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Fitting dynamical models to data Function1 #686169

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EIDM »EIDM EIDM ☜The Emerging Infectious Diseases Modelling Initiative (EIDM) – by the Public Health Agency of Canada and NSERC – aims to establish multi-disciplinary network(s) of specialists across the country in modelling infectious diseases to be applied to public needs associated with emerging infectious diseases and pandemics such as COVID-19. [1]☜F1CEB7

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Software »SoftwareSoftware☜Learn more about the software being used across the EIDM network.☜C78CB7

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Software functionality »Software functionalitySoftware functionality☜☜8CC79C

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Fitting dynamical models to dataFitting dynamical models to data☜☜8CC79C

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Dynamical systems »Dynamical systemsDynamical systems☜☜FFFACD

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COVID Prevalence Estimate »COVID Prevalence EstimateCOVID Prevalence Estimate☜An implementation of Bayesian inference and prediction of COVID-19 point-prevalence.☜FFFACD

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covidseir »covidseircovidseir☜covidseir fits a Bayesian SEIR (Susceptible, Exposed, Infectious, Recovered) model to daily COVID-19 case data. The package focuses on estimating the fraction of the usual contact rate for individuals participating in physical distancing (social distancing). The model is coded in Stan. The model can accommodate multiple types of case data at once (e.g., reported cases, hospitalizations, ICU admissions) and accounts for delays between symptom onset and case appearance. [1]☜FFFACD

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diseasemapping »diseasemappingdiseasemapping☜[Archived on 21 March 2022]☜FFFACD

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EpiILM »EpiILMEpiILM☜The R package EpiILM provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR).☜FFFACD

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EpiILMCT »EpiILMCTEpiILMCT☜The R package EpiILMCT provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks. [2]☜FFFACD

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geostatsp »geostatspgeostatsp☜[Archived on 2021-12-09]☜FFFACD

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localEM »localEMlocalEM☜localEM is an R package that implements the kernel smoothing local-EM algorithm for estimating spatial risk on areal disease data. [1]☜FFFACD

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Mathematical epidemiology in a data-rich world »Mathematical epidemiology in a data-rich worldMathematical epidemiology in a data-rich world☜An exciting trend for modellers, which started 5–10 years ago and is becoming increasingly common, are open data initiatives. Such initiatives see governments (local or higher) create portals where data is centralised and made accessible, usually with very few constraints.☜FFFACD

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McMasterPandemic »McMasterPandemicMcMasterPandemic☜Compartmental epidemic models for forecasting and analysis of infectious disease pandemics: contributions from Ben Bolker, Jonathan Dushoff, David Earn, Weiguang Guan, Morgan Kain, Michael Li, Irena Papst, Steve Walker (in alphabetical order).☜FFFACD

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MultiBD »MultiBDMultiBD☜MultiBD is an R package for direct likelihood-based inference of multivariate birth-death processes. [1]☜FFFACD

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pathogen.jl »pathogen.jlpathogen.jl☜Simulation, visualization, and inference tools for modelling the spread of infectious diseases with Julia☜FFFACD

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python Population Modeller »python Population Modellerpython Population Modeller☜The pyPM.ca software was developed to study and characterize the CoViD-19 epidemic.☜FFFACD

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PyCoMod »PyCoModPyCoMod☜PyCoMod is a Python package for building and running compartment models derived from systems of differential equations such as the Susceptible-Infectious-Recovered (SIR)model of infectious diseases.☜FFFACD

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Eingabe von: Steven Walker
NodeID: #686169
Node type: Function
Eingabedatum (GMT): 10/7/2021 1:42:00 PM
Zuletzt geändert am (GMT): 10/7/2021 1:42:00 PM
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