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  1. Article ; Online: Projecting social contact matrices to populations stratified by binary attributes with known homophily.

    Kadelka, Claus

    Mathematical biosciences and engineering : MBE

    2022  Volume 20, Issue 2, Page(s) 3282–3300

    Abstract: Contact networks are heterogeneous. People with similar characteristics are more likely to interact, a phenomenon called assortative mixing or homophily. Empirical age-stratified social contact matrices have been derived by extensive survey work. We lack ...

    Abstract Contact networks are heterogeneous. People with similar characteristics are more likely to interact, a phenomenon called assortative mixing or homophily. Empirical age-stratified social contact matrices have been derived by extensive survey work. We lack however similar empirical studies that provide social contact matrices for a population stratified by attributes beyond age, such as gender, sexual orientation, or ethnicity. Accounting for heterogeneities with respect to these attributes can have a profound effect on model dynamics. Here, we introduce a new method, which uses linear algebra and non-linear optimization, to expand a given contact matrix to populations stratified by binary attributes with a known level of homophily. Using a standard epidemiological model, we highlight the effect homophily can have on model dynamics, and conclude by briefly describing more complicated extensions. The available Python source code enables any modeler to account for the presence of homophily with respect to binary attributes in contact patterns, ultimately yielding more accurate predictive models.
    MeSH term(s) Humans ; Male ; Female ; Sexual Behavior ; Surveys and Questionnaires
    Language English
    Publishing date 2022-12-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2265126-3
    ISSN 1551-0018 ; 1551-0018
    ISSN (online) 1551-0018
    ISSN 1551-0018
    DOI 10.3934/mbe.2023154
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Projecting social contact matrices to populations stratified by binary attributes with known homophily

    Kadelka, Claus

    2022  

    Abstract: Contact networks are heterogeneous. People with similar characteristics are more likely to interact, a phenomenon called assortative mixing or homophily. While age-assortativity is well-established and social contact matrices for populations stratified ... ...

    Abstract Contact networks are heterogeneous. People with similar characteristics are more likely to interact, a phenomenon called assortative mixing or homophily. While age-assortativity is well-established and social contact matrices for populations stratified by age have been derived through extensive survey work, we lack empirical studies that describe contact patterns of a population stratified by other attributes such as gender, sexual orientation, ethnicity, etc. Accounting for heterogeneities with respect to these attributes can have a profound effect on the dynamics of epidemiological forecasting models. Here, we introduce a new methodology to expand a given e.g. age-based contact matrix to populations stratified by binary attributes with a known level of homophily. We describe a set of linear conditions any meaningful social contact matrix must satisfy and find the optimal matrix by solving a non-linear optimization problem. We show the effect homophily can have on disease dynamics and conclude by briefly describing more complicated extensions. The available Python source code enables any modeler to account for the presence of homophily with respect to binary attributes in contact patterns, ultimately yielding more accurate predictive models.

    Comment: 20 pages, 3 figures, 4 tables
    Keywords Physics - Physics and Society ; Mathematics - Dynamical Systems ; Quantitative Biology - Quantitative Methods ; 92D30
    Subject code 612
    Publishing date 2022-07-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article: Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models.

    Gonzalez-Parra, Gilberto / Mahmud, Md Shahriar / Kadelka, Claus

    medRxiv : the preprint server for health sciences

    2024  

    Abstract: As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease ... ...

    Abstract As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.
    Language English
    Publishing date 2024-03-07
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.03.04.24303726
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  4. Article ; Online: Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models

    Gonzalez-Parra, Gilberto / Mahmud, Md Shahriar / Kadelka, Claus

    medRxiv

    Abstract: As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease ... ...

    Abstract As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.
    Keywords covid19
    Language English
    Publishing date 2024-03-07
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2024.03.04.24303726
    Database COVID19

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  5. Article ; Online: A meta-analysis of Boolean network models reveals design principles of gene regulatory networks.

    Kadelka, Claus / Butrie, Taras-Michael / Hilton, Evan / Kinseth, Jack / Schmidt, Addison / Serdarevic, Haris

    Science advances

    2024  Volume 10, Issue 2, Page(s) eadj0822

    Abstract: Gene regulatory networks (GRNs) play a central role in cellular decision-making. Understanding their structure and how it impacts their dynamics constitutes thus a fundamental biological question. GRNs are frequently modeled as Boolean networks, which ... ...

    Abstract Gene regulatory networks (GRNs) play a central role in cellular decision-making. Understanding their structure and how it impacts their dynamics constitutes thus a fundamental biological question. GRNs are frequently modeled as Boolean networks, which are intuitive, simple to describe, and can yield qualitative results even when data are sparse. We assembled the largest repository of expert-curated Boolean GRN models. A meta-analysis of this diverse set of models reveals several design principles. GRNs exhibit more canalization, redundancy, and stable dynamics than expected. Moreover, they are enriched for certain recurring network motifs. This raises the important question why evolution favors these design mechanisms.
    MeSH term(s) Gene Regulatory Networks ; Models, Genetic ; Algorithms
    Language English
    Publishing date 2024-01-12
    Publishing country United States
    Document type Meta-Analysis ; Journal Article
    ZDB-ID 2810933-8
    ISSN 2375-2548 ; 2375-2548
    ISSN (online) 2375-2548
    ISSN 2375-2548
    DOI 10.1126/sciadv.adj0822
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Effect of homophily and correlation of beliefs on COVID-19 and general infectious disease outbreaks.

    Kadelka, Claus / McCombs, Audrey

    PloS one

    2021  Volume 16, Issue 12, Page(s) e0260973

    Abstract: Contact between people with similar opinions and characteristics occurs at a higher rate than among other people, a phenomenon known as homophily. The presence of clusters of unvaccinated people has been associated with increased incidence of infectious ... ...

    Abstract Contact between people with similar opinions and characteristics occurs at a higher rate than among other people, a phenomenon known as homophily. The presence of clusters of unvaccinated people has been associated with increased incidence of infectious disease outbreaks despite high population-wide vaccination rates. The epidemiological consequences of homophily regarding other beliefs as well as correlations among beliefs or circumstances are poorly understood, however. Here, we use a simple compartmental disease model as well as a more complex COVID-19 model to study how homophily and correlation of beliefs and circumstances in a social interaction network affect the probability of disease outbreak and COVID-19-related mortality. We find that the current social context, characterized by the presence of homophily and correlations between who vaccinates, who engages in risk reduction, and individual risk status, corresponds to a situation with substantially worse disease burden than in the absence of heterogeneities. In the presence of an effective vaccine, the effects of homophily and correlation of beliefs and circumstances become stronger. Further, the optimal vaccination strategy depends on the degree of homophily regarding vaccination status as well as the relative level of risk mitigation high- and low-risk individuals practice. The developed methods are broadly applicable to any investigation in which node attributes in a graph might reasonably be expected to cluster or exhibit correlations.
    MeSH term(s) Adult ; Age Factors ; Aged ; Attitude to Health ; COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19/psychology ; COVID-19 Vaccines/therapeutic use ; Disease Outbreaks ; Humans ; Middle Aged ; Social Identification ; Social Interaction ; Social Networking ; Vaccination Hesitancy/psychology ; Vaccination Hesitancy/statistics & numerical data
    Chemical Substances COVID-19 Vaccines
    Language English
    Publishing date 2021-12-02
    Publishing country United States
    Document type Journal Article
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0260973
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Modular Control of Biological Networks.

    Murrugarra, David / Veliz-Cuba, Alan / Dimitrova, Elena / Kadelka, Claus / Wheeler, Matthew / Laubenbacher, Reinhard

    ArXiv

    2024  

    Abstract: The concept of control is central to understanding and applications of biological network models. Some of their key structural features relate to control functions, through gene regulation, signaling, or metabolic mechanisms, and computational models ... ...

    Abstract The concept of control is central to understanding and applications of biological network models. Some of their key structural features relate to control functions, through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications of models often focus on model-based control, such as in biomedicine or metabolic engineering. This paper presents an approach to model-based control that exploits two common features of biological networks, namely their modular structure and canalizing features of their regulatory mechanisms. The paper focuses on intracellular regulatory networks, represented by Boolean network models. A main result of this paper is that control strategies can be identified by focusing on one module at a time. This paper also presents a criterion based on canalizing features of the regulatory rules to identify modules that do not contribute to network control and can be excluded. For even moderately sized networks, finding global control inputs is computationally very challenging. The modular approach presented here leads to a highly efficient approach to solving this problem. This approach is applied to a published Boolean network model of blood cancer large granular lymphocyte (T-LGL) leukemia to identify a minimal control set that achieves a desired control objective.
    Language English
    Publishing date 2024-01-23
    Publishing country United States
    Document type Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: A model-based evaluation of the efficacy of COVID-19 social distancing, testing and hospital triage policies.

    McCombs, Audrey / Kadelka, Claus

    PLoS computational biology

    2020  Volume 16, Issue 10, Page(s) e1008388

    Abstract: A stochastic compartmental network model of SARS-CoV-2 spread explores the simultaneous effects of policy choices in three domains: social distancing, hospital triaging, and testing. Considering policy domains together provides insight into how different ...

    Abstract A stochastic compartmental network model of SARS-CoV-2 spread explores the simultaneous effects of policy choices in three domains: social distancing, hospital triaging, and testing. Considering policy domains together provides insight into how different policy decisions interact. The model incorporates important characteristics of COVID-19, the disease caused by SARS-CoV-2, such as heterogeneous risk factors and asymptomatic transmission, and enables a reliable qualitative comparison of policy choices despite the current uncertainty in key virus and disease parameters. Results suggest possible refinements to current policies, including emphasizing the need to reduce random encounters more than personal contacts, and testing low-risk symptomatic individuals before high-risk symptomatic individuals. The strength of social distancing of symptomatic individuals affects the degree to which asymptomatic cases drive the epidemic as well as the level of population-wide contact reduction needed to keep hospitals below capacity. The relative importance of testing and triaging also depends on the overall level of social distancing.
    MeSH term(s) Betacoronavirus ; COVID-19 ; COVID-19 Testing ; Clinical Laboratory Techniques/methods ; Clinical Laboratory Techniques/standards ; Communicable Disease Control ; Coronavirus Infections/diagnosis ; Coronavirus Infections/epidemiology ; Coronavirus Infections/prevention & control ; Coronavirus Infections/transmission ; Emergency Service, Hospital ; Hospitals/standards ; Humans ; Models, Theoretical ; Pandemics/prevention & control ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/prevention & control ; Pneumonia, Viral/transmission ; Policy ; Risk Factors ; SARS-CoV-2 ; Social Isolation
    Keywords covid19
    Language English
    Publishing date 2020-10-15
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1008388
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  9. Article ; Online: Effect of homophily and correlation of beliefs on COVID-19 and general infectious disease outbreaks.

    Claus Kadelka / Audrey McCombs

    PLoS ONE, Vol 16, Iss 12, p e

    2021  Volume 0260973

    Abstract: Contact between people with similar opinions and characteristics occurs at a higher rate than among other people, a phenomenon known as homophily. The presence of clusters of unvaccinated people has been associated with increased incidence of infectious ... ...

    Abstract Contact between people with similar opinions and characteristics occurs at a higher rate than among other people, a phenomenon known as homophily. The presence of clusters of unvaccinated people has been associated with increased incidence of infectious disease outbreaks despite high population-wide vaccination rates. The epidemiological consequences of homophily regarding other beliefs as well as correlations among beliefs or circumstances are poorly understood, however. Here, we use a simple compartmental disease model as well as a more complex COVID-19 model to study how homophily and correlation of beliefs and circumstances in a social interaction network affect the probability of disease outbreak and COVID-19-related mortality. We find that the current social context, characterized by the presence of homophily and correlations between who vaccinates, who engages in risk reduction, and individual risk status, corresponds to a situation with substantially worse disease burden than in the absence of heterogeneities. In the presence of an effective vaccine, the effects of homophily and correlation of beliefs and circumstances become stronger. Further, the optimal vaccination strategy depends on the degree of homophily regarding vaccination status as well as the relative level of risk mitigation high- and low-risk individuals practice. The developed methods are broadly applicable to any investigation in which node attributes in a graph might reasonably be expected to cluster or exhibit correlations.
    Keywords Medicine ; R ; Science ; Q
    Subject code 610
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Modularity of biological systems: a link between structure and function.

    Kadelka, Claus / Wheeler, Matthew / Veliz-Cuba, Alan / Murrugarra, David / Laubenbacher, Reinhard

    Journal of the Royal Society, Interface

    2023  Volume 20, Issue 207, Page(s) 20230505

    Abstract: This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network ...

    Abstract This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network models, a commonly used tool. Most of the research on gene regulatory network modularity has focused on network structure, typically represented through either directed or undirected graphs. But since gene regulation is a highly dynamic process as it determines the function of cells over time, it is natural to consider functional modularity as well. One of the main results is that the structural decomposition of a network into modules induces an analogous decomposition of the dynamic structure, exhibiting a strong relationship between network structure and function. An extensive simulation study provides evidence for the hypothesis that modularity might have evolved to increase phenotypic complexity while maintaining maximal dynamic robustness to external perturbations.
    MeSH term(s) Computer Simulation ; Systems Biology ; Gene Regulatory Networks ; Models, Biological
    Language English
    Publishing date 2023-10-25
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2156283-0
    ISSN 1742-5662 ; 1742-5689
    ISSN (online) 1742-5662
    ISSN 1742-5689
    DOI 10.1098/rsif.2023.0505
    Database MEDical Literature Analysis and Retrieval System OnLINE

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