Pub Date : 2026-01-11DOI: 10.1016/j.mbs.2026.109622
Francisco J. Solis , Luz M. Gonzalez
In this paper we present a nonlinear discrete model in order to describe defective interactions of immune system cells with Human Papillomavirus (HPV) infected cells. Statistics show than only a percentage of the HPV infected population will develop malignancy diseases. Our goal is to develop a prototypical mathematical model that is analitically tratable with a statistical complexity to reproduce qualitative and quantitative information of the consequences of HPV-evasion of host defenses and suppression of an efficient immune response. Numerical results obtained from the model confirm the intrinsic relationships of its nonlinear terms representing the earlier evolution of mature infected cells with a successful virus invasion.
{"title":"A discrete nonlinear model for HPV immune suppression and evasion","authors":"Francisco J. Solis , Luz M. Gonzalez","doi":"10.1016/j.mbs.2026.109622","DOIUrl":"10.1016/j.mbs.2026.109622","url":null,"abstract":"<div><div>In this paper we present a nonlinear discrete model in order to describe defective interactions of immune system cells with Human Papillomavirus (HPV) infected cells. Statistics show than only a percentage of the HPV infected population will develop malignancy diseases. Our goal is to develop a prototypical mathematical model that is analitically tratable with a statistical complexity to reproduce qualitative and quantitative information of the consequences of HPV-evasion of host defenses and suppression of an efficient immune response. Numerical results obtained from the model confirm the intrinsic relationships of its nonlinear terms representing the earlier evolution of mature infected cells with a successful virus invasion.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"393 ","pages":"Article 109622"},"PeriodicalIF":1.8,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968287","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}
Pub Date : 2026-01-10DOI: 10.1016/j.mbs.2025.109606
Scott Gadsby , Cyril Rauch , Jonathan A D Wattis
Given data on genotypes and phenotypes from a sample population, we show how ordering the data by phenotype and analysing the information contained in the corresponding list of genotypes can identify those SNPs which have a significant correlation with phenotype. We derive formulae for p-values to quantify the significance of each SNP, and show how to analyse the correlations between different SNPs. As well as using classical covariance and correlations, we introduce an information-theoretic measure of correlation which is based on Shannon’s informational entropy. This variational formulation also gives rise to other ways of determining the strength of a SNP’s influence on phenotype in a biallelic population using ‘field’ functions which account for the relationship between phenotype and genotype. By computing this field for each SNP, we are able to quantify the correlations between SNPs. The results are shown to depend on the number of each genostate (aa, Aa and AA) in the population in a predictable manner. The methods are illustrated using data on horse height.
{"title":"Identification of significant SNPs and the quantification of correlation using genomic informational field theory (GIFT)","authors":"Scott Gadsby , Cyril Rauch , Jonathan A D Wattis","doi":"10.1016/j.mbs.2025.109606","DOIUrl":"10.1016/j.mbs.2025.109606","url":null,"abstract":"<div><div>Given data on genotypes and phenotypes from a sample population, we show how ordering the data by phenotype and analysing the information contained in the corresponding list of genotypes can identify those SNPs which have a significant correlation with phenotype. We derive formulae for <em>p</em>-values to quantify the significance of each SNP, and show how to analyse the correlations <em>between</em> different SNPs. As well as using classical covariance and correlations, we introduce an information-theoretic measure of correlation which is based on Shannon’s informational entropy. This variational formulation also gives rise to other ways of determining the strength of a SNP’s influence on phenotype in a biallelic population using ‘field’ functions which account for the relationship between phenotype and genotype. By computing this field for each SNP, we are able to quantify the correlations between SNPs. The results are shown to depend on the number of each genostate (aa, Aa and AA) in the population in a predictable manner. The methods are illustrated using data on horse height.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"393 ","pages":"Article 109606"},"PeriodicalIF":1.8,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961005","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}
Pub Date : 2026-01-07DOI: 10.1016/j.mbs.2026.109619
Irasema Pedroza-Meza , M. Adrian Acuña-Zegarra , Jorge X. Velasco-Hernández
Vaccination is a cornerstone of infectious disease control, yet vaccines are not fully protective, leaving a fraction of the vaccinated population susceptible to infection. This partial protection can alter behavior, as individuals who perceive themselves as immune may reduce adherence to preventive measures. Motivated by this, we investigate how behavioral changes among non-immune vaccinated individuals influence the dynamics of a directly transmitted disease and the basic reproduction number. We propose a model that incorporates vaccine failure through three facets (take, degree, and duration) alongside a behavioral parameter that modifies contact rates according to compliance with mitigation measures.
Our analysis highlights the critical role of the behavioral index in key phenomena, including backward bifurcation and overall disease dynamics. We identify two thresholds. The first specifies the values of the behavioral index for which backward bifurcation does not arise, thereby indicating the conditions under which the disease may persist. The second establishes a relationship between the behavioral index and vaccine efficacy, which allows us to compare the transmission dynamics of our model with those of the classical vaccination model.
{"title":"Modeling vaccine failures and behavioral change: Effects on disease transmission dynamics and thresholds","authors":"Irasema Pedroza-Meza , M. Adrian Acuña-Zegarra , Jorge X. Velasco-Hernández","doi":"10.1016/j.mbs.2026.109619","DOIUrl":"10.1016/j.mbs.2026.109619","url":null,"abstract":"<div><div>Vaccination is a cornerstone of infectious disease control, yet vaccines are not fully protective, leaving a fraction of the vaccinated population susceptible to infection. This partial protection can alter behavior, as individuals who perceive themselves as immune may reduce adherence to preventive measures. Motivated by this, we investigate how behavioral changes among non-immune vaccinated individuals influence the dynamics of a directly transmitted disease and the basic reproduction number. We propose a model that incorporates vaccine failure through three facets (take, degree, and duration) alongside a behavioral parameter that modifies contact rates according to compliance with mitigation measures.</div><div>Our analysis highlights the critical role of the behavioral index in key phenomena, including backward bifurcation and overall disease dynamics. We identify two thresholds. The first specifies the values of the behavioral index for which backward bifurcation does not arise, thereby indicating the conditions under which the disease may persist. The second establishes a relationship between the behavioral index and vaccine efficacy, which allows us to compare the transmission dynamics of our model with those of the classical vaccination model.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"393 ","pages":"Article 109619"},"PeriodicalIF":1.8,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145947158","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}
Pub Date : 2026-01-07DOI: 10.1016/j.mbs.2026.109618
Junfang Cheng, Xue Zhang
Tick-borne diseases pose a potential threat to public health, and mathematical models have been developed and used to analyze the spread mechanisms of tick-borne diseases. An important host behavior, acquired tick resistance (ATR), to defend against tick infestation, has not yet been modeled and qualitatively analyzed. This paper proposes a model of tick-borne disease transmission incorporating ATR, where hosts are categorized into subgroups based on their infection status and tick bite counts. For the tick-host population model, we derive four distribution patterns of the host subpopulations and analyze the global asymptotic stability of the positive equilibrium. For the disease transmission model, we calculate the basic reproduction number and prove the global asymptotic stability of both the disease-free equilibrium and the endemic equilibrium. Numerical simulations illustrate that the emergence of ATR effectively reduces the number of infected hosts, while an increase in the co-feeding transmission probability leads to a rise in the number of infected ticks.
{"title":"Dynamics of a tick-borne disease transmission model with acquired tick resistance","authors":"Junfang Cheng, Xue Zhang","doi":"10.1016/j.mbs.2026.109618","DOIUrl":"10.1016/j.mbs.2026.109618","url":null,"abstract":"<div><div>Tick-borne diseases pose a potential threat to public health, and mathematical models have been developed and used to analyze the spread mechanisms of tick-borne diseases. An important host behavior, acquired tick resistance (ATR), to defend against tick infestation, has not yet been modeled and qualitatively analyzed. This paper proposes a model of tick-borne disease transmission incorporating ATR, where hosts are categorized into subgroups based on their infection status and tick bite counts. For the tick-host population model, we derive four distribution patterns of the host subpopulations and analyze the global asymptotic stability of the positive equilibrium. For the disease transmission model, we calculate the basic reproduction number and prove the global asymptotic stability of both the disease-free equilibrium and the endemic equilibrium. Numerical simulations illustrate that the emergence of ATR effectively reduces the number of infected hosts, while an increase in the co-feeding transmission probability leads to a rise in the number of infected ticks.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"393 ","pages":"Article 109618"},"PeriodicalIF":1.8,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145947179","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}
Pub Date : 2026-01-07DOI: 10.1016/j.mbs.2025.109609
Heping Jiang , Shan Gao , Hao Wang
Individual differences in predator boldness can alter encounter and attack rates, while maturation introduces biologically realistic time lags. We couple these two mechanisms in a Rosenzweig-MacArthur framework by modeling a nonlinear, personality-dependent attack rate and deriving a stage-structured maturation delay for predators, yielding a delay differential equation system. For the delay-free model, we establish positivity and boundedness, characterize boundary and interior equilibria, and provide a complete local bifurcation picture: transcritical and saddle-node bifurcations together with Hopf bifurcations that generate stable cycles; at codimension two, we prove the occurrence of cusp/Bogdanov-Takens points with accompanying homoclinic loops. Introducing maturation delay produces delay-induced complexity: multiple stability switches, sequences of Hopf bifurcations on distinct frequency branches, and global Hopf continua that connect critical delays. Analytical predictions are corroborated numerically via continuation (DDE-BIFTOOL), revealing periodic and quasi-periodic oscillations as well as bistability between coexistence and boundary states. Our results identify personality heterogeneity and developmental timing as interacting drivers of oscillatory and multistable dynamics, and provide parameter thresholds, expressed in biologically interpretable combinations, for when coexistence equilibria lose or regain stability. These findings refine theory for delayed predator-prey interactions and suggest targets (e.g., handling/harvest and juvenile survival) for stabilizing management in systems with behavioral variation.
{"title":"Predator-prey dynamics with personality-dependent foraging and maturation delay: stability switches, Hopf and Bogdanov-Takens bifurcations","authors":"Heping Jiang , Shan Gao , Hao Wang","doi":"10.1016/j.mbs.2025.109609","DOIUrl":"10.1016/j.mbs.2025.109609","url":null,"abstract":"<div><div>Individual differences in predator boldness can alter encounter and attack rates, while maturation introduces biologically realistic time lags. We couple these two mechanisms in a Rosenzweig-MacArthur framework by modeling a nonlinear, personality-dependent attack rate and deriving a stage-structured maturation delay for predators, yielding a delay differential equation system. For the delay-free model, we establish positivity and boundedness, characterize boundary and interior equilibria, and provide a complete local bifurcation picture: transcritical and saddle-node bifurcations together with Hopf bifurcations that generate stable cycles; at codimension two, we prove the occurrence of cusp/Bogdanov-Takens points with accompanying homoclinic loops. Introducing maturation delay produces delay-induced complexity: multiple stability switches, sequences of Hopf bifurcations on distinct frequency branches, and global Hopf continua that connect critical delays. Analytical predictions are corroborated numerically via continuation (DDE-BIFTOOL), revealing periodic and quasi-periodic oscillations as well as bistability between coexistence and boundary states. Our results identify personality heterogeneity and developmental timing as interacting drivers of oscillatory and multistable dynamics, and provide parameter thresholds, expressed in biologically interpretable combinations, for when coexistence equilibria lose or regain stability. These findings refine theory for delayed predator-prey interactions and suggest targets (e.g., handling/harvest and juvenile survival) for stabilizing management in systems with behavioral variation.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"393 ","pages":"Article 109609"},"PeriodicalIF":1.8,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927974","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}
Pub Date : 2026-01-02DOI: 10.1016/j.mbs.2025.109608
Kaijiao Huang , Lifei Wang , Faisal Mehmood
We introduce an SIQR epidemic model that integrates nonlinear transmission and two discrete time delays corresponding to incubation and treatment durations. The model seeks to encapsulate essential dynamical characteristics of epidemic advancement while being suitable for thorough stability examination. The local asymptotic stability of the disease-free and endemic equilibria is examined by Lyapunov-Krasovskii functionals and delay-dependent linear matrix inequalities (LMIs), which mitigate conservatism in the established stability constraints compared to time-invariant criteria. In the absence of delays, we obtain the classical Routh-Hurwitz criteria for local stability, and for positive delays, we formulate characteristic equations whose roots are examined to identify Hopf bifurcations. Transversality requirements are checked to ensure the presence of Hopf bifurcations and to determine key delay thresholds beyond which persistent oscillations form. We propose a delay-dependent feedback control rule that adaptively modifies transmission and quarantine rates; necessary conditions for stability under this control are provided in LMI form and converted into explicit, interpretable constraints on permissible delays and minimum control intensity. The model is augmented to incorporate basic demographic turnover and multi-stage infection delays to provide subgroup-specific treatment representations. Numerical simulations and bifurcation diagrams demonstrate and validate the theoretical findings, indicating how augmented delays or intensified nonlinear transmission can alter stability thresholds and provoke recurring outbreaks. Our findings quantify (i) essential delay durations that undermine stability and (ii) the control effort necessary to reestablish equilibrium, results that can be articulated as definitive decision thresholds for conversion into policy-relevant outputs.
{"title":"Stability analysis of a time-delayed SIQR epidemic model with nonlinear transmission and control parameters","authors":"Kaijiao Huang , Lifei Wang , Faisal Mehmood","doi":"10.1016/j.mbs.2025.109608","DOIUrl":"10.1016/j.mbs.2025.109608","url":null,"abstract":"<div><div>We introduce an SIQR epidemic model that integrates nonlinear transmission and two discrete time delays corresponding to incubation and treatment durations. The model seeks to encapsulate essential dynamical characteristics of epidemic advancement while being suitable for thorough stability examination. The local asymptotic stability of the disease-free and endemic equilibria is examined by Lyapunov-Krasovskii functionals and delay-dependent linear matrix inequalities (LMIs), which mitigate conservatism in the established stability constraints compared to time-invariant criteria. In the absence of delays, we obtain the classical Routh-Hurwitz criteria for local stability, and for positive delays, we formulate characteristic equations whose roots are examined to identify Hopf bifurcations. Transversality requirements are checked to ensure the presence of Hopf bifurcations and to determine key delay thresholds beyond which persistent oscillations form. We propose a delay-dependent feedback control rule that adaptively modifies transmission and quarantine rates; necessary conditions for stability under this control are provided in LMI form and converted into explicit, interpretable constraints on permissible delays and minimum control intensity. The model is augmented to incorporate basic demographic turnover and multi-stage infection delays to provide subgroup-specific treatment representations. Numerical simulations and bifurcation diagrams demonstrate and validate the theoretical findings, indicating how augmented delays or intensified nonlinear transmission can alter stability thresholds and provoke recurring outbreaks. Our findings quantify (i) essential delay durations that undermine stability and (ii) the control effort necessary to reestablish equilibrium, results that can be articulated as definitive decision thresholds for conversion into policy-relevant outputs.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"393 ","pages":"Article 109608"},"PeriodicalIF":1.8,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902060","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}
Pub Date : 2026-01-01DOI: 10.1016/j.mbs.2025.109610
Anita T. Layton
Hypertension is a global health challenge: it affects one billion people worldwide and is estimated to account for >60% of all cases or types of cardiovascular disease. Premenopausal women have lower blood pressure and hypertension prevalence compared to age-matched men, but that female protection is lost after menopause, the onset of which marks the beginning of a rapid decline in estrogen levels. The precise mechanisms by which estrogen protects premenopausal women from hypertension have yet to be elucidated. What is known is that estrogen has a plethora of interactions with other hormone systems as well as physiological processes known or hypothesized to impact the regulation of blood pressure. Thus, an objective of this study is to identify the primary contributors to the estrogen-mediated cardiovascular protection. To accomplish that goal, we develop a blood pressure regulation model that incorporates the effects of estrogen on the renin-angiotensin system, the reactivity of renal sympathetic nervous activity, vascular tone, and renal epithelial transport. Model simulations suggest that estrogen’s vasodilatory effect, especially on the afferent arterioles, is the largest cause of premenopausal women’s lower blood pressure and resistance to developing hypertension. Furthermore, the model predicts that angiotensin receptor blockers are more effective than angiotensin converting enzyme inhibitors in treating hypertensive women throughout their lifespan, even as estrogen levels decline.
{"title":"Modulation of blood pressure by estrogen: A modeling analysis","authors":"Anita T. Layton","doi":"10.1016/j.mbs.2025.109610","DOIUrl":"10.1016/j.mbs.2025.109610","url":null,"abstract":"<div><div>Hypertension is a global health challenge: it affects one billion people worldwide and is estimated to account for >60% of all cases or types of cardiovascular disease. Premenopausal women have lower blood pressure and hypertension prevalence compared to age-matched men, but that female protection is lost after menopause, the onset of which marks the beginning of a rapid decline in estrogen levels. The precise mechanisms by which estrogen protects premenopausal women from hypertension have yet to be elucidated. What is known is that estrogen has a plethora of interactions with other hormone systems as well as physiological processes known or hypothesized to impact the regulation of blood pressure. Thus, an objective of this study is to identify the primary contributors to the estrogen-mediated cardiovascular protection. To accomplish that goal, we develop a blood pressure regulation model that incorporates the effects of estrogen on the renin-angiotensin system, the reactivity of renal sympathetic nervous activity, vascular tone, and renal epithelial transport. Model simulations suggest that estrogen’s vasodilatory effect, especially on the afferent arterioles, is the largest cause of premenopausal women’s lower blood pressure and resistance to developing hypertension. Furthermore, the model predicts that angiotensin receptor blockers are more effective than angiotensin converting enzyme inhibitors in treating hypertensive women throughout their lifespan, even as estrogen levels decline.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"393 ","pages":"Article 109610"},"PeriodicalIF":1.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886170","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}
Pub Date : 2025-12-30DOI: 10.1016/j.mbs.2025.109607
F.E. Cornes , L. Rivero Gonzalez , M. Otero
We developed a stochastic dynamic model to simulate the development of Peregrinus maidis, capturing key ecological interactions and stage-structured population dynamics of this maize-specialist insect. The model incorporates a density-dependent regulation mechanism that acts during the nymphal stage, with population size constrained by a fixed pseudo-carrying capacity. Development rates are temperature-dependent, allowing the model to capture the insect’s sensitivity to environmental conditions. Simulation results reveal two distinct population regimes: extinction and persistence. Persistence is characterized by stable equilibrium distributions across life stages, with peak abundances occurring near 25 ∘C. In contrast, low temperatures (below 20 ∘C) and limited resource availability significantly increase extinction probability. The analysis also highlights the buffering role of high pseudo-carrying capacities against demographic collapse. Importantly, our simulations of “quasi-extinction” times indicate that local populations often collapse within 1.5-4 months, a range comparable to or shorter than the harvest-to-sowing interval in many maize-based cropping systems, thereby highlighting the potential role of migration or alternative hosts in sustaining persistence. In this framework, population regulation is governed by density-dependent effects through a constant pseudo-carrying capacity, while temperature modulates development rates. These findings provide a mechanistic basis for understanding how stochasticity, nonlinearity, and environmental drivers shape insect population dynamics, with potential applications for anticipating pest behavior under variable climatic and agronomic conditions.
{"title":"Extinction and persistence of Peregrinus maidis: Stochastic modeling under thermal, density-dependent, and maize off-season constraints","authors":"F.E. Cornes , L. Rivero Gonzalez , M. Otero","doi":"10.1016/j.mbs.2025.109607","DOIUrl":"10.1016/j.mbs.2025.109607","url":null,"abstract":"<div><div>We developed a stochastic dynamic model to simulate the development of <em>Peregrinus maidis</em>, capturing key ecological interactions and stage-structured population dynamics of this maize-specialist insect. The model incorporates a density-dependent regulation mechanism that acts during the nymphal stage, with population size constrained by a fixed pseudo-carrying capacity. Development rates are temperature-dependent, allowing the model to capture the insect’s sensitivity to environmental conditions. Simulation results reveal two distinct population regimes: extinction and persistence. Persistence is characterized by stable equilibrium distributions across life stages, with peak abundances occurring near 25 <sup>∘</sup>C. In contrast, low temperatures (below 20 <sup>∘</sup>C) and limited resource availability significantly increase extinction probability. The analysis also highlights the buffering role of high pseudo-carrying capacities against demographic collapse. Importantly, our simulations of “quasi-extinction” times indicate that local populations often collapse within 1.5-4 months, a range comparable to or shorter than the harvest-to-sowing interval in many maize-based cropping systems, thereby highlighting the potential role of migration or alternative hosts in sustaining persistence. In this framework, population regulation is governed by density-dependent effects through a constant pseudo-carrying capacity, while temperature modulates development rates. These findings provide a mechanistic basis for understanding how stochasticity, nonlinearity, and environmental drivers shape insect population dynamics, with potential applications for anticipating pest behavior under variable climatic and agronomic conditions.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"393 ","pages":"Article 109607"},"PeriodicalIF":1.8,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886169","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}
Pub Date : 2025-12-25DOI: 10.1016/j.mbs.2025.109605
David J. Albers , George Hripcsak , Lena Mamykina , Melike Sirlanci , Esteban G. Tabak
This article develops a novel multiobjective data assimilation methodology, addressing challenges that are common in real-world settings, such as severe sparsity of observations, lack of reliable models, and non-stationarity of the system dynamics. These challenges often cause issues and can confound model parameter estimation and initialization that can lead to estimated models with unrealistic qualitative dynamics and induce qualitative and quantitative parameter estimation errors. The proposed multiobjective function is constructed as a sum of components, each serving a different purpose: enforcing point-wise and distribution-wise agreement between data and model output, enforcing agreement of variables and parameters with a model provided, and penalizing unrealistic rapid parameter changes, unless they are due to external drivers or interventions. This methodology was motivated by, developed and evaluated in the context of estimating blood glucose levels in different medical settings. Both simulated and real data are used to evaluate the methodology from different perspectives, such as its ability to estimate unmeasured variables, its ability to reproduce the correct qualitative blood glucose dynamics, how it manages non-stationarity, and how it performs when given a range of dense and severely sparse data. The results show that a multicomponent cost function can balance the minimization of point-wise errors with global properties, robustly preserving correct qualitative dynamics and managing data sparsity.
{"title":"A multiobjective optimization approach to data assimilation for complex biological systems with sparse data","authors":"David J. Albers , George Hripcsak , Lena Mamykina , Melike Sirlanci , Esteban G. Tabak","doi":"10.1016/j.mbs.2025.109605","DOIUrl":"10.1016/j.mbs.2025.109605","url":null,"abstract":"<div><div>This article develops a novel multiobjective data assimilation methodology, addressing challenges that are common in real-world settings, such as severe sparsity of observations, lack of reliable models, and non-stationarity of the system dynamics. These challenges often cause issues and can confound model parameter estimation and initialization that can lead to estimated models with unrealistic qualitative dynamics and induce qualitative and quantitative parameter estimation errors. The proposed multiobjective function is constructed as a sum of components, each serving a different purpose: enforcing point-wise and distribution-wise agreement between data and model output, enforcing agreement of variables and parameters with a model provided, and penalizing unrealistic rapid parameter changes, unless they are due to external drivers or interventions. This methodology was motivated by, developed and evaluated in the context of estimating blood glucose levels in different medical settings. Both simulated and real data are used to evaluate the methodology from different perspectives, such as its ability to estimate unmeasured variables, its ability to reproduce the correct qualitative blood glucose dynamics, how it manages non-stationarity, and how it performs when given a range of dense and severely sparse data. The results show that a multicomponent cost function can balance the minimization of point-wise errors with global properties, robustly preserving correct qualitative dynamics and managing data sparsity.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"393 ","pages":"Article 109605"},"PeriodicalIF":1.8,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145846585","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}
Pub Date : 2025-12-24DOI: 10.1016/j.mbs.2025.109601
Asma Azizi , Zhuolin Qu , Caner Kazanci
Human behavioral changes in response to observing disease in others play a crucial role in the spread of epidemics. These behaviors create selective pressures that influence a virus’s ability to survive. This study explores how human behavioral adaptations influence the co-evolution of symptomatic and asymptomatic pathogen strains during an epidemic. Using a deterministic Susceptible-Infectious-Removed (SIR) model, it examines the role of spontaneous social distancing (SD) in shaping the selection pressures on these strains. The analysis highlights how behavioral changes can drive shifts in the prevalence of symptomatic versus asymptomatic cases, offering insights into the evolutionary dynamics of pathogen variants. Individuals initiate SD after contact with symptomatic cases, either by reducing interactions with everyone or by specifically avoiding symptomatic individuals. The analysis shows that homogeneous contact reduction tends to favor symptomatic strain, while targeted avoidance of symptomatic cases promotes the selection of asymptomatic one. The study underscores the complex, non-linear dynamics of selections under different levels of social distancing. A global sensitivity analysis highlights the significance of behavioral parameters in controlling the overall size of the infection. The findings emphasize the need for public health strategies that account for human behavior to effectively limit the spread and evolution of viral strains.
{"title":"How human behavior drives the balance of symptomatic and asymptomatic cases in emerging infections","authors":"Asma Azizi , Zhuolin Qu , Caner Kazanci","doi":"10.1016/j.mbs.2025.109601","DOIUrl":"10.1016/j.mbs.2025.109601","url":null,"abstract":"<div><div>Human behavioral changes in response to observing disease in others play a crucial role in the spread of epidemics. These behaviors create selective pressures that influence a virus’s ability to survive. This study explores how human behavioral adaptations influence the co-evolution of symptomatic and asymptomatic pathogen strains during an epidemic. Using a deterministic Susceptible-Infectious-Removed (SIR) model, it examines the role of spontaneous social distancing (SD) in shaping the selection pressures on these strains. The analysis highlights how behavioral changes can drive shifts in the prevalence of symptomatic versus asymptomatic cases, offering insights into the evolutionary dynamics of pathogen variants. Individuals initiate SD after contact with symptomatic cases, either by reducing interactions with everyone or by specifically avoiding symptomatic individuals. The analysis shows that homogeneous contact reduction tends to favor symptomatic strain, while targeted avoidance of symptomatic cases promotes the selection of asymptomatic one. The study underscores the complex, non-linear dynamics of selections under different levels of social distancing. A global sensitivity analysis highlights the significance of behavioral parameters in controlling the overall size of the infection. The findings emphasize the need for public health strategies that account for human behavior to effectively limit the spread and evolution of viral strains.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"392 ","pages":"Article 109601"},"PeriodicalIF":1.8,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145844636","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}
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