# 1 Load Data and Packages

Testing data were obtained from the New York City Department of Health and Mental Hygeine (NYC DOHMH) releasing COVID19 tests results posted on GitHub. A detailed description of data creation can be found below. Covariate are drawn primarily from 2010 US Census results.

Prep the data by ordering to match the spatial dataframe, and add a sequential area ID variable for the model statements.

``````library(maptools, quietly = T)
library(sp)
library(spdep, quietly = T)
library(INLA, quietly = T)
library(ggplot2)
library(kableExtra)

library(DescTools)``````
``````## Registered S3 method overwritten by 'DescTools':
##   method         from
##   reorder.factor gdata``````
``````options(scipen = 10) # don't use scientific notation
options(fmt.num=structure(list(digits=2, big.mark=","), class="fmt")) # force use commas as thousands separator and 2 decimal places

nycDat <- attr(nyc, "data")
order <- match(nycDat\$ZCTA5CE10,testDat\$zcta)
testDat<- testDat[order,]
testDat\$ID.area<-seq(1,nrow(testDat))

## 1.1 Descriptive Statistics

There were 177 ZCTAâ€™s in the data set. The mean COVID-19 test rate per 10,000 ZCTA population was 166.2 (95% CI 156.7, 175.7). The distribution appeared multimodal. The mean COVID-19 test rate per 10,000 tests was 5,176.0 (95% CI 5,045.9, 5,306.1) and appeared skewed. The The 5 ZCTAs with highest positive COVID-19 test numbers per 10,000 population were the same as those with the highest proportion per 10,000 tests (10464, 10470, 10455, 10473, 11234, and 11210). The 5 lowest ZCTAs were also the same for both measures 1(1103, 11102, 11693, 11369, 11363, and 10308). Table one presents comparative statistics for the ZCTAâ€™s with the highest and lowest quantiles for population-based positive test rates.

``````## -------------------------------------------------------------------------
## Positive COVID-19 Tests per 10,000 ZCTA Population
##
##      length         n        NAs     unique         0s       mean
##         177       177          0        = n          0  166.18312
##                100.0%       0.0%                  0.0%
##
##         .05       .10        .25     median        .75        .90
##    71.10536  84.17626  113.19039  162.97451  215.08084  248.76561
##
##       range        sd      vcoef        mad        IQR       skew
##   281.17539  64.25910    0.38668   75.00825  101.89045    0.18618
##
##      meanCI
##   156.65093
##   175.71531
##
##         .95
##   267.41561
##
##        kurt
##    -0.81984
##
## lowest : 42.02216, 49.81734, 50.22831, 60.10303, 61.66783
## highest: 282.27373, 297.51712, 316.63043, 322.37247, 323.19755``````

``````## -------------------------------------------------------------------------
## Positive COVID-19 Tests per 10,000 ZCTA Tests
##
##      length          n        NAs     unique         0s       mean
##         177        177          0        = n          0  5'175.967
##                 100.0%       0.0%                  0.0%
##
##         .05        .10        .25     median        .75        .90
##   3'608.459  3'907.126  4'519.016  5'380.117  5'852.102  6'039.269
##
##       range         sd      vcoef        mad        IQR       skew
##   4'747.624    876.960      0.169    780.456  1'333.086     -0.589
##
##      meanCI
##   5'045.879
##   5'306.055
##
##         .95
##   6'198.431
##
##        kurt
##      -0.128
##
## lowest : 2'586.207, 2'937.063, 2'972.973, 3'142.857, 3'178.295
## highest: 6'481.481, 6'614.493, 6'818.530, 6'982.703, 7'333.831``````