COVID

A Simple Docker-Based Workflow for Deploying a Machine Learning Model

In software engineering, the famous quote by Phil Karlton, extended by Martin Fowler goes something like: “There are two hard things in computer science: cache invalidation, naming things, and off-by-one errors.” In data science, there’s one hard thing that towers over all other hard things: deployment.

COVID-19 Hierarchical Bayesian Logistic Model with pymc3

I have two outstanding tasks from the previous notebooks. The first is that I haven’t iterated over all countries.

COVID-19 Logistic Bayesian Model

This post builds upon the exponential model created in a previous post. The main issue was that there an exponential model does not include a limit. A logistic model introduces this limit. I also perform some very basic backtesting and future prediction.

COVID-19 Exponential Bayesian Model Backtesting

This notebook builds upon the exponential bayesian model to implement simple backtesting. The idea here is to hold out data, train a model, and see how well the model is able to predict those results.

COVID-19 Exponential Bayesian Model

The purposes of this notebook is to provide initial experience with the pymc3 library for the purpose of modeling and forecasting COVID-19 virus summary statistics. This model is very simple, and therefore not very accurate, but serves as a good introduction to the topic.

COVID-19 Response: Athena Project and an Introduction Bayesian Analysis

Over the next couple of weeks I will be using Bayesian analysis to model the spread of COVID-19. Inspired by Alex Stage who started the Athena Project, I have committed Winder Research to helping Athena reach its goals.

Winder Research logo

EMail

web@WinderResearch.com

Registered Address

Winder Research and Development Ltd.,

Adm Accountants Ltd, Windsor House,

Cornwall Road,

Harrogate,

North Yorkshire,

HG1 2PW,

UK

Registration Number

08762077

VAT Number

GB214263735
© Winder Research and Development Ltd. 2016-2018; all rights reserved.