Modeling cumulative cases of Covid-19 in the US during the Covid 19 Delta wave using Bayesian methods.

Last update: Jan 05, 2022

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Overview

Introduction

The goal of this analysis is to find a model that fits the observed cumulative cases of COVID-19 in the US, starting in Mid-July 2021 and ending in late November 2021. Mid-July coincides with the introduction and subsequent domination of the delta variant of COVID-19 and late November is the latest data released by the World Health Organization at the time of writing. The data used for this project is made freely available by the World Health Organization (WHO) here. The first section includes exploratory data analysis and formatting. The second section includes several helper functions for plotting data and results. The third section is comprised of the candidate models defined in increasing levels of complexity. The final section is the comparison and conclusion.

This work is heavily inspired by the Thomas Wiecki talk given at PyMCon 2020:

https://www.youtube.com/watch?v=ZxR3mw-Znzc&t=559s

Owner

Alexander Keeney

Georgia Tech Masters Student in Analytics, Data Scientist in Charlotte North Carolina. I'm interested in machine learning apps/analysis, mostly NLP and RL

GitHub Repository

BPEmb is a collection of pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE) and trained on Wikipedia.

BPEmb is a collection of pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE) and trained on Wikipedia. Its intended use is as input for neural models in natural languag

1.1k Jan 03, 2023

Modeling cumulative cases of Covid-19 in the US during the Covid 19 Delta wave using Bayesian methods.

Related tags

Overview

Introduction

Owner

Alexander Keeney

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