Mathematical Biosciences and Engineering最新文献

{"title":"Using mathematical modeling to study the dynamics of Legionnaires' disease and consider management options.","authors":"Mark Z Wang, Christina J Edholm, Lihong Zhao","doi":"10.3934/mbe.2025045","DOIUrl":"https://doi.org/10.3934/mbe.2025045","url":null,"abstract":"<p><p>Legionnaires' disease (LD) is a largely understudied and underreported pneumonic environmentally transmitted disease caused by the bacteria Legionella. It primarily occurs in places with poorly maintained artificial sources of water. There is currently a lack of mathematical models on the dynamics of LD. In this paper, we formulate a novel ordinary differential equation-based susceptible-exposed-infected-recovered (SEIR) model for LD. One issue with LD is the difficulty in its detection, as the majority of countries around the world lack the proper surveillance and diagnosis methods. Thus, there is not much publicly available data or literature on LD. We use parameter estimation for our model with one of the few outbreaks with time series data from Murcia, Spain in 2001. Furthermore, we apply a global sensitivity analysis to understand the contributions of parameters to our model output. To consider managing LD outbreaks, we explore implementing sanitizing individual sources of water by constructing an optimal control problem. Using our fitted model and the optimal control problem, we analyze how different parameters and controls might help manage LD outbreaks in the future.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 5","pages":"1226-1242"},"PeriodicalIF":2.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144023360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic forest transition model dynamics and parameter estimation via deep learning.","authors":"Satoshi Kumabe, Tianyu Song, Tôn Việt Tạ","doi":"10.3934/mbe.2025046","DOIUrl":"https://doi.org/10.3934/mbe.2025046","url":null,"abstract":"<p><p>Forest transitions, characterized by dynamic shifts between forest, agricultural, and abandoned lands, are complex phenomena. This study developed a stochastic differential equation model to capture the intricate dynamics of these transitions. We established the existence of global positive solutions for the model and conducted numerical analyses to assess the impact of model parameters on deforestation incentives. To address the challenge of parameter estimation, we proposed a novel deep learning approach that estimates all model parameters from a single sample containing time-series observations of forest and agricultural land proportions. This innovative approach enables us to understand forest transition dynamics and deforestation trends at any future time.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 5","pages":"1243-1262"},"PeriodicalIF":2.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematical modeling of the synergetic effect between radiotherapy and immunotherapy.","authors":"Yixun Xing, Casey Moore, Debabrata Saha, Dan Nguyen, MaryLena Bleile, Xun Jia, Robert Timmerman, Hao Peng, Steve Jiang","doi":"10.3934/mbe.2025044","DOIUrl":"https://doi.org/10.3934/mbe.2025044","url":null,"abstract":"<p><p>The synergy between radiotherapy and immunotherapy plays a pivotal role in enhancing tumor control and treatment outcomes. To explore the underlying mechanisms of synergy, we investigated a novel treatment approach known as personalized ultra-fractionated stereotactic adaptive radiation (PULSAR) therapy, which emphasizes the impact of radiation timing. Unlike conventional daily treatments, PULSAR delivers high-dose radiation in spaced intervals over weeks or months, enabling tumors to adapt and potentially enhancing synergy with immunotherapy. Drawing on insights from small-animal radiation studies, we developed a discrete-time model based on multiple difference equations to elucidate the temporal dynamics of tumor control driven by both radiation and the adaptive immune response. By accounting for the migration and infiltration of T cells within the tumor microenvironment, we established a quantitative link between radiation therapy and immunotherapy. Model parameters were estimated using a simulated annealing algorithm applied to training data, and our model achieved high accuracy for the test data with a root mean square error of 287 mm³. Notably, our framework replicated the PULSAR effect observed in animal studies, revealing that longer intervals between radiation treatments in the context of immunotherapy yielded enhanced tumor control. Specifically, mice receiving immunotherapy alongside radiation pulses delivered at extended intervals, ten days, showed markedly improved tumor responses, whereas those treated with shorter intervals did not achieve comparable benefits. Moreover, our model offers an in-silico tool for designing future personalized ultra-fractionated stereotactic adaptive radiation trials. Overall, these findings underscore the critical importance of treatment timing in harnessing the synergy between radiotherapy and immunotherapy and highlight the potential of our model to optimize and individualize treatment protocols.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 5","pages":"1206-1225"},"PeriodicalIF":2.6,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144043744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling human response to information in voluntary vaccination behaviour using epidemic data.","authors":"Bruno Buonomo, Rossella Della Marca, Manalebish Debalike Asfaw","doi":"10.3934/mbe.2025043","DOIUrl":"https://doi.org/10.3934/mbe.2025043","url":null,"abstract":"<p><p>Here, we considered Holling functional responses, a core concept in population dynamics, and discussed their potential interpretation in the context of social epidemiology. Then, we assessed which Holling functional response best represents the vaccination behaviour of individuals when such a behaviour is influenced by information and rumours about the disease. In particular, we used the Holling functionals to represent the information-dependent vaccination rate in a socio-epidemiological model for meningococcal meningitis. As a field case test, we estimated the information-related parameters by using official data from a meningitis outbreak in Nigeria and numerically assessed the impact of the functionals on the solutions of the model. We observed significant inaccuracies on parameter estimates when either Holling type Ⅰ or Holling type Ⅲ functional were used. On the contrary, when the Holling type Ⅱ functional was employed, epidemiological data were well reproduced, and reasonable values of the information parameters were obtained. Given the socio-epidemiological interpretation of the Holling type Ⅱ functional, this means that the rate at which susceptible individuals come into contact with information may be assumed to be constant and that the time needed to handle the available information cannot be neglected.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 5","pages":"1185-1205"},"PeriodicalIF":2.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamical analysis of a predator-prey system with fear-induced dispersal between patches.","authors":"Jin Zhong, Yue Xia, Lijuan Chen, Fengde Chen","doi":"10.3934/mbe.2025042","DOIUrl":"https://doi.org/10.3934/mbe.2025042","url":null,"abstract":"<p><p>In this paper, a patchy model in which the migration is induced by the fear effect on the predator was investigated. By applying dynamical theory, the complete study on persistence of the system and the local/global stability of equilibria were discussed. Choosing the diffusion coefficient $ D_1 $ as the bifurcation parameter, transcritical bifurcation occurring near the trivial equilibrium was demonstrated. We concluded that low dispersal is favorable for the coexistence of both species, but large dispersal leads to the extinction of species. There is an optimal diffusion coefficient to make the density of the prey population reach its maximum. In addition, the level of fear effect $ k $ and the maximum fear cost $ eta $ are beneficial to the total population density of prey.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 5","pages":"1159-1184"},"PeriodicalIF":2.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143996024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial neural networks to predict the presence of Neosporosis in cattle.","authors":"Javier Antonio Ballesteros-Ricaurte, Ramon Fabregat, Angela Carrillo-Ramos, Carlos Parra, Andrés Moreno","doi":"10.3934/mbe.2025041","DOIUrl":"https://doi.org/10.3934/mbe.2025041","url":null,"abstract":"<p><p>The prediction of bovine infectious diseases is a constant challenge as generally, only laboratory data is available not allowing the study of their relationship with each disease's risk factors. The diseases neosporosis and bovine viral diarrhea, which are present in Colombia, the United States, Mexico, Brazil, and Argentina, cause reproductive problems in cattle and generate economic losses for ranchers. Although there are mathematical models that can evaluate which cattle are susceptible to these diseases, these provide limited information, maintaining the need for a model that provides information on both transmission and mechanisms for controlling the disease. In this article, a machine learning model is presented that combines laboratory data with risk factors in a neural network to predict the presence of bovine neosporosis. The proposed model was implemented with data from previous studies conducted in the municipality of Sotaquirá, Boyacá, Colombia, and obtained an accuracy of 94% in predicting the presence of the disease. It can be concluded that incorporating laboratory data into machine learning algorithms improves the prediction of the presence of these diseases. Furthermore, the proposed system not only predicts but also provides useful information for clinical decision-making, making it a valuable tool in the veterinary field.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 5","pages":"1140-1158"},"PeriodicalIF":2.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-group epidemic model with heterogeneity in cognitive effects.","authors":"Zehan Liu, Daoxin Qiu, Shengqiang Liu","doi":"10.3934/mbe.2025040","DOIUrl":"https://doi.org/10.3934/mbe.2025040","url":null,"abstract":"<p><p>During the outbreak of new infectious diseases, media information and medical resources play crucial roles in shaping the dynamics of disease transmission. To investigate the combined impact of media information and limited medical resources on disease spread, we proposed a two-group compartmental model. This model divided the population into two groups based on their ability to receive information. We derived the basic reproduction number, analyzed the local stability of the disease-free equilibrium, and examined the conditions under which disease extinction or persistence occured. For control strategies, we explored both constant and optimal control approaches under the constraint of limited media resources. Numerical simulations indicated that enhancing the population's responsiveness to media and medical resources helped reduce the infection rate. The model also exhibited complex dynamical behaviors, such as backward bifurcation, forward-backward bifurcation, and homoclinic bifurcation, which presented significant challenges for disease control. Furthermore, we conducted numerical simulations of the optimal control problem to validate and support our theoretical findings. In the case of constant control, as the disparity between the two populations increases, media resources should be increasingly allocated to the information-insensitive group. For optimal control, we employed the forward-backward sweep method, where media resources were increasingly allocated to information-insensitive groups as population heterogeneity rises. This study established an empirical framework for optimizing media-driven public health communication strategies, offering critical insights into the strategic allocation of limited media resources across heterogeneous populations.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 5","pages":"1109-1139"},"PeriodicalIF":2.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finite mixture models of superspreading in epidemics.","authors":"Suzanne M O'Regan, John M Drake","doi":"10.3934/mbe.2025039","DOIUrl":"https://doi.org/10.3934/mbe.2025039","url":null,"abstract":"<p><p>Superspreading transmission is usually modeled using the negative binomial distribution, simply because its variance is larger than the mean and it can be long-tailed. However, populations are often partitioned into groups by social, behavioral, or environmental risk factors, particularly in closed settings such as workplaces or care homes. While heterogeneities in infectious histories and contact structure have been considered separately, models for superspreading events that include the joint effects of social and biological risk factors are lacking. To address this need, we developed a mechanistic finite mixture model for the number of secondary infections that unites population partitioning with individual-level heterogeneity in infectious period duration. We showed that the variance in the number of secondary infections is composed of both sources of heterogeneity: risk group structuring and infectiousness. We used the model to construct the outbreak size distribution and to derive critical thresholds for elimination resulting from control activities that differentially target the high-contact subpopulation vs. the population at large. We compared our model with the standard negative binomial distribution and showed that the tail behavior of the outbreak size distribution under a finite mixture model differs substantially. Our results indicate that even if the infectious period follows a bell-shaped distribution, heterogeneity in outbreak sizes may arise due to the influence of population risk structure.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 5","pages":"1081-1108"},"PeriodicalIF":2.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144004779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A contiunous-time SIS criss-cross model of co-infection in a heterogeneous population.","authors":"Marcin Choiński","doi":"10.3934/mbe.2025038","DOIUrl":"https://doi.org/10.3934/mbe.2025038","url":null,"abstract":"<p><p>In this paper, we introduce and analyze a contiunous-time model of co-infection dynamics in a heterogeneous population consisting of two subpopulations that differ in the risk of getting infected by individuals with two diseases. We assume that each parameter reflecting a given process for each subpopulation has different values, which makes the population completely heterogeneous. Such complexity and the population heterogeneity make our paper unique, reflecting co-infection dynamics. Moreover, we establish an epidemic spread for each disease not only in a sole subpopulation but also with criss-cross transmission, meaning between different subpopulations. The proposed system has a disease-free stationary state and two states reflecting the presence of one disease. We indicate conditions for their existence and local stability. The conditions for the local stability for states reflecting one disease have a complicated form, so we strengthened them so that they are more transparent. Investigation on the existence of a postulated endemic state corresponding to both disease's presence leads to a complex analysis, which is why we only provide an insight on this issue. Here, we also provide the basic reproduction number of our model and investigate properties of this number. The system has a universal structure; as such, it can be applied to investigate co-infection of different infectious diseases.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 5","pages":"1055-1080"},"PeriodicalIF":2.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144054879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The time-changed stochastic approach and fractionally integrated processes to model the actin-myosin interaction and dwell times.","authors":"Nikolai Leonenko, Enrica Pirozzi","doi":"10.3934/mbe.2025037","DOIUrl":"https://doi.org/10.3934/mbe.2025037","url":null,"abstract":"<p><p>We propose two stochastic models for the interaction between the myosin head and the actin filament, the physio-chemical mechanism triggering muscle contraction and that is not yet completely understood. We make use of the fractional calculus approach with the purpose of constructing non-Markov processes for models with $ memory. $ A time-changed process and a fractionally integrated process are proposed for the two models. Each of these includes memory effects in a different way. We describe such features from a theoretical point of view and with simulations of sample paths. Mean functions and covariances are provided, considering constant and time-dependent tilting forces by which effects of external loads are included. The investigation of the dwell time of such phenomenon is carried out by means of density estimations of the first exit time (FET) of the processes from a strip; this mimics the times of the Steps of the myosin head during the sliding movement outside a potential well due to the interaction with actin. For the case of time-changed diffusion process, we specify an equation for the probability density function of the FET from a strip. The schemes of two simulation algorithms are provided and performed. Some numerical and simulation results are given and discussed.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 4","pages":"1019-1054"},"PeriodicalIF":2.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144010876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}