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



testDat<-readRDS("~/testDat.RDS")
nyc <- readRDS("~/nycMap4COVID.RDS")

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

nyc.adj <- paste("~/nyc.graph")

1.1 Descriptive Statistics

There were 177 ZCTA’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 ZCTA’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