Pub Date : 2025-01-26DOI: 10.1016/j.mbs.2025.109380
Katelyn Plaisier Leisman , Shinhae Park , Sarah Simpson , Zoi Rapti
An epidemiological model with a minimal number of parameters is introduced and its structural and practical identifiabity is investigated both analytically and numerically. The model is useful when a high percentage of unreported cases is suspected, hence only hospitalization data are used to fit the model parameters and calculate the basic reproductive number and the effective reproductive number . As a case study, the model is used to study the initial surge and the Omicron wave of the COVID-19 epidemic in Belgium. It was found that the reported cases largely underestimate the actual cases, and the estimated values of are consistent with other studies. The exact number of people initially in each epidemiological class is also considered unknown and was estimated directly and not considered as additional parameters to be fitted. Furthermore, the parameter fitting was performed with two different available data sets, in order to improve confidence. The methodology presented here can be easily modified to study outbreaks of diseases for which little information on confirmed cases is known a priori or when the available information is largely unreliable.
{"title":"A simple model for the analysis of epidemics based on hospitalization data","authors":"Katelyn Plaisier Leisman , Shinhae Park , Sarah Simpson , Zoi Rapti","doi":"10.1016/j.mbs.2025.109380","DOIUrl":"10.1016/j.mbs.2025.109380","url":null,"abstract":"<div><div>An epidemiological model with a minimal number of parameters is introduced and its structural and practical identifiabity is investigated both analytically and numerically. The model is useful when a high percentage of unreported cases is suspected, hence only hospitalization data are used to fit the model parameters and calculate the basic reproductive number <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> and the effective reproductive number <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>e</mi></mrow></msub></math></span>. As a case study, the model is used to study the initial surge and the Omicron wave of the COVID-19 epidemic in Belgium. It was found that the reported cases largely underestimate the actual cases, and the estimated values of <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> are consistent with other studies. The exact number of people initially in each epidemiological class is also considered unknown and was estimated directly and not considered as additional parameters to be fitted. Furthermore, the parameter fitting was performed with two different available data sets, in order to improve confidence. The methodology presented here can be easily modified to study outbreaks of diseases for which little information on confirmed cases is known a priori or when the available information is largely unreliable.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"381 ","pages":"Article 109380"},"PeriodicalIF":1.9,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143061909","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 : 2025-01-18DOI: 10.1016/j.mbs.2025.109375
Kristin P. Kim, Christopher A. Lemmon
One of the main drivers of fibrotic diseases is epithelial–mesenchymal transition (EMT): a transdifferentiation process in which cells undergo a phenotypic change from an epithelial state to a pro-migratory state. The cytokine transforming growth factor-1 (TGF-1) has been previously shown to regulate EMT. TGF-1 binds to fibronectin (FN) fibrils, which are the primary extracellular matrix (ECM) component in renal fibrosis. We have previously demonstrated experimentally that inhibition of FN fibrillogenesis and/or TGF-1 tethering to FN inhibits EMT. However, these studies have only been conducted on 2-D cell monolayers, and the role of TGF-1-FN tethering in 3-D cellular environments is not clear. As such, we sought to develop a 3-D computational model of epithelial spheroids that captured both EMT signaling dynamics and TGF-1-FN tethering dynamics. We have incorporated the bi-stable EMT switch model developed by Tian et al. (2013) into a 3-D multicellular model to capture both temporal and spatial TGF-1 signaling dynamics. We showed that the addition of increasing concentrations of exogeneous TGF-1 led to faster EMT progression, indicated by increased expression of mesenchymal markers, decreased cell proliferation and increased migration. We then incorporated TGF-1-FN fibril tethering by locally reducing the TGF-1 diffusion coefficient as a function of EMT to simulate the reduced movement of TGF-1 when tethered to FN fibrils during fibrosis. We showed that incorporation of TGF-1 tethering to FN fibrils promoted a partial EMT state, independent of exogenous TGF-1 concentration, indicating a mechanism by which fibrotic ECM can promote a partial EMT state.
{"title":"Fibrotic extracellular matrix preferentially induces a partial Epithelial–Mesenchymal Transition phenotype in a 3-D agent based model of fibrosis","authors":"Kristin P. Kim, Christopher A. Lemmon","doi":"10.1016/j.mbs.2025.109375","DOIUrl":"10.1016/j.mbs.2025.109375","url":null,"abstract":"<div><div>One of the main drivers of fibrotic diseases is epithelial–mesenchymal transition (EMT): a transdifferentiation process in which cells undergo a phenotypic change from an epithelial state to a pro-migratory state. The cytokine transforming growth factor-<span><math><mi>β</mi></math></span>1 (TGF-<span><math><mi>β</mi></math></span>1) has been previously shown to regulate EMT. TGF-<span><math><mi>β</mi></math></span>1 binds to fibronectin (FN) fibrils, which are the primary extracellular matrix (ECM) component in renal fibrosis. We have previously demonstrated experimentally that inhibition of FN fibrillogenesis and/or TGF-<span><math><mi>β</mi></math></span>1 tethering to FN inhibits EMT. However, these studies have only been conducted on 2-D cell monolayers, and the role of TGF-<span><math><mi>β</mi></math></span>1-FN tethering in 3-D cellular environments is not clear. As such, we sought to develop a 3-D computational model of epithelial spheroids that captured both EMT signaling dynamics and TGF-<span><math><mi>β</mi></math></span>1-FN tethering dynamics. We have incorporated the bi-stable EMT switch model developed by Tian et al. (2013) into a 3-D multicellular model to capture both temporal and spatial TGF-<span><math><mi>β</mi></math></span>1 signaling dynamics. We showed that the addition of increasing concentrations of exogeneous TGF-<span><math><mi>β</mi></math></span>1 led to faster EMT progression, indicated by increased expression of mesenchymal markers, decreased cell proliferation and increased migration. We then incorporated TGF-<span><math><mi>β</mi></math></span>1-FN fibril tethering by locally reducing the TGF-<span><math><mi>β</mi></math></span>1 diffusion coefficient as a function of EMT to simulate the reduced movement of TGF-<span><math><mi>β</mi></math></span>1 when tethered to FN fibrils during fibrosis. We showed that incorporation of TGF-<span><math><mi>β</mi></math></span>1 tethering to FN fibrils promoted a partial EMT state, independent of exogenous TGF-<span><math><mi>β</mi></math></span>1 concentration, indicating a mechanism by which fibrotic ECM can promote a partial EMT state.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"381 ","pages":"Article 109375"},"PeriodicalIF":1.9,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143019031","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 : 2025-01-01DOI: 10.1016/j.mbs.2024.109343
Paul Torrillo , David Swigon
Experimental research suggests that local patterns in DNA sequences can result in stiffer or more curved structures, potentially impacting chromatin formation, transcription regulation, and other processes. However, the effect of sequence variation on DNA geometry and mechanics remains relatively underexplored. Using rigid base pair models to aid rapid computation, we investigated the sample space of 100 bp DNA sequences to identify mechanical extrema based on metrics such as static persistence length, global bend, or angular deviation. Our results show that repetitive DNA motifs are overrepresented in these extrema. We identified specific extremal motifs and demonstrated that their geometric and mechanical properties significantly differ from standard DNA through hierarchical clustering. We provide a mathematical argument supporting the presence of DNA repeats in extremizing sequences. Finally, we find that repetitive DNA motifs with extreme mechanical properties are prevalent in genetic databases and hypothesize that their unique mechanical properties could contribute to this abundance.
实验研究表明，DNA 序列的局部模式可导致结构更坚硬或更弯曲，从而对染色质形成、转录调控和其他过程产生潜在影响。然而，序列变异对 DNA 几何学和力学的影响仍然相对缺乏探索。利用刚性碱基对模型帮助快速计算，我们研究了 100 bp DNA 序列的样本空间，根据静态持续长度、全局弯曲度或角度偏差等指标确定力学极值。我们的研究结果表明，重复的 DNA 主题在这些极值中的比例过高。我们确定了特定的极值图案，并通过分层聚类证明它们的几何和机械特性与标准 DNA 有显著不同。我们提供了支持极端化序列中存在 DNA 重复的数学论据。最后，我们发现具有极端机械特性的重复 DNA 主题在基因数据库中非常普遍，并假设它们独特的机械特性可能是造成这种现象的原因。
{"title":"Mechanical causes and implications of repetitive DNA motifs","authors":"Paul Torrillo , David Swigon","doi":"10.1016/j.mbs.2024.109343","DOIUrl":"10.1016/j.mbs.2024.109343","url":null,"abstract":"<div><div>Experimental research suggests that local patterns in DNA sequences can result in stiffer or more curved structures, potentially impacting chromatin formation, transcription regulation, and other processes. However, the effect of sequence variation on DNA geometry and mechanics remains relatively underexplored. Using rigid base pair models to aid rapid computation, we investigated the sample space of 100 bp DNA sequences to identify mechanical extrema based on metrics such as static persistence length, global bend, or angular deviation. Our results show that repetitive DNA motifs are overrepresented in these extrema. We identified specific extremal motifs and demonstrated that their geometric and mechanical properties significantly differ from standard DNA through hierarchical clustering. We provide a mathematical argument supporting the presence of DNA repeats in extremizing sequences. Finally, we find that repetitive DNA motifs with extreme mechanical properties are prevalent in genetic databases and hypothesize that their unique mechanical properties could contribute to this abundance.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"379 ","pages":"Article 109343"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690160","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 : 2024-11-23DOI: 10.1016/j.mbs.2024.109341
Xiumei Deng , Qihua Huang , Zhi-An Wang
In this paper, we develop a reaction–diffusion model with negative toxicant–taxis that incorporates spatiotemporally inhomogeneous toxicant input to investigate the impact of toxicants on the competitive dynamics of two species in a polluted aquatic environment. Here the negative toxicant–taxis models the evasive movement of avoiding toxicants by species. We establish the global well-posedness of the model, analyze the existence and stability of spatially homogeneous steady states, and derive sufficient conditions for species extinction and coexistence. Through linear stability analysis, we identify sufficient conditions on model parameters that destabilize spatially homogeneous steady states under spatiotemporally uniform toxicant input. Numerical experiments reveal the influence of key toxicant-related factors (input rate, taxis intensity, and diffusivity) on competition outcomes and species distributions. Notably, strong negative toxicant–taxis can induce spatial aggregation and segregation patterns between the species and the toxicant under uniform toxicant input. Our findings suggest that toxicant–taxis may promote population persistence and coexistence, particularly when the toxicant input is not uniform in space and time and the toxicant does not diffuse fast (i.e. weak diffusivity). However, strong toxicant diffusion can diminish the impact of taxis, adversely affecting population persistence and species coexistence.
{"title":"A spatiotemporal model for the effects of toxicants on the competitive dynamics of aquatic species","authors":"Xiumei Deng , Qihua Huang , Zhi-An Wang","doi":"10.1016/j.mbs.2024.109341","DOIUrl":"10.1016/j.mbs.2024.109341","url":null,"abstract":"<div><div>In this paper, we develop a reaction–diffusion model with negative toxicant–taxis that incorporates spatiotemporally inhomogeneous toxicant input to investigate the impact of toxicants on the competitive dynamics of two species in a polluted aquatic environment. Here the negative toxicant–taxis models the evasive movement of avoiding toxicants by species. We establish the global well-posedness of the model, analyze the existence and stability of spatially homogeneous steady states, and derive sufficient conditions for species extinction and coexistence. Through linear stability analysis, we identify sufficient conditions on model parameters that destabilize spatially homogeneous steady states under spatiotemporally uniform toxicant input. Numerical experiments reveal the influence of key toxicant-related factors (input rate, taxis intensity, and diffusivity) on competition outcomes and species distributions. Notably, strong negative toxicant–taxis can induce spatial aggregation and segregation patterns between the species and the toxicant under uniform toxicant input. Our findings suggest that toxicant–taxis may promote population persistence and coexistence, particularly when the toxicant input is not uniform in space and time and the toxicant does not diffuse fast (i.e. weak diffusivity). However, strong toxicant diffusion can diminish the impact of taxis, adversely affecting population persistence and species coexistence.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"379 ","pages":"Article 109341"},"PeriodicalIF":1.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142718113","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 : 2024-11-23DOI: 10.1016/j.mbs.2024.109342
Teddy Lazebnik , Avner Friedman
Senescent cells are cells that stop dividing but sustain viability. Cellular senescence is the hallmark of aging, but senescence also appears in cancer, triggered by cells stress, tumor suppression of gene activation, and oncogene activity. In lung cancer, senescent cancer cells secrete VEGF, which initiates a process of angiogenesis, enabling the cancer to grow and proliferate. Chemotherapy kills cancer cells, but some cancer cells become senescent. Hence, a senolytic drug, a drug that eliminates senescent cells, should significantly improve the efficacy of chemotherapy. In this paper, we developed a mathematical spatio-temporal model of combination chemotherapy with senolytic drug in treatment of lung cancer. Model’s simulations of tumor volume growth are shown to agree with mouse experiments in the case where cyclophosphamide is combined with the senolytic drug fisetin. It is then shown how the model can be used to assess the benefits of treatments with different combinations and different schedules of the two drugs in order to achieve optimal tumor volume reduction.
{"title":"Spatio-temporal model of combining chemotherapy with senolytic treatment in lung cancer","authors":"Teddy Lazebnik , Avner Friedman","doi":"10.1016/j.mbs.2024.109342","DOIUrl":"10.1016/j.mbs.2024.109342","url":null,"abstract":"<div><div>Senescent cells are cells that stop dividing but sustain viability. Cellular senescence is the hallmark of aging, but senescence also appears in cancer, triggered by cells stress, tumor suppression of gene activation, and oncogene activity. In lung cancer, senescent cancer cells secrete VEGF, which initiates a process of angiogenesis, enabling the cancer to grow and proliferate. Chemotherapy kills cancer cells, but some cancer cells become senescent. Hence, a senolytic drug, a drug that eliminates senescent cells, should significantly improve the efficacy of chemotherapy. In this paper, we developed a mathematical spatio-temporal model of combination chemotherapy with senolytic drug in treatment of lung cancer. Model’s simulations of tumor volume growth are shown to agree with mouse experiments in the case where cyclophosphamide is combined with the senolytic drug fisetin. It is then shown how the model can be used to assess the benefits of treatments with different combinations and different schedules of the two drugs in order to achieve optimal tumor volume reduction.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"379 ","pages":"Article 109342"},"PeriodicalIF":1.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142718115","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 : 2024-11-20DOI: 10.1016/j.mbs.2024.109339
Mara Perez , Marcelo Actis , Ignacio Sanchez , Esteban A. Hernandez-Vargas , Alejandro H. González
A fraction of individuals infected with SARS-CoV-2 experienced rebounds when treated with effective antivirals such as Nirmatrelvir/Ritonavir (Paxlovid). Although this phenomenon has been studied from biological and statistical perspectives, the underlying dynamical mechanism is not yet fully understood. In this work, we characterize the dynamic behavior of a target-cell model to explain post-treatment rebounds from the perspective of set-theoretic stability analysis. Without relying on the effects of the adaptive immune system or the resistance through viral mutations, we develop mathematical conditions for antiviral treatments to avoid viral rebound. Simulation results illustrate the critical role of dosage (i.e., the doses and timing of administration) in taking advantage of highly effective drugs and tailoring therapies.
{"title":"A theory for viral rebound after antiviral treatment: A study case for SARS-CoV-2","authors":"Mara Perez , Marcelo Actis , Ignacio Sanchez , Esteban A. Hernandez-Vargas , Alejandro H. González","doi":"10.1016/j.mbs.2024.109339","DOIUrl":"10.1016/j.mbs.2024.109339","url":null,"abstract":"<div><div>A fraction of individuals infected with SARS-CoV-2 experienced rebounds when treated with effective antivirals such as Nirmatrelvir/Ritonavir (Paxlovid). Although this phenomenon has been studied from biological and statistical perspectives, the underlying dynamical mechanism is not yet fully understood. In this work, we characterize the dynamic behavior of a target-cell model to explain post-treatment rebounds from the perspective of set-theoretic stability analysis. Without relying on the effects of the adaptive immune system or the resistance through viral mutations, we develop mathematical conditions for antiviral treatments to avoid viral rebound. Simulation results illustrate the critical role of dosage (i.e., the doses and timing of administration) in taking advantage of highly effective drugs and tailoring therapies.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"379 ","pages":"Article 109339"},"PeriodicalIF":1.9,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693996","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 : 2024-11-19DOI: 10.1016/j.mbs.2024.109340
Linhe Zhu , Yi Ding , Shuling Shen
The correlation between green behavior and energy efficiency is growing due to the heightened focus on energy efficiency among individuals. This paper introduces a three-layer network model to analyze the relationships among information diffusion, awareness and green behavior spreading. We have analyzed the probability tree of state transfer across 12 states by using Microscopic Markov Chain Approach (MMCA) and derived the state transfer equations for each state to compute the state transition threshold. In addition, we use the reaction–diffusion system to model the interaction between space and time changes for each state in the green behavior propagation layer. The equilibrium point of the system is defined, and the criteria for Turing bifurcation are identified. The optimal control approach achieves parameter identification, and this study validates the theory through several numerical simulations. Meanwhile, the effectiveness of parameter identification based on the convolutional neural network (CNN) and optimal control is compared. The data on China’s electrical energy generation is predicted and compared by using three neural networks and an autoregressive integrated moving average (ARIMA) model. Further, considering clean energy generation as a green behavior, we fit the data on the percentage of clean energy generation by applying a Microscopic Markov Chain model and a reaction–diffusion system.
{"title":"Green behavior propagation analysis based on statistical theory and intelligent algorithm in data-driven environment","authors":"Linhe Zhu , Yi Ding , Shuling Shen","doi":"10.1016/j.mbs.2024.109340","DOIUrl":"10.1016/j.mbs.2024.109340","url":null,"abstract":"<div><div>The correlation between green behavior and energy efficiency is growing due to the heightened focus on energy efficiency among individuals. This paper introduces a three-layer network model to analyze the relationships among information diffusion, awareness and green behavior spreading. We have analyzed the probability tree of state transfer across 12 states by using Microscopic Markov Chain Approach (MMCA) and derived the state transfer equations for each state to compute the state transition threshold. In addition, we use the reaction–diffusion system to model the interaction between space and time changes for each state in the green behavior propagation layer. The equilibrium point of the system is defined, and the criteria for Turing bifurcation are identified. The optimal control approach achieves parameter identification, and this study validates the theory through several numerical simulations. Meanwhile, the effectiveness of parameter identification based on the convolutional neural network (CNN) and optimal control is compared. The data on China’s electrical energy generation is predicted and compared by using three neural networks and an autoregressive integrated moving average (ARIMA) model. Further, considering clean energy generation as a green behavior, we fit the data on the percentage of clean energy generation by applying a Microscopic Markov Chain model and a reaction–diffusion system.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"379 ","pages":"Article 109340"},"PeriodicalIF":1.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690159","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 : 2024-11-06DOI: 10.1016/j.mbs.2024.109336
Maisha Islam Sejunti , Dane Taylor , Naoki Masuda
Many social and biological networks periodically change over time with daily, weekly, and other cycles. Thus motivated, we formulate and analyze susceptible-infectious-susceptible (SIS) epidemic models over temporal networks with periodic schedules. More specifically, we assume that the temporal network consists of a cycle of alternately used static networks, each with a given duration. We observe a phenomenon in which two static networks are individually above the epidemic threshold but the alternating network composed of them renders the dynamics below the epidemic threshold, which we refer to as a Parrondo paradox for epidemics. We find that network structure plays an important role in shaping this phenomenon, and we study its dependence on the connectivity between and number of subpopulations in the network. We associate such paradoxical behavior with anti-phase oscillatory dynamics of the number of infectious individuals in different subpopulations.
{"title":"A Parrondo paradox in susceptible-infectious-susceptible dynamics over periodic temporal networks","authors":"Maisha Islam Sejunti , Dane Taylor , Naoki Masuda","doi":"10.1016/j.mbs.2024.109336","DOIUrl":"10.1016/j.mbs.2024.109336","url":null,"abstract":"<div><div>Many social and biological networks periodically change over time with daily, weekly, and other cycles. Thus motivated, we formulate and analyze susceptible-infectious-susceptible (SIS) epidemic models over temporal networks with periodic schedules. More specifically, we assume that the temporal network consists of a cycle of alternately used static networks, each with a given duration. We observe a phenomenon in which two static networks are individually above the epidemic threshold but the alternating network composed of them renders the dynamics below the epidemic threshold, which we refer to as a Parrondo paradox for epidemics. We find that network structure plays an important role in shaping this phenomenon, and we study its dependence on the connectivity between and number of subpopulations in the network. We associate such paradoxical behavior with anti-phase oscillatory dynamics of the number of infectious individuals in different subpopulations.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"378 ","pages":"Article 109336"},"PeriodicalIF":1.9,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142635463","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}
Pub Date : 2024-11-05DOI: 10.1016/j.mbs.2024.109337
Alessandro De Gaetano , Alain Barrat , Daniela Paolotti
Individuals’ perceptions of disease influence their adherence to preventive measures, shaping the dynamics of disease spread. Despite extensive research on the interaction between disease spread, human behaviors, and interventions, few models have incorporated real-world behavioral data on disease perception, limiting their applicability. In this study, we propose an approach to integrate survey data on contact patterns and disease perception into a data-driven compartmental model, by hypothesizing that perceived severity is a determinant of behavioral change. We explore scenarios involving a competition between a COVID-19 wave and a vaccination campaign, where individuals’ behaviors vary based on their perceived severity of the disease. Results indicate that behavioral heterogeneities influenced by perceived severity affect epidemic dynamics, in a way depending on the interplay between two contrasting effects. On the one hand, longer adherence to protective measures by groups with high perceived severity provides greater protection to vulnerable individuals, while premature relaxation of behaviors by low perceived severity groups facilitates virus spread. Differences in behavior across different population groups may impact strongly the epidemiological curves, with a transition from a scenario with two successive epidemic peaks to one with only one (higher) peak and overall more numerous severe outcomes and deaths. The specific modeling choices for how perceived severity modulates behavior parameters do not strongly impact the model’s outcomes. Moreover, the study of several simplified models indicate that the observed phenomenology depends on the combination of data describing age-stratified contact patterns and of the feedback loop between disease perception and behavior, while it is robust with respect to the lack of precise information on the distribution of perceived severity in the population. Sensitivity analyses confirm the robustness of our findings, emphasizing the consistent impact of behavioral heterogeneities across various scenarios. Our study underscores the importance of integrating risk perception into infectious disease transmission models and gives hints on the type of data that further extensive data collection should target to enhance model accuracy and relevance.
{"title":"Modeling the interplay between disease spread, behaviors, and disease perception with a data-driven approach","authors":"Alessandro De Gaetano , Alain Barrat , Daniela Paolotti","doi":"10.1016/j.mbs.2024.109337","DOIUrl":"10.1016/j.mbs.2024.109337","url":null,"abstract":"<div><div>Individuals’ perceptions of disease influence their adherence to preventive measures, shaping the dynamics of disease spread. Despite extensive research on the interaction between disease spread, human behaviors, and interventions, few models have incorporated real-world behavioral data on disease perception, limiting their applicability. In this study, we propose an approach to integrate survey data on contact patterns and disease perception into a data-driven compartmental model, by hypothesizing that perceived severity is a determinant of behavioral change. We explore scenarios involving a competition between a COVID-19 wave and a vaccination campaign, where individuals’ behaviors vary based on their perceived severity of the disease. Results indicate that behavioral heterogeneities influenced by perceived severity affect epidemic dynamics, in a way depending on the interplay between two contrasting effects. On the one hand, longer adherence to protective measures by groups with high perceived severity provides greater protection to vulnerable individuals, while premature relaxation of behaviors by low perceived severity groups facilitates virus spread. Differences in behavior across different population groups may impact strongly the epidemiological curves, with a transition from a scenario with two successive epidemic peaks to one with only one (higher) peak and overall more numerous severe outcomes and deaths. The specific modeling choices for how perceived severity modulates behavior parameters do not strongly impact the model’s outcomes. Moreover, the study of several simplified models indicate that the observed phenomenology depends on the combination of data describing age-stratified contact patterns and of the feedback loop between disease perception and behavior, while it is robust with respect to the lack of precise information on the distribution of perceived severity in the population. Sensitivity analyses confirm the robustness of our findings, emphasizing the consistent impact of behavioral heterogeneities across various scenarios. Our study underscores the importance of integrating risk perception into infectious disease transmission models and gives hints on the type of data that further extensive data collection should target to enhance model accuracy and relevance.</div></div>","PeriodicalId":51119,"journal":{"name":"Mathematical Biosciences","volume":"378 ","pages":"Article 109337"},"PeriodicalIF":1.9,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607445","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}
Pub Date : 2024-11-02DOI: 10.1016/j.mbs.2024.109335
Ananth Vedururu Srinivas, Carmen C. Canavier
Phase Response Curves (PRCs) have been useful in determining and analyzing various phase-locking modes in networks of oscillators under pulse-coupling assumptions, as reviewed in Mathematical Biosciences, 226:77–96, 2010. Here, we update that review to include progress since 2010 on pulse coupled oscillators with conduction delays. We then present original results that extend the derivation of the criteria for stability of global synchrony in networks of pulse-coupled oscillators to include conduction delays. We also incorporate conduction delays to extend previous studies that showed how an alternating firing pattern between two synchronized clusters could enforce within-cluster synchrony, even for clusters unable to synchronize themselves in isolation. To obtain these results, we used self-connected neurons to represent clusters. These results greatly extend the applicability of the stability analyses to networks of pulse-coupled oscillators since conduction delays are ubiquitous and strongly impact the stability of synchrony. Although these analyses only strictly apply to identical oscillators with identical connections to other oscillators, the principles are general and suggest how to promote or impede synchrony in physiological networks of neurons, for example. Heterogeneity can be interpreted as a form of frozen noise, and approximate synchrony can be sustained despite heterogeneity. The pulse-coupled oscillator model can not only be used to describe biological neuronal networks but also cardiac pacemakers, lasers, fireflies, artificial neural networks, social self-organization, and wireless sensor networks.
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