Simulation

Using Statistical Modeling to Win a NFL Knockout Pool

Introduction Admittedly, I am a terrible sports fan. However, I do enjoy competition and the opportunity to participate in a little “smack talk” with family and friends. Every year, family, friends, and folks from my childhood community come together to play a game of NFL Pool Knockout. The rules are deceptively simple: Each participant picks one NFL team each week If your team wins, you advance to the next week If your team loses or ties, you’re eliminated Each team can only be selected once during the season Last person standing wins Sounds simple, right?

Simulating Deterministic SIR Models in a Closed and Open Population Playground

Below, you will find code for creating a shiny flex-dashboard for simple SIR open and closed population models. --- title: "Simulating SIR Models in a Closed and Open Population" date: "`r format(Sys.time(), '%B %d, %Y')`" editor_options: chunk_output_type: console output: flexdashboard::flex_dashboard runtime: shiny --- {r setup, include=FALSE} knitr::opts_chunk$set(echo = F, warning = F, message = F, fig.align = 'center', results = 'hide', fig.keep = 'all') library(tidyverse) library(cowplot) # https://rmarkdown.rstudio.com/flexdashboard/layouts.html#multiple_pages {r} #.............................................................. # Open Model #.

Stochastic Simulations for Flattening the Curve Playground

Below, you will find code for creating a shiny flex-dashboard for performing simple stochastic simulations with one time-interruption corresponding to an intervention (unrealistically simulated all at once). Code relies on the excellent EpiModel R package. --- title: "Stochastic Simulations for Flattening the Curve" date: "`r format(Sys.time(), '%B %d, %Y')`" editor_options: chunk_output_type: console output: flexdashboard::flex_dashboard runtime: shiny --- {r setup, include=FALSE} knitr::opts_chunk$set(echo = F, warning = F, message = F, fig.align = 'center', results = 'hide', fig.