Code for Bayesian Hierarchical Spatial Modeling of New York City ZIP Code-Level Positive COVID19 Testing Results

Division of Trauma and Critical Care

Abstract

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")
```

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
```