Dynamic Outbreak Modeling

An Exercise in Shiny and Slidify

Courtney D. Shelley
Aspiring Epidemiologist

equuSIR

Dynamically Modeling Outbreaks

equuSIR will allow you to see outbreaks in "real time" for both the current 2014 outbreak and past outbreak occurences. For the 2012 Outbreak,

data_2012[7:11, ]            # display sample tidy data
##    DURATION REPORT       DATE STATE     COUNTY FIPS.CODE ACTV CUMLTV QRNTN
## 7       238      4 2012-12-24    NM    LINCOLN        27    0      1     1
## 8       238      4 2012-12-24    NM       MORA        33    0      2     2
## 9       238      4 2012-12-24    NM RIO ARRIBA        39    0      5     5
## 10      238      4 2012-12-24    NM      OTERO        35    0      1     1
## 11      238      4 2012-12-24    NM   SANDOVAL        43    0      4     4
MAP_2012_238 <- choroplethr(df, lod="county", num_buckets=9, warn_na=FALSE, renderAsInsets=TRUE) + 
      scale_fill_brewer(palette="YlOrRd", na.value="white") +
      ggtitle(expression(atop("2012 Outbreak", 
                      atop(italic("Day 238")))))
## Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

Dynamically Rendered Outbreak Mapping

Maps are built on the choroplethr platform. Coloration is used to simulate a spreading wildfire of infection.

MAP_2012_238

plot of chunk mapOutput

App Features

Within the app you can further explore:

  • Current outbreak and additional historical outbreaks from 2004-2012

  • Additional narratives, background information, and epidemiological models

  • Individual infection levels for each outbreak to explore infection and recovery rates

Thanks for stopping by! @_BlackWidoww