sdwfrost

# Ordinary differential equation model with uncertainty quantification
Simon Frost (@sdwfrost), 2021-12-20

## Introduction

The classical ODE version of the SIR model is:

- Deterministic
- Continuous in time
- Continuous in state

sdwfrost

from __future__ import division
from numpy.fft import rfft
from numpy import argmax, mean, diff, log, nonzero
from scipy.signal import blackmanharris, correlate
from time import time
import sys
try:
    import soundfile as sf
except ImportError:
    from scikits.audiolab import flacread

sdwfrost

GCC compiler optimization for ARM-based systems

2017-03-03 fm4dd

The gcc compiler can optimize code by taking advantage of CPU specific features. Especially for ARM CPU's, this can have impact on application performance. ARM CPU's, even under the same architecture, could be implemented with different versions of floating point units (FPU). Utilizing full FPU potential improves performance of heavier operating systems such as full Linux distributions.

-mcpu, -march: Defining the CPU type and architecture

These flags can both be used to set the CPU type. Setting one or the other is sufficient.

sdwfrost

# Sine regression using echo state networks in ReservoirComputing.jl

## Loading libraries

```julia
using Random
using Distributions
using Plots
using StatsPlots
using ParameterizedFunctions

sdwfrost

# Ordinary differential equation model using ModiaSim
Simon Frost (@sdwfrost), 2021-04-15

## Introduction

The classical ODE version of the SIR model is:

- Deterministic
- Continuous in time
- Continuous in state

sdwfrost

# Composing ODE models using AlgebraicDynamics.jl
Simon Frost (@sdwfrost), 2021-03-17

## Introduction

To keep things simple, I only consider a reduced system with susceptibles and infected individuals, with a constant population size.

## Libraries

```julia

sdwfrost

# Multivariate birth process reparameterisation of the SDE model
Simon Frost (@sdwfrost), 2020-06-12

## Introduction

[Fintzi et al.](https://arxiv.org/abs/2001.05099) reparameterise a stochastic epidemiological model in two ways:
- they consider the dynamics of time-integrated rates (infection and recovery in the SIR model); and
- they use a log-transformed scale, to model stochastic perturbations due to stochasticity on a multiplicative scale.

There are lots of advantages to this parameterisation, not the least that the states in this model more closely match the kind of data that are usually collected.

sdwfrost

# Stochastic differential equation model using Bridge.jl
Simon Frost (@sdwfrost), 2021-03-13

## Introduction

A stochastic differential equation version of the SIR model is:

- Stochastic
- Continuous in time
- Continuous in state

sdwfrost

# Moment closure of an SIR reaction network model using MomentClosure
Simon Frost (@sdwfrost), 2021-03-10

## Libraries

```julia
using DifferentialEquations
using OrdinaryDiffEq
using MomentClosure
using ModelingToolkit

sdwfrost

# Moment closure of an SIR reaction network model using MomentClosure
Simon Frost (@sdwfrost), 2021-03-10

## Libraries

```julia
using OrdinaryDiffEq
using MomentClosure
using ModelingToolkit
using Tables