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Ciganda D, Lorenti A, Dommermuth L. Microfoundations of the weakening educational gradient in fertility. POPULATION STUDIES 2024:1-20. [PMID: 38700204 DOI: 10.1080/00324728.2024.2319031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 10/09/2023] [Indexed: 05/05/2024]
Abstract
The disappearance of the social gradient in fertility represents a paradigm shift that has called into question the validity of theories that predicted a decline in fertility with increased access to education and resources. Emerging theories have tried to explain this trend by highlighting a potential change in the fertility preferences of more educated couples. In this paper we add additional elements to this explanation. Using a computational modelling approach, we show that it is still possible to simulate the weakening social gradient in fertility, in the context of steady declines in family size preferences. Our results show that one of the key drivers of the change in the education-fertility relationship can be found in the transition to an increasingly regulated fertility regime. As the share of unplanned births decreases over time, the negative association between education and fertility weakens and the mechanisms that positively connect educational attainment with desired fertility become dominant.
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Ciganda D, Todd N. Demographic models of the reproductive process: Past, interlude, and future. POPULATION STUDIES 2022; 76:495-513. [PMID: 34486942 DOI: 10.1080/00324728.2021.1959943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
After 30 years of active development, mechanistic models of the reproductive process nearly stopped attracting scholarly interest in the early 1980s. In the following decades, fertility research continued to thrive, relying on solid descriptive work and detailed analysis of micro-level data. The absence of systematic modelling efforts, however, has also made the field more fragmented, with empirical research, theory building, and forecasting advancing along largely disconnected channels. In this paper we outline some of the drivers of this process, from the popularization of user-friendly statistical software to the limitations of early family building models. We then describe a series of developments in computational modelling and statistical computing that can contribute to the emergence of a new generation of mechanistic models. Finally, we introduce a concrete example of this new kind of model, and show how they can be used to formulate and test theories coherently and make informed projections.
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Pokharel G, Deardon R. Emulation‐based inference for spatial infectious disease transmission models incorporating event time uncertainty. Scand Stat Theory Appl 2021. [DOI: 10.1111/sjos.12523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Gyanendra Pokharel
- Mathematics and Statistics University of Winnipeg Winnipeg Manitoba Canada
| | - Rob Deardon
- Production Animal Health & Mathematics and Statistics University of Calgary Calgary Alberta Canada
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Verma M, Bassaganya-Riera J, Leber A, Tubau-Juni N, Hoops S, Abedi V, Chen X, Hontecillas R. High-resolution computational modeling of immune responses in the gut. Gigascience 2020; 8:5513894. [PMID: 31185494 PMCID: PMC6559340 DOI: 10.1093/gigascience/giz062] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 01/19/2019] [Accepted: 05/05/2019] [Indexed: 02/07/2023] Open
Abstract
Background Helicobacter pylori causes gastric cancer in 1–2% of cases but is also beneficial for protection against allergies and gastroesophageal diseases. An estimated 85% of H. pylori–colonized individuals experience no detrimental effects. To study the mechanisms promoting host tolerance to the bacterium in the gastrointestinal mucosa and systemic regulatory effects, we investigated the dynamics of immunoregulatory mechanisms triggered by H. pylori using a high-performance computing–driven ENteric Immunity SImulator multiscale model. Immune responses were simulated by integrating an agent-based model, ordinary, and partial differential equations. Results The outputs were analyzed using 2 sequential stages: the first used a partial rank correlation coefficient regression–based and the second a metamodel-based global sensitivity analysis. The influential parameters screened from the first stage were selected to be varied for the second stage. The outputs from both stages were combined as a training dataset to build a spatiotemporal metamodel. The Sobol indices measured time-varying impact of input parameters during initiation, peak, and chronic phases of infection. The study identified epithelial cell proliferation and epithelial cell death as key parameters that control infection outcomes. In silico validation showed that colonization with H. pylori decreased with a decrease in epithelial cell proliferation, which was linked to regulatory macrophages and tolerogenic dendritic cells. Conclusions The hybrid model of H. pylori infection identified epithelial cell proliferation as a key factor for successful colonization of the gastric niche and highlighted the role of tolerogenic dendritic cells and regulatory macrophages in modulating the host responses and shaping infection outcomes.
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Affiliation(s)
- Meghna Verma
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA.,Graduate Program in Translational Biology, Medicine and Health, Virginia Tech, Blacksburg, 1 Riverside Circle, Roanoke, VA 24016, USA
| | - Josep Bassaganya-Riera
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Andrew Leber
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Nuria Tubau-Juni
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Stefan Hoops
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Vida Abedi
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
| | - Xi Chen
- Grado Department of Industrial and Systems Engineering, Virginia Tech, 250 Perry St, Blacksburg, VA 24061, USA
| | - Raquel Hontecillas
- Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, 1015 Life Science Circle, Blacksburg, VA 24061, USA
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Affiliation(s)
- Li-Hsiang Lin
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
| | - V. Roshan Joseph
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
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Sung CL, Wang W, Plumlee M, Haaland B. Multiresolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments. J Am Stat Assoc 2019. [DOI: 10.1080/01621459.2019.1595630] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Chih-Li Sung
- Department of Statistics and Probability, Michigan State University, East Lansing, MI
| | - Wenjia Wang
- Statistical and Applied Mathematical Sciences Institute, Raleigh, NC
| | - Matthew Plumlee
- Department of Industrial Engineering and Management Sciences, Northwestern Universit, Evanston, IL
| | - Benjamin Haaland
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
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Pokharel G, Deardon R. Gaussian process emulators for spatial individual-level models of infectious disease. CAN J STAT 2016. [DOI: 10.1002/cjs.11304] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Gyanendra Pokharel
- Department of Mathematics and Statistics, Faculty of Science; University of Calgary; Alberta Canada
- Department of Mathematics and Statistics, College of Physical and Engineering Science; University of Guelph; Ontario Canada
| | - Rob Deardon
- Department of Mathematics and Statistics, Faculty of Science; University of Calgary; Alberta Canada
- Department of Production Animal Health, Faculty of Veterinary Medicine; University of Calgary; Alberta Canada
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