Trending repositories for topic epidemiology
Course materials of "Bayesian Modelling and Probabilistic Programming with Numpyro, and Deep Generative Surrogates for Epidemiology"
Course materials of "Bayesian Modelling and Probabilistic Programming with Numpyro, and Deep Generative Surrogates for Epidemiology"
Functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by using evidence-based methods, as described in https://doi....
Tools to enable flexible and efficient hierarchical nowcasting of epidemiological time-series using a semi-mechanistic Bayesian model with support for a range of reporting and generative processes.
Models of SEIRS epidemic dynamics with extensions, including network-structured populations, testing, contact tracing, and social distancing.
Course materials of "Bayesian Modelling and Probabilistic Programming with Numpyro, and Deep Generative Surrogates for Epidemiology"
Spatiotemporal datasets collected for network science, deep learning and general machine learning research.
Tutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3
Tools to enable flexible and efficient hierarchical nowcasting of epidemiological time-series using a semi-mechanistic Bayesian model with support for a range of reporting and generative processes.
Functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by using evidence-based methods, as described in https://doi....
Course materials of "Bayesian Modelling and Probabilistic Programming with Numpyro, and Deep Generative Surrogates for Epidemiology"
Spatiotemporal datasets collected for network science, deep learning and general machine learning research.
Tutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3
Models of SEIRS epidemic dynamics with extensions, including network-structured populations, testing, contact tracing, and social distancing.
Course materials of "Bayesian Modelling and Probabilistic Programming with Numpyro, and Deep Generative Surrogates for Epidemiology"
BioDynaMo is a high-performance and modular, agent-based simulation platform.
COVID-19 Agent-based Simulator (Covasim): a model for exploring coronavirus dynamics and interventions
Tutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3
Models of SEIRS epidemic dynamics with extensions, including network-structured populations, testing, contact tracing, and social distancing.
Functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by using evidence-based methods, as described in https://doi....
R package with classes and helper functions for working with epidemiological parameters and access to a library of epidemiological parameters for infectious diseases
Spatiotemporal datasets collected for network science, deep learning and general machine learning research.
🦠 Huge collection of useful projects and resources for COVID-19 (2019 novel Coronavirus)
Tools to enable flexible and efficient hierarchical nowcasting of epidemiological time-series using a semi-mechanistic Bayesian model with support for a range of reporting and generative processes.
June is a framework for agent based modelling in an epidemiological and geographical context.
Algorithms from circuit theory to predict connectivity in heterogeneous landscapes
R package with classes and helper functions for working with epidemiological parameters and access to a library of epidemiological parameters for infectious diseases
Spatiotemporal datasets collected for network science, deep learning and general machine learning research.
BioDynaMo is a high-performance and modular, agent-based simulation platform.
Tools to enable flexible and efficient hierarchical nowcasting of epidemiological time-series using a semi-mechanistic Bayesian model with support for a range of reporting and generative processes.
Functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by using evidence-based methods, as described in https://doi....
June is a framework for agent based modelling in an epidemiological and geographical context.
Tutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3
Machine-readable version of electronic health record phenotypes for Kuan V. and Denaxas S. et al.
COVID-19 Agent-based Simulator (Covasim): a model for exploring coronavirus dynamics and interventions
A web platform to detect and analyze variants of SARS-CoV-2
