Bulletin of Mathematical Biology最新文献

{"title":"Stochastic Analysis of a Plant-Vector-Virus Model with Saturated Incidence Rate.","authors":"Sunil Maity","doi":"10.1007/s11538-026-01735-y","DOIUrl":"10.1007/s11538-026-01735-y","url":null,"abstract":"<p><p>Cassava mosaic disease (CMD), caused by the cassava mosaic virus (CMV) and transmitted primarily by the whitefly Bemisia tabaci, reflects the most severe and widespread viral threat to cassava cultivation. The emergence of CMD with a nonlinear saturated incidence of Holling type II form is stochastically modeled and analyzed in this paper utilizing the continuous-time Markov chain (CTMC) modeling approach. The most significant distinction between deterministic and stochastic models is that the deterministic model predicts disease persistence when the basic reproduction number <math> <mrow><msub><mi>R</mi> <mn>0</mn></msub> <mo>></mo> <mn>1</mn></mrow> </math> , whereas the stochastic model, characterized by the branching-process threshold <math><mrow><mi>ρ</mi> <mo>(</mo> <mi>M</mi> <mo>)</mo></mrow> </math> , indicates that disease extinction can still occur in finite time due to random fluctuations, even when <math> <mrow><msub><mi>R</mi> <mn>0</mn></msub> <mo>></mo> <mn>1</mn></mrow> </math> . The likelihood of disease extinction is determined using the Galton-Watson branching process (GWbp) approximation and compared to the estimated probability obtained from the stochastic model's 10,000 sample paths. This comparison reveals a substantial link between these probabilities. It is found that the disease has a higher probability of extinction when transmission occurs solely through infected vectors, as opposed to transmission through infected plants or both infected plants and vectors. In the stochastic settings, the implicit equation for the mean first passage time is derived to assess the typical time until the first state transition. Additionally, we evaluate both the quasi-stationary distribution of infected individuals and the probability distribution of the epidemic's ultimate size.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148856898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Eco-Evolutionary Dynamics of Proliferation Heterogeneity: A Phenotype-Structured Model for Tumor Growth and Treatment Response.","authors":"Lara Schmalenstroer, Haojun Chen, Russell C Rockne, Farnoush Farahpour","doi":"10.1007/s11538-026-01740-1","DOIUrl":"10.1007/s11538-026-01740-1","url":null,"abstract":"<p><p>Intra-tumor heterogeneity in proliferation rates fundamentally influences cancer progression and treatment resistance. To investigate how continuous phenotypic variation shapes eco-evolutionary dynamics, we develop a phenotype-structured partial differential equation framework that explicitly models proliferation heterogeneity as a dynamic trait. Our model integrates three key biological principles: (1) phenotypic diffusion capturing heritable variation in proliferation rates, (2) global resource competition enforcing density-dependent growth constraints, and (3) an experimentally grounded life-history trade-off linking elevated proliferation to increased mortality. Using adaptive dynamics, we derive the optimum proliferation rate in a growing tumor, showing that the optimal phenotype dynamically shifts toward slower proliferation as tumors approach carrying capacity under control condition. We perform in silico treatment simulations for four different treatment regimes (uniform targeting, low-, mid-, and high-proliferation targeting) to show how therapeutic selective pressures reshape fitness landscapes. While all treatments slow down tumor growth, they induce divergent evolutionary trajectories. We connect these dynamics with changes in mean proliferation rates during and after treatment. Our work establishes a predictive, evolutionarily grounded framework for understanding how therapy reshapes tumor proliferation landscapes, offering a mechanistic basis for designing strategies that anticipate and counteract adaptive resistance.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13526070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148856931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gene Genealogies in Haploid Populations Evolving According to Sweepstakes Reproduction.","authors":"Bjarki Eldon","doi":"10.1007/s11538-026-01736-x","DOIUrl":"10.1007/s11538-026-01736-x","url":null,"abstract":"<p><p>Recruitment dynamics, or the distribution of the number of offspring among individuals, is fundamental to ecology and evolution. We take sweepstakes reproduction to mean a skewed (heavy right-tailed) offspring number distribution without natural selection being involved. Sweepstakes may be generated by chance matching of reproduction with favorable environmental conditions. Gene genealogies generated by sweepstakes reproduction are in the domain of attraction of multiple-merger coalescents where a random number of lineages merges at such times. We consider population genetic models of sweepstakes reproduction for haploid panmictic populations of both constant (N), and varying population size, and evolving in a random environment. We construct our models so that we can recover the observed number of new mutations in a given sample without requiring strong assumptions regarding the population size or the mutation rate. Our main results are (i) continuous-time coalescents that are either the Kingman coalescent or specific families of Beta- or Poisson-Dirichlet coalescents; when combining the results the parameter <math><mi>α</mi></math> of the Beta-coalescent ranges from 0 to 2, and the Beta-coalescents may be incomplete due to an upper bound on the number of potential offspring an arbitrary individual may produce; (ii) in large populations we measure time in units proportional to either <math><mrow><mi>N</mi> <mo>/</mo> <mo>log</mo> <mi>N</mi></mrow> </math> or N generations; (iii) incorporating fluctuations in population size leads to time-changed multiple-merger coalescents where the time-change does not depend on <math><mi>α</mi></math> ; (iv) using simulations we show that in some cases approximations of functionals of a given coalescent do not match the ones of the ancestral process in the domain of attraction of the given coalescent; (v) approximations of functionals obtained by conditioning on the population ancestry (the ancestral relations of all gene copies at all times) are broadly similar (for the models considered here) to the approximations obtained without conditioning on the population ancestry.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148817004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Delay Differential Equation Approach to Oligomerization Reveals the Delay Dynamics Underlying Oscillations in Mitochondrial Fission.","authors":"Kitrick Fynaardt, Anna K Leinheiser, Colleen C Mitchell, Chad E Grueter","doi":"10.1007/s11538-026-01743-y","DOIUrl":"10.1007/s11538-026-01743-y","url":null,"abstract":"<p><p>Disruptions in the balance of mitochondrial fission and fusion are implicated in a host of diseases including cardiovascular, metabolic, and neurodegenerative, as well as cancer. Leinheiser et al. proposed a mechanistic model for mitochondrial fission which relies on the oligomerization of Dynamin-related protein 1 (Drp1). In this work, we propose an alternative state-dependent delay-differential equation (sdDDE) framework for mitochondrial fission, which reveals that the intrinsic delay dynamics in Drp1 oligomerization can drive oscillations in the rate of mitochondrial fission. To develop this sdDDE model, we generate a simplified model which disallows oligomer disassembly on the mitochondrial membrane. Following homogenization, the simplified model approaches a steady state dominated by oligomers too small to reach the threshold for fission. Therefore, the fission rate approaches zero when initial conditions reside within the basin of attraction of this fission-free equilibrium. We therefore reincorporate oligomer disassembly on the mitochondrial membrane. However, the attracting, fission-free equilibrium persists. To eliminate this fission-free equilibrium, we incorporate an atomization term into the oligomerization mechanism, highlighting the importance of oligomer disassembly in sustaining mitochondrial fission. Using homogenization techniques, we derive an advection PDE with nonlocal interactions and obtain a reduced sdDDE system governing oligomer partial moments. Analysis of this reduced system reveals an analogous Hopf bifurcation to the Leinheiser et al. fission model, demonstrating that intrinsic delays in Drp1 oligomerization are sufficient to generate oscillatory mitochondrial fission dynamics.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13499728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148788478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate-Informed Predictive Optimal Control of Desert Locust Dynamics Under Machine-Learning Climate Forcing.","authors":"Dejen K Mamo, Mathew N Kinyanjui, Nourridine Siewe","doi":"10.1007/s11538-026-01737-w","DOIUrl":"10.1007/s11538-026-01737-w","url":null,"abstract":"<p><p>Climate variability plays a fundamental role in desert locust population dynamics, yet most existing modelling and optimal control frameworks rely on simplified representations of seasonal forcing that inadequately capture short-term climatic variability. To address this limitation, we develop a climate-informed predictive optimal control framework that integrates a mechanistic stage- and phase-structured desert locust model with machine-learning-based climate forecasting. Long short-term memory networks and gradient-boosting regression are employed to predict temperature and rainfall, respectively, providing data-driven climatic inputs to a non-autonomous system describing locust development, reproduction, mortality, vegetation dynamics, and phase transitions. The mathematical analysis establishes the well-posedness of the model through the existence, uniqueness, positivity, and boundedness of solutions, while an autonomous reduction yields a closed-form basic offspring number that provides analytical insight into invasion potential under representative climatic conditions. A predictive optimal control problem is formulated to evaluate stage-specific physical, biological, and chemical interventions targeting juvenile and adult locust populations. Numerical simulations demonstrate that machine-learning-derived climate forcing more accurately reproduces observed climatic variability than conventional harmonic forcing, thereby providing improved inputs for predictive population modelling. Integrated juvenile-adult intervention strategies consistently outperform single-stage controls by reducing locust abundance, preserving vegetation, and lowering the composite management index. Among the intervention strategies considered, chemical control provides the greatest short-term suppression, biological control offers a more environmentally sustainable alternative, and physical control is most effective as a complementary measure during the early stages of population growth. Robustness analyses under deterministic climate perturbations and stochastic forecast errors show that the comparative ranking of intervention strategies remains stable under realistic climate forecast uncertainty. These findings demonstrate that integrating machine-learning climate prediction with predictive optimal control provides a mathematically rigorous and flexible framework for climate-informed decision support in desert locust management and establishes a foundation for future developments incorporating spatial dynamics, probabilistic forecasting, and real-time surveillance.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148788497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Bulletin of Mathematical Biology and the Society for Mathematical Biology: Strengthening our Shared Community.","authors":"Matthew J Simpson, Reinhard C Laubenbacher, Jennifer A Flegg","doi":"10.1007/s11538-026-01742-z","DOIUrl":"10.1007/s11538-026-01742-z","url":null,"abstract":"","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148788606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bacteriophage Density Influences the Rate of Resistance Evolution.","authors":"Tuan Phan, Anuja Shrestha, Jacob Schow, Craig R Miller, Tracey L Peters, James T Van Leuven","doi":"10.1007/s11538-026-01704-5","DOIUrl":"10.1007/s11538-026-01704-5","url":null,"abstract":"&lt;p&gt;&lt;p&gt;The antagonistic relationship between bacteria and bacteriophages (phages) drives genetic changes that result in phage resistance, making it important to understand the dynamics of resistance acquisition in both natural microbial communities and therapeutic contexts. We developed a generalized Lotka-Volterra model describing the interaction among phage-susceptible bacteria, phage-resistant bacteria, and a lytic phage, incorporating asymmetric interspecific competition and partial phage resistance. The model was fitted to growth data from four bacterial strains-Pseudomonas aeruginosa PA103 and PAK challenged with a phage cocktail, and Paenibacillus larvae Y-3650 and 25747 challenged with phage Fern IDv1-across four levels of multiplicity of infection (MOI). We compared two parameterization schemes: M1, in which the interspecific competition coefficients &lt;math&gt;&lt;msub&gt;&lt;mi&gt;a&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;rs&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt; and &lt;math&gt;&lt;msub&gt;&lt;mi&gt;a&lt;/mi&gt; &lt;mrow&gt;&lt;mi&gt;sr&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt; are shared across MOI treatments, and M2, in which these coefficients are allowed to vary by treatment. For the two P. aeruginosa strains, M1 produced adequate fits (mean &lt;math&gt; &lt;mrow&gt;&lt;msup&gt;&lt;mi&gt;R&lt;/mi&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mn&gt;0.91&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; and 0.89). For the two P. larvae strains, M1 failed catastrophically at specific treatments ( &lt;math&gt;&lt;msup&gt;&lt;mi&gt;R&lt;/mi&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt; &lt;/math&gt; as low as &lt;math&gt;&lt;mrow&gt;&lt;mo&gt;-&lt;/mo&gt; &lt;mn&gt;19.7&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; ), while M2 achieved mean &lt;math&gt;&lt;msup&gt;&lt;mi&gt;R&lt;/mi&gt; &lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt; &lt;/math&gt; of 0.90 and 0.95 for Y-3650 and 25747, respectively. Information-theoretic model comparison ( &lt;math&gt;&lt;mrow&gt;&lt;mi&gt;Δ&lt;/mi&gt; &lt;mtext&gt;AIC&lt;/mtext&gt; &lt;mo&gt;=&lt;/mo&gt; &lt;mo&gt;-&lt;/mo&gt; &lt;mn&gt;753&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; and &lt;math&gt;&lt;mrow&gt;&lt;mo&gt;-&lt;/mo&gt; &lt;mn&gt;458&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; ) and profile likelihood analysis confirmed that the competition coefficients are fundamentally MOI-dependent, with per-treatment optima diverging by up to three orders of magnitude. These findings indicate an effect that extends beyond phage-mediated apparent competition-which assumes zero direct competition between the susceptible and resistant subpopulations-because the fitted interspecific competition coefficients are nonzero and MOI-dependent. We observed three distinct growth patterns-delayed growth, two-phase growth, and complete suppression-with non-monotonic dose-response relationships in which intermediate phage doses produced stronger suppression than higher doses. This non-monotonicity demonstrates that the boundaries between growth regimes are governed by competitive dynamics rather than phage dose alone. Our results establish that phage resistance evolution is fundamentally an ecological process: the competitive context, shaped by phage pressure, determines population-level outcomes independently of intrinsic mutation capacity. These findings have implications for phage therapy, suggesting that optimal dosing strategies must account for the competitive environment created by treatment","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13493482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148788428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Metal-Ion-Mediated Amyloid- <ns0:math><ns0:mi>β</ns0:mi></ns0:math> Aggregation in Alzheimer's Disease: A Mathematical Model of Chelation and Inhibitory Therapies.","authors":"Shantia Yarahmadian, Yasser Alzahrani, Vaghawan Prasad Ojha","doi":"10.1007/s11538-026-01732-1","DOIUrl":"10.1007/s11538-026-01732-1","url":null,"abstract":"<p><p>We develop a novel, comprehensive, and rigorously validated mathematical framework to investigate the kinetics of amyloid- <math><mi>β</mi></math> (A <math><mi>β</mi></math> ) aggregation in the presence of biologically relevant metal ions, chelating agents, and inhibitor drugs. Building upon and extending existing aggregation models, our approach integrates metal-assisted aggregation, A <math><mi>β</mi></math> self-assembly, and therapeutic interventions within a unified and mechanistically consistent formulation. The model captures the microscopic reaction pathways governing A <math><mi>β</mi></math> dynamics and explicitly incorporates the catalytic roles of copper, zinc, and iron ions-key contributors to neurotoxic plaque formation in Alzheimer's disease. Distinctively, the framework combines dual therapeutic strategies: (i) metal chelation therapy, which sequesters free metal ions, and (ii) direct inhibition of A <math><mi>β</mi></math> aggregation. Numerical simulations across multiple kinetic regimes reveal how these interventions modulate aggregation pathways, both independently and synergistically. To further validate the model, we perform a quantitative comparison with experimental data by reconstructing aggregate morphology distributions and benchmarking them against reported AFM measurements. The model successfully captures key experimental features, including peak structure and metal-dependent heterogeneity, thereby demonstrating its predictive capability. Overall, this work provides an extended and unified modeling platform that advances the quantitative understanding of metal-mediated amyloid aggregation and offers a predictive tool for evaluating and optimizing therapeutic strategies for Alzheimer's disease.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148788392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Making Ends Meet or Just Meeting at the Ends? Assessing End-to-End Distance in Folded RNA Sequences and Other Branched Structures.","authors":"Torin Greenwood, Christine Heitsch","doi":"10.1007/s11538-026-01718-z","DOIUrl":"10.1007/s11538-026-01718-z","url":null,"abstract":"<p><p>Researchers have repeatedly found that the ends of an RNA sequence are significantly closer than expected for a random linear chain. However, we prove that the ends of a branched structure are almost certainly close. Our results are obtained via combinatorial branching models of increasing complexity using tools from multivariate analytic combinatorics. We completely characterize parameters tracking end-to-end distance, including means and variances. Then, we compare to existing datasets of known RNA structures, as well as the minimum free-energy structures of randomized shuffles. We find that the shuffled structures resemble our theoretical distributions while the known RNA structures have similar parameter values but are more concentrated. The helices on the exterior loop of a structure draw the ends of the sequence together, and we find evidence that these helices are more stable than expected when compared to their counterparts in uniformly random branched structures.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13490249/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148788447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Key Factors in Predicting Chikungunya and Zika Transmission Across French Polynesia and the French West Indies: A Data-Driven Probabilistic and Mathematical Modeling Approach.","authors":"Zhiyuan Yu, Xi Huo, Peter J Thomas, Qimin Huang","doi":"10.1007/s11538-026-01728-x","DOIUrl":"10.1007/s11538-026-01728-x","url":null,"abstract":"<p><p>Chikungunya (CHIKV) and Zika are arboviruses transmitted by ectothermic Aedes mosquitoes, leading to seasonal outbreak patterns of viral infections. Mathematical models linked with vector and case data have proven powerful for understanding arboviral transmission dynamics, but it remains unclear if and which environmental, vector, and host features are essential for accurately capturing transmission dynamics and short-term forecasting. We began with a baseline modified SEIR compartmental model, extended it into a weekly probabilistic model aligned with reported surveillance data, and evaluated variants that include or exclude mosquito dynamics (M), temperature effects (T), and precipitation effects (P). We calibrated these variants to weekly CHIKV and Zika incidence, temperature, and precipitation data from nine regions in French Polynesia and the French West Indies. We used AIC-based model comparison to assess goodness-of-fit, rolling-window forecasting to compare predictive performance with statistical and null baselines, and the best-fitting model to estimate biologically meaningful transmission parameters. Incorporating mosquito dynamics, temperature, and precipitation substantially improves prediction accuracy, with precipitation acting as the primary environmental driver and temperature serving as a modulator. The corresponding SEI+M+T+P formulation achieves forecasting accuracy comparable to ARIMA while providing mechanistically interpretable predictions. CHIKV possesses higher transmissibility than Zika, with regionally heterogeneous parameters driven by varying mosquito-human transmission rates and mosquito densities, whereas Zika parameters are relatively homogeneous across regions. These findings suggest uniform vector control may be effective for Zika, whereas region-specific strategies are needed for CHIKV. Sensitivity analysis indicates that rapid diagnosis and isolation of symptomatic individuals are among the most effective measures for reducing transmission of both viruses.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"88 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13490255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148788473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}