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Measles Interest1 #715952
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+Citations (4) - CitationsAdd new citationList by: CiterankMapLink[2] Compositional methods for health modeling
Author: Sophie Libkind, Evan Patterson, James Fairbanks, Xiaoyan Li, Nathaniel Osgood Publication date: 2 August 2022 Cited by: David Price 10:14 AM 12 December 2024 GMT Citerank: (1) 70101222/08/02 Compositional methods for health modelingThe intended audience is mathematical epidemiologists and dynamic modelers for infectious diseases, health and health care seeking to learn about emerging methods and tools, based on applied category theory, for constructing large-scale models efficiently, reliably, and modularly. [1]63E883B6 URL:
| Excerpt / Summary Dynamic models in infectious disease and health and healthcare more generally integrate information and processes across many domains. Model modularity serves as a key enabler for smooth and flexible coordination between domains --- for instance, between pathogen transmission, human behavior, and genomic data. Such modularity allows domain experts to independently build and refine clearly delineated model components which are composed into a single complex model. This approach divides the complexity of model building into two factors: the development of submodels by domain experts and the integration of the submodels into the whole. Applied category theory, the mathematics of composition, tackles this second challenge. Composing models using the tools of applied category theory provides visual transparency for stakeholders, formal analyzability, alignment between types of model heterogeneity (e.g., modular stratification), and opportunities for optimization and parallelization. In this course, we will show how these mathematical tools and their implementation in the open-source AlgebraicJulia programming environment can be used to rapidly develop transparent dynamic models in health, with a particular emphasis on models of infectious diseases.
Beyond teaching these fundamental advances in modeling methodology and theory, the course will explore the strong application potential for these platforms, where students will gain experience in use of existing toolsets that make practical a variety of types of compositional modeling. These tools include interactive application programming interfaces as well as browser-based interactive, collaborative, graphical user interfaces for compositional modeling. Examples model will be drawn from conditions such as COVID-19, measles, pertussis, and other infectious, zoonotic and chronic diseases. |
Link[3] Effect of wetness on penetration dynamics of droplets impacted on facemasks
Author: Abhishek Saha, Sombuddha Bagchi, Saptarshi Basu, Swetaprovo Chaudhuri Publication date: 21 November 2021 Publication info: 74th Annual Meeting of the APS Division of Fluid Dynamics, Volume 66, Number 17 Cited by: David Price 10:16 AM 12 December 2024 GMT Citerank: (4) 701037MfPH – Publications144B5ACA0, 704045Covid-19859FDEF6, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6, 717032Swetaprovo ChaudhuriSwetaprovo is an Associate Professor in the Institute for Aerospace Studies in the Faculty of Applied Science and Engineering at the University of Toronto.10019D3ABAB URL:
| Excerpt / Summary [APS Division of Fluid Dynamics, 21 November 2021]
Properly designed facemasks can limit the spread of ballistic droplets and aerosol particles coming out of oral and nasal cavities during respiratory events, such as sneezing, coughing, singing, talking etc. Furthermore, it can also protect the user from inhaling small droplets, droplet nuclei, or aerosol particles. Thus, proper usage of facemasks can prevent the transmission of many diseases, including Covid19, influenza, measles, and the common cold. Although N95 masks are particularly designed to provide the best protection, various types of facemask became popular during the Covid19 pandemic due to a shortage of supply and high demand. In our recent study (Sharma et al. Sc. Adv. (2021) 7, eabf0452), we reported the fate of a respiratory droplet impacting on a dry facemask to show that larger droplets can penetrate the mask layers and undergo secondary atomizations leading to multiple smaller droplets. In this work, we focus on the effect of the wetness of the mask matrix on this atomization process. Indeed, due to the condensation process, longtime use renders the masks wet, and hence, its influence on the efficacy in blocking the droplet is worth investigating. We will present a regime map to show the penetration probability with impact velocity and wetness for two different types of masks. We will also present a scaling argument to explain the observed effects of wetness on penetration. |
Link[4] Pathogen.jl: Infectious Disease Transmission Network Modelling with Julia
Author: Justin Angevaare, Zeny Feng, Rob Deardon Publication date: 25 August 2021 Publication info: arXiv:2002.05850v3 Cited by: David Price 10:17 AM 12 December 2024 GMT Citerank: (1) 685206pathogen.jlSimulation, visualization, and inference tools for modelling the spread of infectious diseases with Julia122C78CB7 URL: DOI: https://doi.org/10.48550/arXiv.2002.05850
| Excerpt / Summary We introduce Pathogen.jl for simulation and inference of transmission network individual level models (TN-ILMs) of infectious disease spread in continuous time. TN-ILMs can be used to jointly infer transmission networks, event times, and model parameters within a Bayesian framework via Markov chain Monte Carlo (MCMC). We detail our specific strategies for conducting MCMC for TN-ILMs, and our implementation of these strategies in the Julia package, Pathogen.jl, which leverages key features of the Julia language. We provide an example using Pathogen.jl to simulate an epidemic following a susceptible-infectious-removed (SIR) TN-ILM, and then perform inference using observations that were generated from that epidemic. We also demonstrate the functionality of Pathogen.jl with an application of TN-ILMs to data from a measles outbreak that occurred in Hagelloch, Germany in 1861(Pfeilsticker 1863; Oesterle 1992). |
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