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aarhus-covid19-datathon

Data and code for Aarhus COVID-19 datathon https://events.au.dk/coviddata

abstractgps.jl

Abstract types and methods for Gaussian Processes.

bayesiannonparametrics.jl

BayesianNonparametrics in julia

contact_based_epidemiology

Message-Passing framework for SIR epidemic spreading on temporal networks

contagion

Monte-Carlo simulator for disease spreading on static complex networks with multiple outbreak locations.

covid19survey

source code for "Monitoring Public Behaviour During a Pandemic Using Surveys: Proof-of-Concept Via Epidemic Modelling"

covidsurvey

WIP package that explores the relationship between survey data and epidemiological observations on Covid19

effective_distance

The effective distance is an alternative distance metric, which takes into account the local topology of the network and the fluxes along the edges. It has been introduced to predict the time that an infection needs in order to spread from a given source node to a specific target.

epidemia.jl

WIP package that tries to implement the corresponding R-package from Imperial College London in Julia

logdensityproblems.jl

A common framework for implementing and using log densities for inference.

stheno.jl

Probabilistic Programming with Gaussian processes in Julia

temporal_contagion

Monte-Carlo integration of the SIR epidemic model on time-varying networks

temporalgps.jl

Fast inference for Gaussian processes in problems involving time

temporalnetworkepidemics

A matrix based approach to epidemics on temporal networks. We use methods from linear algebra to simulate infection processes on time-varying topologies. For more information see reference

threshold