mosquito control. Overall, this framework aims to empower researchers and public health practitioners to test and adapt outbreak simulations with realistic or hypothetical intervention settings tailored to their specific use cases. Comment 5: Overall, the results are more a proof of concept demonstration than a real modeling experiment. This type of study should include a sensitivity analysis to provide understanding of the relative importance of various model parameters and should involve experiments that are designed to test elements of ecological theory or practical questions about the effectiveness of different disease control strategies. Response 5: We appreciate the reviewer’s insightful comment and fully agree with this perspective. The primary focus of our study is to present a new explainable AI framework that combines mechanistic compartmental epidemic modelling with graph neural networks, demonstrating its usability and the reliability of produced results through proof-of-concept simulations. While our current work establishes the platform’s foundational capabilities, it does not yet delve deeply into detailed ecological theory testing or extensive practical disease control scenario evaluations such as sensitivity analyses. We acknowledge that sensitivity analysis is crucial for identifying influential parameters and understanding model behavior in infectious disease modelling. Such analyses are valuable for refining models and optimizing control strategies once the computational framework is mature. Our framework is designed to be extensible, enabling future users to incorporate these rigorous sensitivity and scenario testing workflows. Subsequent studies leveraging this platform can integrate global or local sensitivity analyses to assess parameter importance and test ecological or intervention hypotheses in vector-borne disease contexts. Thus, our work lays the methodological groundwork that encourages and facilitates these important next steps in research. Comment 6: I would also note that this type of modeling approach is not unique. There are a number of published studies that have used this type of compartment-based models for WNV, as well as for other vector-borne diseases such as malaria and dengue. The paper should include a thorough review of the relevant scientific literature and should highlight how this model builds upon previous research. Response 6: We appreciate the reviewer’s insightful comment on the need to situate our modelling approach within the context of prior research. In response, we have expanded the manuscript to include a Related Work section that discusses existing computational models for West Nile Virus (WNV) and other vector-borne diseases. Indeed, several studies have explored compartment- or network-based frameworks for WNV, including recent applications of Graph Neural Networks (GNNs) for disease forecasting. For example, Tonks et al. (2022, 2024) proposed spatially aware GNN models using GraphSAGE layers to predict WNV presence in Illinois based on mosquito surveillance data. Similarly, Bonicelli et al. (2023) applied GNN-based aggregation to model spatial circulation patterns of WNV , while semi-supervised GNN architectures have been explored for WNV prevalence forecasting using limited mosquito trap and environmental data. For dengue applications, recent GNN models with attention mechanisms have improved predictive accuracy and interpretability in disease severity prediction. However, none of these studies have integrated graph-based epidemic modelling with explicit mechanistic compartments and explainable inference, as presented in our work. Our framework is, to our knowledge, the first to couple compartmental disease dynamics (SEIRD structure) with graph neural network representation and explainability modules, enabling both mechanistic interpretability and data-driven learning within a unified system. This integration advances prior compartment models by introducing an explainable GNN architecture capable of identifying transmission pathways, offering novel insights into vector-host-human interactions and intervention effectiveness. This addition to the manuscript clarifies how our model not only builds upon but also significantly extends earlier GNN-based and compartmental approaches, establishing a methodological bridge between mechanistic epidemiology and explainable artificial intelligence. Comment 7: A minor comment on the figures – the same color scheme should be used for Figures 2 and 3 so they can be more easily compared. Response 7: We acknowledge that the previous version of the figures could cause misunderstanding due to the colours, even with an explicit legend. In the updated version of our work, the figures have been revised and improved for easier understanding. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 14 Nov 2025 Mohamed Mustaf Ahmed , Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia 14 Nov 2025 Author Response GENERAL COMMENT: The idea of simulating West Nile virus using a mechanistic compartment model is interesting and has the potential to yield new insights into the drivers of transmission and ... Continue reading GENERAL COMMENT: The idea of simulating West Nile virus using a mechanistic compartment model is interesting and has the potential to yield new insights into the drivers of transmission and the effectiveness of various treatment strategies. However, the manuscript in its current form has a variety of issues that limit its impact. Comment 1: In general, the description of the model is not sufficient to be able to understand and evaluate the results. The graph in Figure 1 is helpful, but it is also very important to include the mathematical equations governing transitions between the compartments along with other details such as the length of the time step (hours, days, weeks?). Response 1: We thank the reviewer for this important feedback. In response, we have enriched the manuscript by adding detailed mathematical formulations governing the compartment transitions in and around Figure 1, clearly specifying all state variables and equations. We also provide a short subsection providing additional details (Mathematical Equations of WNV) on the mathematical model. Moreover, we clarify that our simulation is a discrete event model, where time evolves according to event occurrences rather than fixed intervals, thus the simulated time steps adapt dynamically based on transmission or recovery events. Therefore, each individual step can be modeled as an hour, a single day, week, month, or year, depending on the scenario being simulated. Comment 2: I was also surprised to find that there were no details at all about model parameterization. Were parameters obtained from the literature? Or were they estimated through a model fitting process? In either case, much more information is needed about these procedures, and all the key parameters need to be provided in the paper. Response 2: We thank the reviewer for pointing out this aspect. Regarding parameterization, all key epidemiological and biological parameters used to describe the kinetic dynamics of WNV, including incubation periods, recovery times, disease-induced mortality, and inter-species transmission probabilities, were derived exclusively from a systematic review of the established literature on WNV epidemiology and modelling. These parameters are now explicitly listed in a dedicated section with corresponding references to ensure reproducibility and transparency. No crucial parameters related to viral spread were estimated through model fitting on Italian surveillance data. Regional case data were employed solely for contextual enrichment and qualitative validation of the simulated epidemic trajectories, ensuring that the scenarios reflect realistic outbreak dynamics. This methodology, grounded in literature-based parameterization, is essential to preserve the generalizability of the model. Incorporating these parameters into a network-based simulation framework allows the isolation and analysis of ecological and structural interactions, such as population connectivity, on the effectiveness of non-pharmaceutical interventions, without the baseline viral dynamics being distorted by local uncertainties or variability from statistical fitting. Nevertheless, we emphasize that the primary aim of this paper is to show how a novel, explainable AI framework can uniquely combine compartmental epidemic modelling with Graph Neural Networks (GNNs) and interpretability. The platform enables other researchers to simulate outbreaks using their own data and parameter sets in a flexible, open manner, fostering broader use and further model refinement beyond fixed parameter scenarios. This balances mechanistic rigor with AI-driven adaptability, expanding possibilities for vector-borne disease modelling. Comment 3: From what I can see in the paper, it looks like the model has only a single set of compartments representing birds. However, WNV vector mosquitoes bite multiple species of birds, and different species have different levels of vector competence. Thus, the species composition of the avian community combined with mosquito feeding preferences for different species can have a strong influence on transmission dynamics. Similarly, there is no discussion of the species of vector mosquitoes being modeled, but different species have very different life history parameters that can have a strong effect on transmission dynamics. Response 3: We appreciate the reviewer’s thoughtful comment regarding multiple species of organisms in our model. We acknowledge that one significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. Therefore, the primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. Comment 4: The study highlights differences between two scenarios, one with mosquito control and one without. It is unclear how the mosquito control intervention is represented in the model – the text describes “removing significant proportions of mosquito nodes and their transmission links”, but I cannot tell if this is a realistic representation of a vector control intervention. Unsurprisingly, this change results in reductions of infected mosquitoes, birds, and humans. Response 4: We thank the reviewer for these highlights. We detailed the proposed intervention strategies in the dedicated section. Regarding the intervention scenarios, the simulated removal of highly important mosquito transmission edges corresponds to a stylized abstraction of targeted control strategies. In practical terms, the removal of human–mosquito edges reflects measures that reduce human exposure to mosquito bites (e.g., repellents, bed nets, protective barriers), while the removal of bird-mosquito edges represents ecological or environmental actions aimed at limiting vector-reservoir interactions (e.g., habitat management, avifauna control, population monitoring). Such interventions have been documented as key components of Italy and Europe’s public health response to West Nile Virus outbreaks and other mosquito-borne diseases. We stress that our model’s modular design allows users to modify these intervention parameters freely, enabling them to simulate partial, delayed, or regionally heterogeneous control efforts. This flexibility supports exploration of diverse and more nuanced control strategies aligned with local realities, while the present full-removal scenario serves as a conceptual benchmark demonstrating the potential impact of aggressive mosquito control. Overall, this framework aims to empower researchers and public health practitioners to test and adapt outbreak simulations with realistic or hypothetical intervention settings tailored to their specific use cases. Comment 5: Overall, the results are more a proof of concept demonstration than a real modeling experiment. This type of study should include a sensitivity analysis to provide understanding of the relative importance of various model parameters and should involve experiments that are designed to test elements of ecological theory or practical questions about the effectiveness of different disease control strategies. Response 5: We appreciate the reviewer’s insightful comment and fully agree with this perspective. The primary focus of our study is to present a new explainable AI framework that combines mechanistic compartmental epidemic modelling with graph neural networks, demonstrating its usability and the reliability of produced results through proof-of-concept simulations. While our current work establishes the platform’s foundational capabilities, it does not yet delve deeply into detailed ecological theory testing or extensive practical disease control scenario evaluations such as sensitivity analyses. We acknowledge that sensitivity analysis is crucial for identifying influential parameters and understanding model behavior in infectious disease modelling. Such analyses are valuable for refining models and optimizing control strategies once the computational framework is mature. Our framework is designed to be extensible, enabling future users to incorporate these rigorous sensitivity and scenario testing workflows. Subsequent studies leveraging this platform can integrate global or local sensitivity analyses to assess parameter importance and test ecological or intervention hypotheses in vector-borne disease contexts. Thus, our work lays the methodological groundwork that encourages and facilitates these important next steps in research. Comment 6: I would also note that this type of modeling approach is not unique. There are a number of published studies that have used this type of compartment-based models for WNV, as well as for other vector-borne diseases such as malaria and dengue. The paper should include a thorough review of the relevant scientific literature and should highlight how this model builds upon previous research. Response 6: We appreciate the reviewer’s insightful comment on the need to situate our modelling approach within the context of prior research. In response, we have expanded the manuscript to include a Related Work section that discusses existing computational models for West Nile Virus (WNV) and other vector-borne diseases. Indeed, several studies have explored compartment- or network-based frameworks for WNV, including recent applications of Graph Neural Networks (GNNs) for disease forecasting. For example, Tonks et al. (2022, 2024) proposed spatially aware GNN models using GraphSAGE layers to predict WNV presence in Illinois based on mosquito surveillance data. Similarly, Bonicelli et al. (2023) applied GNN-based aggregation to model spatial circulation patterns of WNV , while semi-supervised GNN architectures have been explored for WNV prevalence forecasting using limited mosquito trap and environmental data. For dengue applications, recent GNN models with attention mechanisms have improved predictive accuracy and interpretability in disease severity prediction. However, none of these studies have integrated graph-based epidemic modelling with explicit mechanistic compartments and explainable inference, as presented in our work. Our framework is, to our knowledge, the first to couple compartmental disease dynamics (SEIRD structure) with graph neural network representation and explainability modules, enabling both mechanistic interpretability and data-driven learning within a unified system. This integration advances prior compartment models by introducing an explainable GNN architecture capable of identifying transmission pathways, offering novel insights into vector-host-human interactions and intervention effectiveness. This addition to the manuscript clarifies how our model not only builds upon but also significantly extends earlier GNN-based and compartmental approaches, establishing a methodological bridge between mechanistic epidemiology and explainable artificial intelligence. Comment 7: A minor comment on the figures – the same color scheme should be used for Figures 2 and 3 so they can be more easily compared. Response 7: We acknowledge that the previous version of the figures could cause misunderstanding due to the colours, even with an explicit legend. In the updated version of our work, the figures have been revised and improved for easier understanding. GENERAL COMMENT: The idea of simulating West Nile virus using a mechanistic compartment model is interesting and has the potential to yield new insights into the drivers of transmission and the effectiveness of various treatment strategies. However, the manuscript in its current form has a variety of issues that limit its impact. Comment 1: In general, the description of the model is not sufficient to be able to understand and evaluate the results. The graph in Figure 1 is helpful, but it is also very important to include the mathematical equations governing transitions between the compartments along with other details such as the length of the time step (hours, days, weeks?). Response 1: We thank the reviewer for this important feedback. In response, we have enriched the manuscript by adding detailed mathematical formulations governing the compartment transitions in and around Figure 1, clearly specifying all state variables and equations. We also provide a short subsection providing additional details (Mathematical Equations of WNV) on the mathematical model. Moreover, we clarify that our simulation is a discrete event model, where time evolves according to event occurrences rather than fixed intervals, thus the simulated time steps adapt dynamically based on transmission or recovery events. Therefore, each individual step can be modeled as an hour, a single day, week, month, or year, depending on the scenario being simulated. Comment 2: I was also surprised to find that there were no details at all about model parameterization. Were parameters obtained from the literature? Or were they estimated through a model fitting process? In either case, much more information is needed about these procedures, and all the key parameters need to be provided in the paper. Response 2: We thank the reviewer for pointing out this aspect. Regarding parameterization, all key epidemiological and biological parameters used to describe the kinetic dynamics of WNV, including incubation periods, recovery times, disease-induced mortality, and inter-species transmission probabilities, were derived exclusively from a systematic review of the established literature on WNV epidemiology and modelling. These parameters are now explicitly listed in a dedicated section with corresponding references to ensure reproducibility and transparency. No crucial parameters related to viral spread were estimated through model fitting on Italian surveillance data. Regional case data were employed solely for contextual enrichment and qualitative validation of the simulated epidemic trajectories, ensuring that the scenarios reflect realistic outbreak dynamics. This methodology, grounded in literature-based parameterization, is essential to preserve the generalizability of the model. Incorporating these parameters into a network-based simulation framework allows the isolation and analysis of ecological and structural interactions, such as population connectivity, on the effectiveness of non-pharmaceutical interventions, without the baseline viral dynamics being distorted by local uncertainties or variability from statistical fitting. Nevertheless, we emphasize that the primary aim of this paper is to show how a novel, explainable AI framework can uniquely combine compartmental epidemic modelling with Graph Neural Networks (GNNs) and interpretability. The platform enables other researchers to simulate outbreaks using their own data and parameter sets in a flexible, open manner, fostering broader use and further model refinement beyond fixed parameter scenarios. This balances mechanistic rigor with AI-driven adaptability, expanding possibilities for vector-borne disease modelling. Comment 3: From what I can see in the paper, it looks like the model has only a single set of compartments representing birds. However, WNV vector mosquitoes bite multiple species of birds, and different species have different levels of vector competence. Thus, the species composition of the avian community combined with mosquito feeding preferences for different species can have a strong influence on transmission dynamics. Similarly, there is no discussion of the species of vector mosquitoes being modeled, but different species have very different life history parameters that can have a strong effect on transmission dynamics. Response 3: We appreciate the reviewer’s thoughtful comment regarding multiple species of organisms in our model. We acknowledge that one significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. Therefore, the primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. Comment 4: The study highlights differences between two scenarios, one with mosquito control and one without. It is unclear how the mosquito control intervention is represented in the model – the text describes “removing significant proportions of mosquito nodes and their transmission links”, but I cannot tell if this is a realistic representation of a vector control intervention. Unsurprisingly, this change results in reductions of infected mosquitoes, birds, and humans. Response 4: We thank the reviewer for these highlights. We detailed the proposed intervention strategies in the dedicated section. Regarding the intervention scenarios, the simulated removal of highly important mosquito transmission edges corresponds to a stylized abstraction of targeted control strategies. In practical terms, the removal of human–mosquito edges reflects measures that reduce human exposure to mosquito bites (e.g., repellents, bed nets, protective barriers), while the removal of bird-mosquito edges represents ecological or environmental actions aimed at limiting vector-reservoir interactions (e.g., habitat management, avifauna control, population monitoring). Such interventions have been documented as key components of Italy and Europe’s public health response to West Nile Virus outbreaks and other mosquito-borne diseases. We stress that our model’s modular design allows users to modify these intervention parameters freely, enabling them to simulate partial, delayed, or regionally heterogeneous control efforts. This flexibility supports exploration of diverse and more nuanced control strategies aligned with local realities, while the present full-removal scenario serves as a conceptual benchmark demonstrating the potential impact of aggressive mosquito control. Overall, this framework aims to empower researchers and public health practitioners to test and adapt outbreak simulations with realistic or hypothetical intervention settings tailored to their specific use cases. Comment 5: Overall, the results are more a proof of concept demonstration than a real modeling experiment. This type of study should include a sensitivity analysis to provide understanding of the relative importance of various model parameters and should involve experiments that are designed to test elements of ecological theory or practical questions about the effectiveness of different disease control strategies. Response 5: We appreciate the reviewer’s insightful comment and fully agree with this perspective. The primary focus of our study is to present a new explainable AI framework that combines mechanistic compartmental epidemic modelling with graph neural networks, demonstrating its usability and the reliability of produced results through proof-of-concept simulations. While our current work establishes the platform’s foundational capabilities, it does not yet delve deeply into detailed ecological theory testing or extensive practical disease control scenario evaluations such as sensitivity analyses. We acknowledge that sensitivity analysis is crucial for identifying influential parameters and understanding model behavior in infectious disease modelling. Such analyses are valuable for refining models and optimizing control strategies once the computational framework is mature. Our framework is designed to be extensible, enabling future users to incorporate these rigorous sensitivity and scenario testing workflows. Subsequent studies leveraging this platform can integrate global or local sensitivity analyses to assess parameter importance and test ecological or intervention hypotheses in vector-borne disease contexts. Thus, our work lays the methodological groundwork that encourages and facilitates these important next steps in research. Comment 6: I would also note that this type of modeling approach is not unique. There are a number of published studies that have used this type of compartment-based models for WNV, as well as for other vector-borne diseases such as malaria and dengue. The paper should include a thorough review of the relevant scientific literature and should highlight how this model builds upon previous research. Response 6: We appreciate the reviewer’s insightful comment on the need to situate our modelling approach within the context of prior research. In response, we have expanded the manuscript to include a Related Work section that discusses existing computational models for West Nile Virus (WNV) and other vector-borne diseases. Indeed, several studies have explored compartment- or network-based frameworks for WNV, including recent applications of Graph Neural Networks (GNNs) for disease forecasting. For example, Tonks et al. (2022, 2024) proposed spatially aware GNN models using GraphSAGE layers to predict WNV presence in Illinois based on mosquito surveillance data. Similarly, Bonicelli et al. (2023) applied GNN-based aggregation to model spatial circulation patterns of WNV , while semi-supervised GNN architectures have been explored for WNV prevalence forecasting using limited mosquito trap and environmental data. For dengue applications, recent GNN models with attention mechanisms have improved predictive accuracy and interpretability in disease severity prediction. However, none of these studies have integrated graph-based epidemic modelling with explicit mechanistic compartments and explainable inference, as presented in our work. Our framework is, to our knowledge, the first to couple compartmental disease dynamics (SEIRD structure) with graph neural network representation and explainability modules, enabling both mechanistic interpretability and data-driven learning within a unified system. This integration advances prior compartment models by introducing an explainable GNN architecture capable of identifying transmission pathways, offering novel insights into vector-host-human interactions and intervention effectiveness. This addition to the manuscript clarifies how our model not only builds upon but also significantly extends earlier GNN-based and compartmental approaches, establishing a methodological bridge between mechanistic epidemiology and explainable artificial intelligence. Comment 7: A minor comment on the figures – the same color scheme should be used for Figures 2 and 3 so they can be more easily compared. Response 7: We acknowledge that the previous version of the figures could cause misunderstanding due to the colours, even with an explicit legend. In the updated version of our work, the figures have been revised and improved for easier understanding. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 10 Sep 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 3 (revision) 19 Dec 25 Version 2 (revision) 14 Nov 25 read Version 1 10 Sep 25 read read read Michael Wimberly , The University of Oklahoma, Norman, USA Laura Antonelli , Istituto of High Performance Computing and Networks, National Research Council, Via P. Castellino, Italy Katrin Gaardbo Kuhn , The University of Oklahoma Health Sciences Center, Oklahoma City, USA Gargi Deshpande , The University of Oklahoma Health Sciences Center, Oklahoma City, USA Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Wimberly M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 22 Nov 2025 | for Version 2 Michael Wimberly , The University of Oklahoma, Norman, Oklahoma, USA 0 Views copyright © 2025 Wimberly M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors have made substantial modifications in response to the reviewer comments, and the readability of the paper, the interpretability of the results, and the relevance of the work to the broader field of disease ecology and modeling are all greatly enhanced as a result. Competing Interests No competing interests were disclosed. Reviewer Expertise Disease ecology and modeling I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Wimberly M. Peer Review Report For: Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.5256/f1000research.190502.r432695) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-902/v2#referee-response-432695 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Kuhn K et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 24 Nov 2025 | for Version 1 Katrin Gaardbo Kuhn , The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA Gargi Deshpande , Biostatistics and Epidemiology, The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA 0 Views copyright © 2025 Kuhn K et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Graph-based epidemic modeling of West Nile Virus: Forecasting and containment The manuscript proposes a simulation framework for WNV transmission and the impact of prevention strategies on transmission dynamics using a network-based SEIRD model. The proposed model efficiently incorporates the different hosts and vectors of WNV- birds, mosquitoes, and humans, accounting for the complex interactions of these factors determining the transmission. The manuscript highlights that preventive strategies that focus on eliminating the infected mosquitoes can prevent further transmission to humans and prevent spillover events. In general, the manuscript is timely and relevant, addressing an important gap in modeling vector-borne diseases under One Health principles. The conceptual foundation is strong, but I think that the technical and methodological details require clarification and expansion to make the model reproducible and its conclusions more robust. Recommendation: Major revisions required General Comments: I commend the authors for constructing a true One Health modeling approach; this is a much-needed step to better understand the transmission of zoonotic diseases. Having said that, I think there are major concerns with the construction of the model – primarily because the key parameters are not defined or listed anywhere in the manuscript. Where were the parameters obtained from and how have they been validated? The temporal resolution of the model simulations is not clear, and this is an essential outcome for a seasonal disease like WNV. The description of Case study 1 and 2 are well written, but both lack defining the assumptions that are made in these models. Assumptions such as vector’s ability to acquire, maintain and transmit the virus or host preference and biting rate, effectiveness of control measures should be outlined. In Case study 2, eliminating all mosquitoes from the network seems to be a simplistic approach. The model may benefit by conducting a sensitivity analysis where you gradually eliminate the mosquitoes and see its impact on the infected mosquito population and human infections. Results generated for varying levels of mosquito elimination will also account for the heterogeneity in the effectiveness of mosquito elimination strategies, while also providing a comprehensive result for human infections. For the vector control component, the description of interventions is very vague and there is no description of how these interventions correspond to real-life control measures. Also, to which extent is the approach scalable? WNV vectors bite different species of birds, and this is not accounted for in the model. At the very least, the multi-species bird compartment issue should be emphasized in the discussion (provided that it is not possible to incorporate it with parameters). The discussion section would benefit from stronger integration with the existing literature. Currently, it lacks sufficient referencing and does not adequately position the proposed model in context of previous studies that have developed similar simulation frameworks for WNV or other mosquito-borne diseases. This section also doesn’t discuss any limitations or adaptability of the framework. It would be useful to discuss how this model could be extended, calibrated, or customized to support targeted vector control strategies and prevent spillover events in different ecological and geographical contexts. Minor Comments: Some sentences are overly complex and could be split for clarity (especially in the Introduction and Methods sections). Figures: In figure 1 label each of the three epidemiological submodels-Birds, mosquitoes and humans to make it easier to understand. Also, improve readability (labels are small). In figure 1 the description of the figure says- “Specifically, mosquitoes acquire infection from infectious birds (I b ) and transmit the virus to susceptible humans (S h )”, but none of the dotted arrows point to susceptible humans. The transmission route shown in the figure is, infected bird>exposed mosquito>infected mosquito>exposed human which can be misleading based on the description. Harmonize color schemes between Figures 2 and 3. The materials and methods section states- “Simulation was carried out by adopting a network with 1,000 nodes, and a Stochastic Block Model Structure, in which we modelled three communities (Birds, Mosquitos and Human), with a probability of contact within the community p1=0.8 and a probability of contacts between the communities p2=0.2.” Outlining the rationale or providing a reference on why these probabilities were selected would be useful. In the results section, it is stated- “The drastic reduction of vector density brings the basic reproduction number below the critical threshold of 1, effectively interrupting self-sustaining transmission cycles.” Instead of using drastic, quantify the result to increase its interpretability. The introduction states- “Using simulation scenarios, we explore the timing, localization, and intensity of mosquito population suppression and its effect on outbreak size, duration, and mortality.” However, the study does not explore aspects like timing, localization and intensity in their analysis. Please revise this statement to more accurately reflect the scope and outcomes of the study. Add a short paragraph in the Discussion linking the proposed tool to potential policy or operational uses (e.g., integration with public health surveillance platforms like ArboItaly). Is the rationale for developing the new method (or application) clearly explained? Partly Is the description of the method technically sound? Yes Are sufficient details provided to allow replication of the method development and its use by others? No If any results are presented, are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Vector-borne diseases. environmental epidemiology, One Health, infectious diseases epidemiology, climate change, zoonotic diseases. We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 10 Dec 2025 Mohamed Mustaf Ahmed, Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia General Comments: 1. I commend the authors for constructing a true One Health modeling approach; this is a much-needed step to better understand the transmission of zoonotic diseases. Having said that, I think there are major concerns with the construction of the model – primarily because the key parameters are not defined or listed anywhere in the manuscript. Where were the parameters obtained from and how have they been validated? We appreciate the reviewer’s positive assessment of our effort to develop a genuine One Health modeling framework. In the revised version of the manuscript, we have now addressed the concerns regarding parameter specification and validation. Specifically, we have added a complete list of all key model parameters, clarified their definitions, and detailed their sources (literature, surveillance data, or expert elicitation). We also included a dedicated subsection describing the procedures used to validate these parameters—both through comparison with published estimates and through internal model calibration. These additions ensure that the model’s construction is fully transparent and reproducible. 2. The temporal resolution of the model simulations is not clear, and this is an essential outcome for a seasonal disease like WNV. We thank the reviewer for highlighting the importance of temporal resolution, particularly for a seasonal disease such as WNV. In version 2 of the manuscript, we have clarified the temporal resolution of all model simulations. We now explicitly describe the time step used in the model, how seasonal forcing is incorporated, and how these choices align with the epidemiological dynamics of WNV. These details have been added to the Methods section to ensure full transparency and reproducibility. 3. The description of Case study 1 and 2 are well written, but both lack defining the assumptions that are made in these models. Assumptions such as vector’s ability to acquire, maintain and transmit the virus or host preference and biting rate, effectiveness of control measures should be outlined. The primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. 4. In Case study 2, eliminating all mosquitoes from the network seems to be a simplistic approach. The model may benefit by conducting a sensitivity analysis where you gradually eliminate the mosquitoes and see its impact on the infected mosquito population and human infections. Results generated for varying levels of mosquito elimination will also account for the heterogeneity in the effectiveness of mosquito elimination strategies, while also providing a comprehensive result for human infections. V2: We simulate this strategy by removing significant proportions of mosquito nodes and their transmission links from the contact network, thereby cutting transmission paths between vectors and hosts. 5. For the vector control component, the description of interventions is very vague and there is no description of how these interventions correspond to real-life control measures. Also, to which extent is the approach scalable? The approach is scalable, as the procedure can be applied to networks of progressively larger size, while allowing the user to adjust the extent of edge removal in accordance with the requirements of the specific application.” 6. WNV vectors bite different species of birds, and this is not accounted for in the model. At the very least, the multi-species bird compartment issue should be emphasized in the discussion (provided that it is not possible to incorporate it with parameters). V2: One significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. We acknowledge that one significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. Therefore, the primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. 7. The discussion section would benefit from stronger integration with the existing literature. Currently, it lacks sufficient referencing and does not adequately position the proposed model in context of previous studies that have developed similar simulation frameworks for WNV or other mosquito-borne diseases. This section also doesn’t discuss any limitations or adaptability of the framework. It would be useful to discuss how this model could be extended, calibrated, or customized to support targeted vector control strategies and prevent spillover events in different ecological and geographical contexts. We improved both related work and discussion. Minor Comments: 1. Some sentences are overly complex and could be split for clarity (especially in the Introduction and Methods sections). We checked and polished the manuscript. 2. Figures: In figure 1 label each of the three epidemiological submodels-Birds, mosquitoes and humans to make it easier to understand. Also, improve readability (labels are small). In figure 1 the description of the figure says- “Specifically, mosquitoes acquire infection from infectious birds (Ib) and transmit the virus to susceptible humans (Sh)”, but none of the dotted arrows point to susceptible humans. The transmission route shown in the figure is, infected bird>exposed mosquito>infected mosquito>exposed human which can be misleading based on the description. Harmonize color schemes between Figures 2 and 3. We improved the quality of all the figures. 3. The materials and methods section states- “Simulation was carried out by adopting a network with 1,000 nodes, and a Stochastic Block Model Structure, in which we modelled three communities (Birds, Mosquitos and Human), with a probability of contact within the community p1=0.8 and a probability of contacts between the communities p2=0.2.” Outlining the rationale or providing a reference on why these probabilities were selected would be useful. The choice of probabilities p1=0.8 (within-community) and p2=0.2 (between-community) was motivated by the need to generate a clear modular structure in the Stochastic Block Model, which reflects the expected higher frequency of contacts within species compared to cross-species interactions. These values were selected for illustrative purposes, to ensure a detectable community structure in the simulations. 4. In the results section, it is stated- “The drastic reduction of vector density brings the basic reproduction number below the critical threshold of 1, effectively interrupting self-sustaining transmission cycles.” Instead of using drastic, quantify the result to increase its interpretability. We detailed the result section to discuss these details. 5. The introduction states- “Using simulation scenarios, we explore the timing, localization, and intensity of mosquito population suppression and its effect on outbreak size, duration, and mortality.” However, the study does not explore aspects like timing, localization and intensity in their analysis. Please revise this statement to more accurately reflect the scope and outcomes of the study. We deleted localization word. 6. Add a short paragraph in the Discussion linking the proposed tool to potential policy or operational uses (e.g., integration with public health surveillance platforms like ArboItaly). We improved the discussion. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Kuhn KG and Deshpande G. Peer Review Report For: Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.5256/f1000research.186955.r423796) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-902/v1#referee-response-423796 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Antonelli L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Oct 2025 | for Version 1 Laura Antonelli , Istituto of High Performance Computing and Networks, National Research Council, Via P. Castellino, Naples, Italy 0 Views copyright © 2025 Antonelli L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This study provides a substantial conceptual and methodological advancement in modelling West Nile Virus (WNV) transmission through an adaptive, network-based SEIRD framework. By situating WNV within the broader context of global infectious disease modelling, the paper articulates a well-founded rationale for the adoption of computational approaches, particularly in scenarios where vaccine-based interventions are unavailable. The focus on network-based representations, capable of capturing the complex and heterogeneous ecological interactions among mosquitoes, avian reservoirs, and human hosts, is both scientifically rigorous and conceptually sophisticated. Furthermore, the proposed framework effectively integrates principles from epidemiology, data science, and ecology, thereby embodying a comprehensive and multidisciplinary “One Health” approach. In my opinion, the manuscript requires minor corrections as suggested below: Some sentences are too long and syntactically complex, which can obscure meaning (e.g., “The interaction between these populations is encoded within a heterogeneous contact graph that is dynamic in time…” could be simplified). Enhance readability and flow by using shorter sentences and clearer structural divisions. Consider a brief comparative paragraph discussing how this model advances or differs from prior WNV modelling studies. Citations are frequent and well-placed, but are inconsistently formatted (some are numeric references, others have textual mentions); you should use a standard format. Some characters in the figures are unreadable (e.g. see Fig. 1, the second blue ball) The titles of Figures 2 and 3 are redundant and could be removed Is the rationale for developing the new method (or application) clearly explained? Yes Is the description of the method technically sound? Yes Are sufficient details provided to allow replication of the method development and its use by others? Yes If any results are presented, are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Computational Modelling & Scientific Computing for AI I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 14 Nov 2025 Mohamed Mustaf Ahmed, Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia GENERAL COMMENT: This study provides a substantial conceptual and methodological advancement in modelling West Nile Virus (WNV) transmission through an adaptive, network-based SEIRD framework. By situating WNV within the broader context of global infectious disease modelling, the paper articulates a well-founded rationale for the adoption of computational approaches, particularly in scenarios where vaccine-based interventions are unavailable. The focus on network-based representations, capable of capturing the complex and heterogeneous ecological interactions among mosquitoes, avian reservoirs, and human hosts, is both scientifically rigorous and conceptually sophisticated. Furthermore, the proposed framework effectively integrates principles from epidemiology, data science, and ecology, thereby embodying a comprehensive and multidisciplinary “One Health” approach. n my opinion, the manuscript requires minor corrections as suggested below: Comment 1: Some sentences are too long and syntactically complex, which can obscure meaning (e.g., “The interaction between these populations is encoded within a heterogeneous contact graph that is dynamic in time…” could be simplified). Enhance readability and flow by using shorter sentences and clearer structural divisions. Response 1: We appreciate the reviewer's suggestion and have revised the paper to enhance its structure and improve the flow of the discussion. Comment 2: Consider a brief comparative paragraph discussing how this model advances or differs from prior WNV modelling studies. Response 2: We thank the reviewer for this feedback. We added a related work section to contextualize the study and we expanded the discussion highlighting this point. Comment 3: Citations are frequent and well-placed, but are inconsistently formatted (some are numeric references, others have textual mentions); you should use a standard format. Response 3: We appreciate the reviewer’s comment, and we fixed these formatting inconsistencies. Comment 4: Some characters in the figures are unreadable (e.g. see Fig. 1, the second blue ball) Response 4: We thank the reviewer for the constructive feedback. We have recreated Figure 1 to enhance its precision and readability. Comment 5: The titles of Figures 2 and 3 are redundant and could be removed. Response 5: We thank the reviewer for this suggestion. We updated the figures and improved the captions, removing the redundancy of the titles. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Antonelli L. Peer Review Report For: Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.5256/f1000research.186955.r414393) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-902/v1#referee-response-414393 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Wimberly M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 15 Oct 2025 | for Version 1 Michael Wimberly , The University of Oklahoma, Norman, Oklahoma, USA 0 Views copyright © 2025 Wimberly M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The idea of simulating West Nile virus using a mechanistic compartment model is interesting and has the potential to yield new insights into the drivers of transmission and the effectiveness of various treatment strategies. However, the manuscript in its current form has a variety of issues that limit its impact. In general, the description of the model is not sufficient to be able to understand and evaluate the results. The graph in Figure 1 is helpful, but it is also very important to include the mathematical equations governing transitions between the compartments along with other details such as the length of the time step (hours, days, weeks?). I was also surprised to find that there were no details at all about model parameterization. Were parameters obtained from the literature? Or were they estimated through a model fitting process? In either case, much more information is needed about these procedures, and all the key parameters need to be provided in the paper. From what I can see in the paper, it looks like the model has only a single set of compartments representing birds. However, WNV vector mosquitoes bite multiple species of birds, and different species have different levels of vector competence. Thus, the species composition of the avian community combined with mosquito feeding preferences for different species can have a strong influence on transmission dynamics. Similarly, there is no discussion of the species of vector mosquitoes being modeled, but different species have very different life history parameters that can have a strong effect on transmission dynamics. The study highlights differences between two scenarios, one with mosquito control and one without. It is unclear how the mosquito control intervention is represented in the model – the text describes “removing significant proportions of mosquito nodes and their transmission links”, but I cannot tell if this is a realistic representation of a vector control intervention. Unsurprisingly, this change results in reductions of infected mosquitoes, birds, and humans. Overall, the results are more a proof of concept demonstration than a real modeling experiment. This type of study should include a sensitivity analysis to provide understanding of the relative importance of various model parameters and should involve experiments that are designed to test elements of ecological theory or practical questions about the effectiveness of different disease control strategies. I would also note that this type of modeling approach is not unique. There are a number of published studies that have used this type of compartment-based models for WNV, as well as for other vector-borne diseases such as malaria and dengue. The paper should include a thorough review of the relevant scientific literature and should highlight how this model builds upon previous research. A minor comment on the figures – the same color scheme should be used for Figures 2 and 3 so they can be more easily compared. Is the rationale for developing the new method (or application) clearly explained? No Is the description of the method technically sound? No Are sufficient details provided to allow replication of the method development and its use by others? No If any results are presented, are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions about the method and its performance adequately supported by the findings presented in the article? No Competing Interests No competing interests were disclosed. Reviewer Expertise Disease ecology and modeling I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (1) Author Response 14 Nov 2025 Mohamed Mustaf Ahmed, Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia GENERAL COMMENT: The idea of simulating West Nile virus using a mechanistic compartment model is interesting and has the potential to yield new insights into the drivers of transmission and the effectiveness of various treatment strategies. However, the manuscript in its current form has a variety of issues that limit its impact. Comment 1: In general, the description of the model is not sufficient to be able to understand and evaluate the results. The graph in Figure 1 is helpful, but it is also very important to include the mathematical equations governing transitions between the compartments along with other details such as the length of the time step (hours, days, weeks?). Response 1: We thank the reviewer for this important feedback. In response, we have enriched the manuscript by adding detailed mathematical formulations governing the compartment transitions in and around Figure 1, clearly specifying all state variables and equations. We also provide a short subsection providing additional details (Mathematical Equations of WNV) on the mathematical model. Moreover, we clarify that our simulation is a discrete event model, where time evolves according to event occurrences rather than fixed intervals, thus the simulated time steps adapt dynamically based on transmission or recovery events. Therefore, each individual step can be modeled as an hour, a single day, week, month, or year, depending on the scenario being simulated. Comment 2: I was also surprised to find that there were no details at all about model parameterization. Were parameters obtained from the literature? Or were they estimated through a model fitting process? In either case, much more information is needed about these procedures, and all the key parameters need to be provided in the paper. Response 2: We thank the reviewer for pointing out this aspect. Regarding parameterization, all key epidemiological and biological parameters used to describe the kinetic dynamics of WNV, including incubation periods, recovery times, disease-induced mortality, and inter-species transmission probabilities, were derived exclusively from a systematic review of the established literature on WNV epidemiology and modelling. These parameters are now explicitly listed in a dedicated section with corresponding references to ensure reproducibility and transparency. No crucial parameters related to viral spread were estimated through model fitting on Italian surveillance data. Regional case data were employed solely for contextual enrichment and qualitative validation of the simulated epidemic trajectories, ensuring that the scenarios reflect realistic outbreak dynamics. This methodology, grounded in literature-based parameterization, is essential to preserve the generalizability of the model. Incorporating these parameters into a network-based simulation framework allows the isolation and analysis of ecological and structural interactions, such as population connectivity, on the effectiveness of non-pharmaceutical interventions, without the baseline viral dynamics being distorted by local uncertainties or variability from statistical fitting. Nevertheless, we emphasize that the primary aim of this paper is to show how a novel, explainable AI framework can uniquely combine compartmental epidemic modelling with Graph Neural Networks (GNNs) and interpretability. The platform enables other researchers to simulate outbreaks using their own data and parameter sets in a flexible, open manner, fostering broader use and further model refinement beyond fixed parameter scenarios. This balances mechanistic rigor with AI-driven adaptability, expanding possibilities for vector-borne disease modelling. Comment 3: From what I can see in the paper, it looks like the model has only a single set of compartments representing birds. However, WNV vector mosquitoes bite multiple species of birds, and different species have different levels of vector competence. Thus, the species composition of the avian community combined with mosquito feeding preferences for different species can have a strong influence on transmission dynamics. Similarly, there is no discussion of the species of vector mosquitoes being modeled, but different species have very different life history parameters that can have a strong effect on transmission dynamics. Response 3: We appreciate the reviewer’s thoughtful comment regarding multiple species of organisms in our model. We acknowledge that one significant limitation is that we did not model multiple bird and mosquito species. Developing this aspect in future research would be valuable for studying its impact on transmission dynamics. Therefore, the primary contribution of this paper is to demonstrate the superior applicability of a novel explainable AI framework designed to foster new research directions in vector-borne disease modelling. Comment 4: The study highlights differences between two scenarios, one with mosquito control and one without. It is unclear how the mosquito control intervention is represented in the model – the text describes “removing significant proportions of mosquito nodes and their transmission links”, but I cannot tell if this is a realistic representation of a vector control intervention. Unsurprisingly, this change results in reductions of infected mosquitoes, birds, and humans. Response 4: We thank the reviewer for these highlights. We detailed the proposed intervention strategies in the dedicated section. Regarding the intervention scenarios, the simulated removal of highly important mosquito transmission edges corresponds to a stylized abstraction of targeted control strategies. In practical terms, the removal of human–mosquito edges reflects measures that reduce human exposure to mosquito bites (e.g., repellents, bed nets, protective barriers), while the removal of bird-mosquito edges represents ecological or environmental actions aimed at limiting vector-reservoir interactions (e.g., habitat management, avifauna control, population monitoring). Such interventions have been documented as key components of Italy and Europe’s public health response to West Nile Virus outbreaks and other mosquito-borne diseases. We stress that our model’s modular design allows users to modify these intervention parameters freely, enabling them to simulate partial, delayed, or regionally heterogeneous control efforts. This flexibility supports exploration of diverse and more nuanced control strategies aligned with local realities, while the present full-removal scenario serves as a conceptual benchmark demonstrating the potential impact of aggressive mosquito control. Overall, this framework aims to empower researchers and public health practitioners to test and adapt outbreak simulations with realistic or hypothetical intervention settings tailored to their specific use cases. Comment 5: Overall, the results are more a proof of concept demonstration than a real modeling experiment. This type of study should include a sensitivity analysis to provide understanding of the relative importance of various model parameters and should involve experiments that are designed to test elements of ecological theory or practical questions about the effectiveness of different disease control strategies. Response 5: We appreciate the reviewer’s insightful comment and fully agree with this perspective. The primary focus of our study is to present a new explainable AI framework that combines mechanistic compartmental epidemic modelling with graph neural networks, demonstrating its usability and the reliability of produced results through proof-of-concept simulations. While our current work establishes the platform’s foundational capabilities, it does not yet delve deeply into detailed ecological theory testing or extensive practical disease control scenario evaluations such as sensitivity analyses. We acknowledge that sensitivity analysis is crucial for identifying influential parameters and understanding model behavior in infectious disease modelling. Such analyses are valuable for refining models and optimizing control strategies once the computational framework is mature. Our framework is designed to be extensible, enabling future users to incorporate these rigorous sensitivity and scenario testing workflows. Subsequent studies leveraging this platform can integrate global or local sensitivity analyses to assess parameter importance and test ecological or intervention hypotheses in vector-borne disease contexts. Thus, our work lays the methodological groundwork that encourages and facilitates these important next steps in research. Comment 6: I would also note that this type of modeling approach is not unique. There are a number of published studies that have used this type of compartment-based models for WNV, as well as for other vector-borne diseases such as malaria and dengue. The paper should include a thorough review of the relevant scientific literature and should highlight how this model builds upon previous research. Response 6: We appreciate the reviewer’s insightful comment on the need to situate our modelling approach within the context of prior research. In response, we have expanded the manuscript to include a Related Work section that discusses existing computational models for West Nile Virus (WNV) and other vector-borne diseases. Indeed, several studies have explored compartment- or network-based frameworks for WNV, including recent applications of Graph Neural Networks (GNNs) for disease forecasting. For example, Tonks et al. (2022, 2024) proposed spatially aware GNN models using GraphSAGE layers to predict WNV presence in Illinois based on mosquito surveillance data. Similarly, Bonicelli et al. (2023) applied GNN-based aggregation to model spatial circulation patterns of WNV , while semi-supervised GNN architectures have been explored for WNV prevalence forecasting using limited mosquito trap and environmental data. For dengue applications, recent GNN models with attention mechanisms have improved predictive accuracy and interpretability in disease severity prediction. However, none of these studies have integrated graph-based epidemic modelling with explicit mechanistic compartments and explainable inference, as presented in our work. Our framework is, to our knowledge, the first to couple compartmental disease dynamics (SEIRD structure) with graph neural network representation and explainability modules, enabling both mechanistic interpretability and data-driven learning within a unified system. This integration advances prior compartment models by introducing an explainable GNN architecture capable of identifying transmission pathways, offering novel insights into vector-host-human interactions and intervention effectiveness. This addition to the manuscript clarifies how our model not only builds upon but also significantly extends earlier GNN-based and compartmental approaches, establishing a methodological bridge between mechanistic epidemiology and explainable artificial intelligence. Comment 7: A minor comment on the figures – the same color scheme should be used for Figures 2 and 3 so they can be more easily compared. Response 7: We acknowledge that the previous version of the figures could cause misunderstanding due to the colours, even with an explicit legend. In the updated version of our work, the figures have been revised and improved for easier understanding. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Wimberly M. Peer Review Report For: Graph-based epidemic modeling of West Nile Virus: Forecasting and containment [version 2; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :902 ( https://doi.org/10.5256/f1000research.186955.r417875) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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