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Nathaniel Osgood Person1 #679855 Nathaniel D. Osgood is a Professor in the Department of Computer Science and Associate Faculty in the Department of Community Health & Epidemiology at the University of Saskatchewan. | 
Research InterestsCombining Data Science, Systems Science, Computational Science and Applied Math to improve decision making in health and healthcare - Tools of choice include supplementing system science dynamic models (particularly Agent-Based models, System Dynamics, and discrete event simulation) with particle filtering and particle MCMC with such system science models, systems for visualizing state space reconstruction and for Convergent Cross Mapping (CCM), existing and novel machine learning and dynamic modeling toolsets, GPU-based computational statistics algorithms (PMCMC and Particle Filtering) and CM, and a growing amount of FPGA-based performance acceleration of key algorithms. Of late, I have a particularly strong focus on leveraging understanding from Applied Category Theory -- an area I find to offer compelling alignment with the systems science perspective, synergies with systems science techniques, the requisite power to deliver compelling insight, and an outstanding foundation for more powerful tools for study of complex systems. I am particularly committed to using Applied Category Theory as the basis for use of higher-level functional programming and metalinguistic abstraction to enhance the clarity, transparency, concision, modularity, flexibility and power of languages for characterizing dynamic models.
- All such tools are applied within the health sphere, as this is our elected point of focus, inspiration and dedication. We further make extensive investment in machine learning to sharpen and broaden the health surveillance, including using our Ethica epidemiological smartphone and wearable-based data collection system, time series of search volumes, time series of machine-learning-classifed Twitter messages, web-scraped data, and other mechanisms. For example, as part of our strategy of using twitter for health surveillance in Canada, we have amassed more than 200M tweets. The types of data we obtain through surveillance data are classified using machine learning tools (including more traditional tools through to deep learning) to flag tweets of relevance and inform our particle-filtered and PMCMC-regrounded models.
Tags: Nate Osgood, Mental health, Nathan Osgood |
+Citations (4) - CitationsAdd new citationList by: CiterankMapLink[2] Compositional modeling with stock and flow diagrams
Author: John Baez, Xiaoyan Li, Sophie Libkind, Nathaniel Osgood, Evan Patterson Publication date: 31 July 2023 Publication info: arXiv:2205.08373 [cs.LO] Cited by: David Price 9:16 PM 14 November 2023 GMT Citerank: (1) 715301AlgebraicJuliaAlgebraicJulia aims to create novel approaches to scientific computing based on applied category theory, and constitutes an ecosystem of software based on generalized algebra and category theory in Julia.122C78CB7 URL: DOI: https://doi.org/10.48550/arXiv.2205.08373
| Excerpt / Summary [arXiv, 31 July 2023]
Stock and flow diagrams are widely used in epidemiology to model the dynamics of populations. Although tools already exist for building these diagrams and simulating the systems they describe, we have created a new package called StockFlow, part of the AlgebraicJulia ecosystem, which uses ideas from category theory to overcome notable limitations of existing software. Compositionality is provided by the theory of decorated cospans: stock and flow diagrams can be composed to form larger ones in an intuitive way formalized by the operad of undirected wiring diagrams. Our approach also cleanly separates the syntax of stock and flow diagrams from the semantics they can be assigned. We consider semantics in ordinary differential equations, although others are possible. As an example, we explain code in StockFlow that implements a simplified version of a COVID-19 model used in Canada. |
Link[3] Impacts of observation frequency on proximity contact data and modeled transmission dynamics
Author: Weicheng Qian, Kevin Gordon Stanley, Nathaniel David Osgood Publication date: 27 February 2023 Publication info: PLOS Computational Biology, 19(2), e1010917–e1010917. Cited by: David Price 0:14 AM 30 November 2023 GMT Citerank: (2) 701037MfPH – Publications144B5ACA0, 715294Contact tracing859FDEF6 URL: DOI: https://doi.org/10.1371/journal.pcbi.1010917
| Excerpt / Summary [PLOS Computational Biology, 27 February 2023]
Transmission of many communicable diseases depends on proximity contacts among humans. Modeling the dynamics of proximity contacts can help determine whether an outbreak is likely to trigger an epidemic. While the advent of commodity mobile devices has eased the collection of proximity contact data, battery capacity and associated costs impose tradeoffs between the observation frequency and scanning duration used for contact detection. The choice of observation frequency should depend on the characteristics of a particular pathogen and accompanying disease. We downsampled data from five contact network studies, each measuring participant-participant contact every 5 minutes for durations of four or more weeks. These studies included a total of 284 participants and exhibited different community structures. We found that for epidemiological models employing high-resolution proximity data, both the observation method and observation frequency configured to collect proximity data impact the simulation results. This impact is subject to the population’s characteristics as well as pathogen infectiousness. By comparing the performance of two observation methods, we found that in most cases, half-hourly Bluetooth discovery for one minute can collect proximity data that allows agent-based transmission models to produce a reasonable estimation of the attack rate, but more frequent Bluetooth discovery is preferred to model individual infection risks or for highly transmissible pathogens. Our findings inform the empirical basis for guidelines to inform data collection that is both efficient and effective. |
Link[4] Participatory Modeling with Discrete-Event Simulation: A Hybrid Approach to Inform Policy Development to Reduce Emergency Department Wait Times
Author: Yuan Tian, Jenny Basran, James Stempien, Adrienne Danyliw, Graham Fast, Patrick Falastein, Nathaniel D. Osgood Publication date: 17 July 2023 Publication info: Systems 2023, 11(7), 362; Cited by: David Price 2:36 PM 2 December 2023 GMT Citerank: (2) 685420Hospital16289D5D4, 701037MfPH – Publications144B5ACA0 URL: DOI: https://doi.org/10.3390/systems11070362
| Excerpt / Summary [Systems, 17 July 2023]
We detail a case study using a participatory modeling approach in the development and use of discrete-event simulations to identify intervention strategies aimed at reducing emergency department (ED) wait times in a Canadian health policy setting. A four-stage participatory modeling approach specifically adapted to the local policy environment was developed to engage stakeholders throughout the modeling processes. The participatory approach enabled a provincial team to engage a broad range of stakeholders to examine and identify the causes and solutions to lengthy ED wait times in the studied hospitals from a whole-system perspective. Each stage of the approach was demonstrated through its application in the case study. A novel and key feature of the participatory modeling approach was the development and use of a multi-criteria framework to identify and prioritize interventions to reduce ED wait times. We conclude with a discussion on lessons learned, which provide insights into future development and applications of participatory modeling methods to facilitate policy development and build multi-stakeholder consensus. |
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