comments

Age-Patch Mobility Mixing

Deriving contact patterns for age & patch mixing, based on recurrent mobility data. [1]

Deriving contact patterns for age & patch mixing, based on recurrent mobility data, using:

  • Canadian contact projections from POLYMOD (Prem 2021)
  • Ontario mobility data from Veraset (Ghasemi 2021)
  • Patch-based mixing methods from (Arenas 2020)

Source: Adaptive data-driven age and patch mixing in contact networks with recurrent mobility [2]

RELATED ARTICLESExplain
⌅
EIDM 
⌃
Software
⌃
Software programs
■
Age-Patch Mobility Mixing
⇤
R
⇤
Data analysis
⇥
Sharmistha Mishra
□
AlgebraicJulia
□
Bayesian Evolutionary Analysis by Sampling Trees (BEAST)
□
CCODWG
□
Covasim
□
COVID Hospital Surge
□
COVID JupyterHub
□
COVID Prevalence Estimate
□
COVID-19 Re - Generation Time
□
covidseir
□
CovidSim
□
covid-topic-modeling
□
Dempster-Shafer Bayesian Network inference package
□
diseasemapping
□
Efficacy of quarantine
□
epi base package for SyncroSim
□
EpiEstim
□
epigrowthfit
□
EpiILM
□
EpiILMCT
□
Estimating the infection fatality rate of Covid-19
□
geostatsp
□
Hexagonalfan
□
localEM
□
Localized Epidemiological Agent-Based Simulation (ABS)
□
macpan2
□
Mathematical epidemiology in a data-rich world
□
McMasterPandemic
□
MultiBD
□
Ontario pandemic mobility
□
Open Data Model
□
pathogen.jl
□
Pathosim
□
plASgraph2
□
pmcmc_dynamic_modeling
□
Prediction Codes
□
PyCoMod
□
python Population Modeller
□
SamPy
□
Stochastic simulations of classroom-level COVID-19 outbreaks
□
SyncroSim
□
Graph of this discussion
Enter the title of your article


Enter a short (max 500 characters) summation of your article
Enter the main body of your article
Lock
+Comments (0)
+Citations (2)
+About
Enter comment

Select article text to quote
welcome text

First name   Last name 

Email

Skip