epitraxr

Introduction to epitraxr

EpiTrax is a central repository for epidemiological data developed by Utah State’s Department of Health and Human Services (DHHS). It is now used by several other states. Through EpiTrax, public health officials have access to many different types of disease surveillance data, which they use to produce regular (e.g., weekly, monthly, annual) reports on their respective jurisdictions. This can be a tedious, time-intensive process.

The epitraxr package makes it fast and easy to process EpiTrax data and produce multiple reports.

EpiTrax Data

To explore basic report functions in epitraxr, we’ll use this sample dataset:

data_file <- "vignette-data/epitrax_data.csv"
head(read.csv(data_file))
#>   patient_mmwr_week patient_mmwr_year                      patient_disease
#> 1                31              2020 Influenza-associated hospitalization
#> 2                51              2019 Influenza-associated hospitalization
#> 3                27              2021 Influenza-associated hospitalization
#> 4                30              2024       Coronavirus, Novel (2019-nCoV)
#> 5                24              2020 Influenza-associated hospitalization
#> 6                13              2022 Influenza-associated hospitalization

When you export data from EpiTrax, each row corresponds to a single disease case. EpiTrax can provide many different data points, but epitraxr only cares about three:

The package ignores all other columns.

Read in the data with the read_epitrax_data() function:

epitrax_data <- read_epitrax_data(data_file)
head(epitrax_data)
#>                                disease month year counts
#> 1 Influenza-associated hospitalization     8 2020      1
#> 2 Influenza-associated hospitalization    12 2019      1
#> 3 Influenza-associated hospitalization     7 2021      1
#> 4       Coronavirus, Novel (2019-nCoV)     7 2024      1
#> 5 Influenza-associated hospitalization     6 2020      1
#> 6 Influenza-associated hospitalization     4 2022      1

This validates the input data and converts the week number to a month number (1-12), because reports generally use months instead of weeks. The function also adds the counts column (initially all rows have a count of 1), which is used internally in manipulating the data while generating reports.

Disease Lists

Before you can generate reports from the data, epitraxr needs a list of diseases to include in the report. Often, you’ll have two lists, one for internal reports and one for public reports. Read these files in using the functions get_report_diseases_internal() and get_report_diseases_public().

internal_disease_list <- "vignette-data/ireport_diseases.csv"
internal_diseases <- get_report_diseases_internal(internal_disease_list)
head(internal_diseases)
#>                      EpiTrax_name                      Group_name
#> 1                         Anthrax                Zoonotic Disease
#> 2             Botulism, foodborne                  Enteric Toxins
#> 3              Campylobacteriosis       Bacterial Enteric Disease
#> 4          Chickenpox (Varicella)    Vaccine-Preventable Diseases
#> 5 Chlamydia trachomatis infection Sexually Transmitted Infections
#> 6                         Cholera                  Enteric Toxins

internal_diseases has two columns. EpiTrax_name is the disease name as reported by EpiTrax. All internal reports need the values in this column. Group_name is for grouping diseases, which is only used by create_report_grouped_stats(). If you aren’t creating grouped reports, you don’t need Group_name in your internal disease list.

public_disease_list <- "vignette-data/preport_diseases.csv"
public_diseases <- get_report_diseases_public(public_disease_list)
head(public_diseases)
#>                           EpiTrax_name                       Public_name
#> 1               Chickenpox (Varicella)                        Chickenpox
#> 2      Chlamydia trachomatis infection                         Chlamydia
#> 3                  Colorado Tick Fever               Colorado Tick Fever
#> 4       Coronavirus, Novel (2019-nCoV)                          COVID-19
#> 5                 HIV Infection, adult Human Immondeficiency Virus (HIV)
#> 6 Influenza-associated hospitalization       Influenza (hospitalization)

public_diseases also has two columns. Like internal_diseases, EpiTrax_name is the disease name as reported by EpiTrax. All public reports need the values in this column to properly compute statistics from the data. Public_name is used by certain functions (prefixed with create_public_report_) to translate the EpiTrax disease name to something more accessible to the public. Public_name is also used to combine related diseases in the final report (e.g., “Syphilis, primary” and “Syphilis, secondary” publicly reported by the collected statistic of “Syphilis”).

Generating Reports: Standard Mode

We can now call the report generation functions, such as create_report_annual_counts(), providing the list of diseases we want to include in our report.

report <- create_report_annual_counts(
  data = epitrax_data,
  diseases = internal_diseases$EpiTrax_name
)

head(report)
#>                           disease 2019 2020 2021 2022 2023 2024
#> 1                         Anthrax    0    0    0    0    0    0
#> 2             Botulism, foodborne    0    0    0    0    0    0
#> 3              Campylobacteriosis    0    0    0    0    0    0
#> 4          Chickenpox (Varicella)  218  318  263  234  249  292
#> 5 Chlamydia trachomatis infection    0    0    0    0    0    0
#> 6                         Cholera    0    0    0    0    0    0

This gives us a data frame containing a row for each disease in our disease list and a column showing the case counts for each year in the dataset.

Let’s call the report function again, but this time give it the public disease list.

report <- create_report_annual_counts(
  data = epitrax_data,
  diseases = public_diseases$EpiTrax_name
)

head(report)
#>                                disease 2019 2020 2021 2022 2023 2024
#> 1               Chickenpox (Varicella)  218  318  263  234  249  292
#> 2      Chlamydia trachomatis infection    0    0    0    0    0    0
#> 3                  Colorado Tick Fever    0    0    0    0    0    0
#> 4       Coronavirus, Novel (2019-nCoV) 1014 1627 2398 1855  908 1191
#> 5                 HIV Infection, adult    0    0    0    0    0    0
#> 6 Influenza-associated hospitalization  625 1733 1889 2289 1664 1466