In leyaozh/CA-eSAIR:
Purpose
As the COVID-19 pandemic continues worsening in the US, it is of critical importance todevelop a health information system that provides timely risk evaluation and prediction of theCOVID-19 infection in communities. We propose a spatiotemporal epidemiological forecastmodel that combines a spatial cellular automata (CA) with a temporal extended Susceptible-Antibody-Infectious-Removed (eSAIR) model under time-varying state-specific control mea-sures. This new toolbox enables the projection of the county-level COVID-19 prevalence over3,109 counties in the continental US, includingt-day ahead risk forecast and the risk relatedto a travel route. In comparison to the existing temporal risk prediction models, the proposedCA-eSAIR model informs the projected county-level risk to governments and residents of thelocal coronavirus spread patterns and the associated personal risks at specific geolocations. Suchhigh-resolution risk projection is useful for decision-making on business reopening and resourceallocation for COVID-19 tests.
Preparation
To install and use this R package from Github, you will need to first install the R package devtools. Please uncomment the codes to install them. eSIR depends on three other packages, rjags (an interface to the JAGS library), chron and gtools, which could be installed with CA-eSAIR if not yet.
An error may occur if you have not yet installed JAGS-4.x.y.exe (for any x >= 0, y >=0). Windows users may download and install JAGS from here. Mac users may follow steps at casallas/8411082.
# install.packages("devtools")
# library(devtools)
# install_github("leyaozh/CA-eSAIR")
library(CA-eSAIR)
Our data are collected daily from dxy.com. Alternatively, we notice some convenient access to COVID-19 data from GuangchuangYu/nCov2019.
For data outside China, we use JHU CSSE GitHub data. Another package coronavirus has its GitHub version udpated daily, which is also quite useful.
We also pre-calculate some parameters in
data("confirmed") # From JHU CSSE
data("death") # From JHU CSSE
data("recovered") # partly from 1Point3Acres
data("USA_state_N") #population in each state
knitr::opts_chunk$set(echo = TRUE)
GitHub Documents
This is an R Markdown format used for publishing markdown documents to GitHub. When you click the Knit button all R code chunks are run and a markdown file (.md) suitable for publishing to GitHub is generated.
Including Code
You can include R code in the document as follows:
summary(cars)
Including Plots
You can also embed plots, for example:
plot(pressure)
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.
leyaozh/CA-eSAIR documentation built on June 28, 2020, 2:39 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Purpose
As the COVID-19 pandemic continues worsening in the US, it is of critical importance todevelop a health information system that provides timely risk evaluation and prediction of theCOVID-19 infection in communities. We propose a spatiotemporal epidemiological forecastmodel that combines a spatial cellular automata (CA) with a temporal extended Susceptible-Antibody-Infectious-Removed (eSAIR) model under time-varying state-specific control mea-sures. This new toolbox enables the projection of the county-level COVID-19 prevalence over3,109 counties in the continental US, includingt-day ahead risk forecast and the risk relatedto a travel route. In comparison to the existing temporal risk prediction models, the proposedCA-eSAIR model informs the projected county-level risk to governments and residents of thelocal coronavirus spread patterns and the associated personal risks at specific geolocations. Suchhigh-resolution risk projection is useful for decision-making on business reopening and resourceallocation for COVID-19 tests.
Preparation
To install and use this R package from Github, you will need to first install the R package devtools. Please uncomment the codes to install them. eSIR depends on three other packages, rjags (an interface to the JAGS library), chron and gtools, which could be installed with CA-eSAIR if not yet.
An error may occur if you have not yet installed JAGS-4.x.y.exe (for any x >= 0, y >=0). Windows users may download and install JAGS from here. Mac users may follow steps at casallas/8411082.
# install.packages("devtools") # library(devtools) # install_github("leyaozh/CA-eSAIR") library(CA-eSAIR)
Our data are collected daily from dxy.com. Alternatively, we notice some convenient access to COVID-19 data from GuangchuangYu/nCov2019.
For data outside China, we use JHU CSSE GitHub data. Another package coronavirus has its GitHub version udpated daily, which is also quite useful.
We also pre-calculate some parameters in
data("confirmed") # From JHU CSSE data("death") # From JHU CSSE data("recovered") # partly from 1Point3Acres data("USA_state_N") #population in each state
knitr::opts_chunk$set(echo = TRUE)
GitHub Documents
This is an R Markdown format used for publishing markdown documents to GitHub. When you click the Knit button all R code chunks are run and a markdown file (.md) suitable for publishing to GitHub is generated.
Including Code
You can include R code in the document as follows:
summary(cars)
Including Plots
You can also embed plots, for example:
plot(pressure)
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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