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C++ Option1 #685213

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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 languages »Software languagesSoftware languages☜☜76A4CF

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C++C++☜☜76A4CF

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Dempster-Shafer Bayesian Network inference package »Dempster-Shafer Bayesian Network inference packageDempster-Shafer Bayesian Network inference package☜DS-BN is a C++ executable that accepts input data related to probabilistic belief networks and Dempster-Shafer belief networks through the use of files. According to provided instructions from one of the files, performs computations and writes inferred probability distributions or Dempster-Shafer models into an output file. [2]☜FFFACD

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epigrowthfit »epigrowthfitepigrowthfit☜epigrowthfit is an R package for fitting nonlinear mixed effects models of epidemic growth to collections of one or more disease incidence time series. It can be applied to birth processes other than epidemics, as the statistical machinery is agnostic to the precise interpretation of supplied count data. epigrowthfit is built on Template Model Builder. [1]☜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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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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Entrée par: David Price
NodeID: #685213
Node type: Option
Date d'entrée (GMT): 9/26/2021 9:26:00 PM
Date de la dernière modification (Heure GMT): 9/26/2021 9:26:00 PM
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