comments
Respond
Comment on the article
Add a citation
Reply with an article
Start a new topic
Edit
Edit article
Delete article
Share
Invite
Link
Embed
Social media
Avatar
View
Graph
Explorer
Focus
Down
Load 1 level
Load 2 levels
Load 3 levels
Load 4 levels
Load all levels
All
Dagre
Focus
Down
Load 1 level
Load 2 levels
Load 3 levels
Load 4 level
Load all levels
All
Tree
SpaceTree
Focus
Expanding
Load 1 level
Load 2 levels
Load 3 levels
Down
All
Down
Radial
Focus
Expanding
Load 1 level
Load 2 levels
Load 3 levels
Down
All
Down
Box
Focus
Expanding
Down
Up
All
Down
Article ✓
Outline
Document
Down
All
Page
Canvas
Time
Timeline
Calendar
Updates
Subscribe to updates
Get updates
Past 24 hours
Past week
Past month
Past year
Pause updates
Contact us
python Population Modeller
The pyPM.ca software was developed to study and characterize the CoViD-19 epidemic.
It consists of:
pypmca: a general purpose population modelling engine.
ipypm: a graphical user interface for pypmca. Based on ipywidgets, it runs in a jupyter notebook.
models: a repository of CoViD-19 models tuned for different regions.
data: a repository of regional CoViD-19 data on cases, hospitalizations, and deaths.
RELATED ARTICLES
Explain
⌅
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
⌃
Software
Software☜Learn more about the software being used across the EIDM network.☜C78CB7
⌃
Software programs
Software programs☜☜C78CB7
■
python Population Modeller
python Population Modeller☜The pyPM.ca software was developed to study and characterize the CoViD-19 epidemic.☜C78CB7
⇤
Python
Python☜☜FFFACD
⇤
Data analysis
Data analysis☜☜FFFACD
⇤
Fitting dynamical models to data
Fitting dynamical models to data☜☜FFFACD
⇤
Simulation
Simulation☜☜FFFACD
⇤
Compartmental
Compartmental☜☜FFFACD
⇤
Deterministic
Deterministic☜☜FFFACD
⇤
Stochastic
Stochastic☜☜FFFACD
⇤
British Columbia COVID-19 Group
British Columbia COVID-19 Group☜The BC COVID-19 Modelling Group works on rapid response modelling of the COVID-19 pandemic, with a special focus on British Columbia and Canada.☜FFFACD
⇥
Dean Karlen
Dean Karlen☜R.M. Pearce Professor of Physics, University of Victoria and TRIUMF☜FFFACD
⇥
Covid-19
Covid-19☜☜FFFACD
□
Age-Patch Mobility Mixing
Age-Patch Mobility Mixing☜Deriving contact patterns for age & patch mixing, based on recurrent mobility data. [1]☜C78CB7
□
AlgebraicJulia
AlgebraicJulia☜AlgebraicJulia 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.☜C78CB7
□
Bayesian Evolutionary Analysis by Sampling Trees (BEAST)
Bayesian Evolutionary Analysis by Sampling Trees (BEAST)☜BEAST 2 is an open source cross-platform software package for analysing genetic sequences in a Bayesian phylogenetic framework. BEAST 2 provides a growing collection of new models tailored specifically to particular data sets and/or research questions.☜C78CB7
□
CCODWG
CCODWG☜☜C78CB7
□
Covasim
Covasim☜COVID-19 Agent-based Simulator (Covasim): a model for exploring coronavirus dynamics and interventions.☜C78CB7
□
COVID Hospital Surge
COVID Hospital Surge☜COVID-19 Healthcare Surge Model for Greater Toronto Area Hospitals.☜C78CB7
□
COVID JupyterHub
COVID JupyterHub☜☜C78CB7
□
COVID Prevalence Estimate
COVID Prevalence Estimate☜An implementation of Bayesian inference and prediction of COVID-19 point-prevalence.☜C78CB7
□
COVID-19 Re - Generation Time
COVID-19 Re - Generation Time☜Estimating the effective reproduction number Re(t) for COVID19 in GTA, Canada. [1]☜C78CB7
□
covidseir
covidseir☜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]☜C78CB7
□
CovidSim
CovidSim☜CovidSim models the transmission dynamics and severity of COVID-19 infections throughout a spatially and socially structured population over time.☜C78CB7
□
covid-topic-modeling
covid-topic-modeling☜Social media topic modeling software.☜C78CB7
□
Dempster-Shafer Bayesian Network inference package
Dempster-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]☜C78CB7
□
diseasemapping
diseasemapping☜[Archived on 21 March 2022]☜C78CB7
□
Efficacy of quarantine
Efficacy of quarantine☜R files linked to the paper Quarantine and the risk of COVID-19 importation [2]☜C78CB7
□
epi base package for SyncroSim
epi base package for SyncroSim☜A scenario-based modelling framework for generating locally relevant forecasts of COVID-19☜C78CB7
□
EpiEstim
EpiEstim☜A tool to estimate time varying instantaneous reproduction number during epidemics.☜C78CB7
□
epigrowthfit
epigrowthfit☜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]☜C78CB7
□
EpiILM
EpiILM☜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).☜C78CB7
□
EpiILMCT
EpiILMCT☜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]☜C78CB7
□
Estimating the infection fatality rate of Covid-19
Estimating the infection fatality rate of Covid-19☜Estimating the infection fatality rate of Covid-19 using demographics data and deaths records from Italys hardest hit area. [1]☜C78CB7
□
geostatsp
geostatsp☜[Archived on 2021-12-09]☜C78CB7
□
Hexagonalfan
Hexagonalfan☜An R script that generates a range of hexagonal fan designs. Hexagonal fans are triangular grids in which each successive row of triangles is larger than the previous, by some constant multiplier. Such designs can include a range of both (plant) densities and frequencies in a single plot, providing large economies in space and material for studying local interactions such as competition. However, in practice the fan design can be difficult to implement, hence why we have provided this script.☜C78CB7
□
localEM
localEM☜localEM is an R package that implements the kernel smoothing local-EM algorithm for estimating spatial risk on areal disease data. [1]☜C78CB7
□
Localized Epidemiological Agent-Based Simulation (ABS)
Localized Epidemiological Agent-Based Simulation (ABS)☜ This ABS provides information about risk and the effects of both pharmacological and non-pharmacological interventions, as well as detailed control over the rapidly evolving epidemiological characteristics of COVID-19. [1]☜C78CB7
□
macpan2
macpan2☜McMasterPandemic was developed to provide forecasts and insights to Canadian public health agencies throughout the COVID-19 pandemic. The goal of this macpan2 project is to re-imagine McMasterPandemic, building it from the ground up with architectural and technological decisions that address the many lessons that we learned from COVID-19 about software.☜C78CB7
□
Mathematical epidemiology in a data-rich world
Mathematical 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.☜C78CB7
□
McMasterPandemic
McMasterPandemic☜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).☜C78CB7
□
MultiBD
MultiBD☜MultiBD is an R package for direct likelihood-based inference of multivariate birth-death processes. [1]☜C78CB7
□
Ontario pandemic mobility
Ontario pandemic mobility☜This interactive data tool uses publicly available Google Mobility data to generate graphs displaying overall mobility changes in Ontario. Google typically updates their datasets every 3-5 days, and the new data will contain data points with a 2-3 day lag. ☜C78CB7
□
Open Data Model
Open Data Model☜Metadata and code to support covid-19 environmental surveillance for public health. Wastewater-based surveillance for Covid-19, with support for other health risks and environmental settings. [1]☜C78CB7
□
pathogen.jl
pathogen.jl☜Simulation, visualization, and inference tools for modelling the spread of infectious diseases with Julia☜C78CB7
□
Pathosim
Pathosim☜A Multi-Pathogen Agent-Based-Model capable of accurately simulating the spread of multiple pathogens in a synthetic population as well as pathogen-pathogen interactions within a host.☜C78CB7
□
plASgraph2
plASgraph2☜Classifying Plasmid Contigs From Bacterial Assembly Graphs Using Graph Neural Networks.☜C78CB7
□
pmcmc_dynamic_modeling
pmcmc_dynamic_modeling☜Dynamic Modeling using Particle Markov Chain Monte Carlo (PMCMC) Techniques.☜C78CB7
□
Prediction Codes
Prediction Codes☜Dataset of COVID-19 outbreak and potential predictive features in the USA.☜C78CB7
□
PyCoMod
PyCoMod☜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.☜C78CB7
□
SamPy
SamPy☜A New Python Library for Stochastic Spatial Agent-Based Modeling in Epidemiology of Infectious Diseases. [1]☜C78CB7
□
Stochastic simulations of classroom-level COVID-19 outbreaks
Stochastic simulations of classroom-level COVID-19 outbreaks☜This repository contains code associated with a preprint exploring COVID-19 outbreaks in classrooms, and how these might be managed with several different protocols: COVID-19s unfortunate events in schools: mitigating classroom clusters in the context of variable transmission. [1]☜C78CB7
□
SyncroSim
SyncroSim☜Delivering geospatial forecasting models direct to decision makers. SyncroSim streamlines the process of delivering your map-based forecasting models to non-technical users☜C78CB7
□
Graph of this discussion
Graph of this discussion☜Click this to see the whole debate, excluding comments, in graphical form☜dcdcdc
Enter the title of your article
Enter a short (max 500 characters) summation of your article
Click the button to enter task scheduling information
Open
Enter the main body of your article
Prefer more work space? Try the
big editor
Enter task details
Message text
Select assignee(s)
Due date (click calendar)
RadDatePicker
RadDatePicker
Open the calendar popup.
Calendar
Title and navigation
Title and navigation
<<
<
November 2024
>
<<
November 2024
S
M
T
W
T
F
S
44
27
28
29
30
31
1
2
45
3
4
5
6
7
8
9
46
10
11
12
13
14
15
16
47
17
18
19
20
21
22
23
48
24
25
26
27
28
29
30
49
1
2
3
4
5
6
7
Reminder
No reminder
1 day before due
2 days before due
3 days before due
1 week before due
Ready to post
Copy to text
Enter
Cancel
Task assignment(s) have been emailed and cannot now be altered
Lock
Cancel
Save
Comment graphing options
Choose comments:
Comment only
Whole thread
All comments
Choose location:
To a new map
To this map
New map options
Select map ontology
Options
Standard (default) ontology
College debate ontology
Hypothesis ontology
Influence diagram ontology
Story ontology
Graph to private map
Cancel
Proceed
+Comments (
0
)
- Comments
Add a comment
Newest first
Oldest first
Show threads
+Citations (
3
)
- Citations
Add new citation
List by:
Citerank
Map
+About
- About
Entered by:-
David Price
NodeID:
#685208
Node type:
Resource
Entry date (GMT):
9/26/2021 9:22:00 PM
Last edit date (GMT):
9/14/2022 2:31:00 PM
Show other editors
Incoming cross-relations:
2
Outgoing cross-relations:
8
Average rating:
0
by
0
users
Enter comment
Select article text to quote
Cancel
Enter
welcome text
First name
Last name
Email
Skip
Join
x
Select file to upload