Weekly Surveillance Summary of U.S. COVID-19 Activity
The U.S. Centers for Disease Control provides weekly summary and interpretation of key indicators that have been adapted to track the COVID-19 pandemic in the United States. Tools are provided to retrive data from both COVIDView (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html) and COVID-NET (https://gis.cdc.gov/grasp/COVIDNet/COVID19_3.html).
The following functions are implemented:
about
: Display information about the data sourceage_groups
: Return age groups used in the surveillanceavailable_seasons
: Show available seasonsclinical_labs
: Retrieve U.S. Clinical Laboratories Reporting SARS-CoV-2 Test Results to CDClaboratory_confirmed_hospitalizations
: Retrieve Laboratory-Confirmed COVID-19-Associated Hospitalizationsmmwr_week_to_date
: Convert an MMWR year+week or year+week+day to a Date objectmmwr_week
: Convert a Date to an MMWR day+week+yearmmwr_weekday
: Convert a Date to an MMWR weekdaymmwrid_map
: MMWR ID to Calendar Mappingsmortality_surveillance_data
: Retrieve NCHS Mortality Surveillance Datanssp_er_visits_national
: Retrieve National Syndromic Surveillance Program (NSSP): Emergency Department Visits Percentage of Visits for COVID-19-Like Illness (CLI) or Influenza-like Illness (ILI)nssp_er_visits_regional
: Retrieve Regional Syndromic Surveillance Program (NSSP): Emergency Department Visits Percentage of Visits for COVID-19-Like Illness (CLI) or Influenza-like Illness (ILI)provisional_death_counts
: Retrieve Provisional Death Counts for Coronavirus Disease (COVID-19)public_health_labs_national
: Retrieve National Surveillance of U.S. State and Local Public Health Laboratories Reporting to CDCpublic_health_labs_regional
: Retrieve Regional Surveillance of U.S. State and Local Public Health Laboratories Reporting to CDCsurveillance_areas
: Show network & network catchmentsinstall.packages("cdccovidview", repos = c("https://cinc.rud.is", "https://cloud.r-project.org/")) # or remotes::install_git("https://git.rud.is/hrbrmstr/cdccovidview.git") # or remotes::install_git("https://git.sr.ht/~hrbrmstr/cdccovidview") # or remotes::install_gitlab("hrbrmstr/cdccovidview") # or remotes::install_bitbucket("hrbrmstr/cdccovidview") # or remotes::install_github("hrbrmstr/cdccovidview")
NOTE: To use the ‘remotes’ install options you will need to have the {remotes} package installed.
library(cdccovidview) # current version packageVersion("cdccovidview") ## [1] '0.1.1'
library(cdccovidview) library(hrbrthemes) library(tidyverse) hosp <- laboratory_confirmed_hospitalizations() hosp ## # A tibble: 4,590 x 8 ## catchment network year mmwr_year mmwr_week age_category cumulative_rate weekly_rate ## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> ## 1 Entire Network COVID-NET 2020 2020 10 0-4 yr 0 0 ## 2 Entire Network COVID-NET 2020 2020 11 0-4 yr 0 0 ## 3 Entire Network COVID-NET 2020 2020 12 0-4 yr 0 0 ## 4 Entire Network COVID-NET 2020 2020 13 0-4 yr 0.3 0.3 ## 5 Entire Network COVID-NET 2020 2020 14 0-4 yr 0.6 0.3 ## 6 Entire Network COVID-NET 2020 2020 15 0-4 yr NA NA ## 7 Entire Network COVID-NET 2020 2020 16 0-4 yr NA NA ## 8 Entire Network COVID-NET 2020 2020 17 0-4 yr NA NA ## 9 Entire Network COVID-NET 2020 2020 18 0-4 yr NA NA ## 10 Entire Network COVID-NET 2020 2020 19 0-4 yr NA NA ## # … with 4,580 more rows c( "0-4 yr", "5-17 yr", "18-49 yr", "50-64 yr", "65+ yr", "65-74 yr", "75-84 yr", "85+" ) -> age_f mutate(hosp, start = mmwr_week_to_date(mmwr_year, mmwr_week)) %>% filter(!is.na(weekly_rate)) %>% filter(catchment == "Entire Network") %>% select(start, network, age_category, weekly_rate) %>% filter(age_category != "Overall") %>% mutate(age_category = factor(age_category, levels = age_f)) %>% ggplot() + geom_line( aes(start, weekly_rate) ) + scale_x_date( date_breaks = "2 weeks", date_labels = "%b\n%d" ) + facet_grid(network~age_category) + labs( x = NULL, y = "Rates per 100,000 pop", title = "COVID-NET Weekly Rates by Network and Age Group", caption = sprintf("Source: COVID-NET: COVID-19-Associated Hospitalization Surveillance Network, Centers for Disease Control and Prevention.\n<https://gis.cdc.gov/grasp/COVIDNet/COVID19_3.html>; Accessed on %s", Sys.Date()) ) + theme_ipsum_es(grid="XY")
head(clinical_labs()) ## week num_labs tested tested_pos pct_pos region source ## 1 202011 26 2785 182 0.065 National Clinical Labs ## 2 202012 41 18494 1149 0.062 National Clinical Labs ## 3 202013 50 37390 2966 0.079 National Clinical Labs ## 4 202014 37 36468 2798 0.077 National Clinical Labs
head(public_health_labs_national()) ## week num_labs tested tested_pos pct_pos age_group region source ## 1 202010 73 8049 945 0.117 Overall National Public Health Labs ## 2 202011 79 32072 3292 0.103 Overall National Public Health Labs ## 3 202012 80 63369 6494 0.103 Overall National Public Health Labs ## 4 202013 79 56443 9529 0.169 Overall National Public Health Labs ## 5 202014 75 65917 12177 0.185 Overall National Public Health Labs ## 6 202010 73 212 9 0.043 0-4 yr National Public Health Labs head(public_health_labs_regional()) ## week num_labs tested tested_pos pct_pos region source ## 1 202010 8 619 46 0.074 Region 1 Public Health Labs ## 2 202011 17 3208 194 0.061 Region 1 Public Health Labs ## 3 202012 18 9608 732 0.076 Region 1 Public Health Labs ## 4 202013 16 4625 700 0.151 Region 1 Public Health Labs ## 5 202014 15 6123 1611 0.263 Region 1 Public Health Labs ## 6 202010 5 1381 193 0.140 Region 2 Public Health Labs
head(nssp_er_visits_national()) ## week num_fac total_ed_visits visits pct_visits visit_type region source year ## 1 40 3255 2146776 19503 0.009 ili National Emergency Departments 2019 ## 2 41 3249 2106999 20457 0.010 ili National Emergency Departments 2019 ## 3 42 3256 2101358 22515 0.011 ili National Emergency Departments 2019 ## 4 43 3254 2122427 23776 0.011 ili National Emergency Departments 2019 ## 5 44 3295 2087335 25466 0.012 ili National Emergency Departments 2019 ## 6 45 3315 2137854 29948 0.014 ili National Emergency Departments 2019 head(nssp_er_visits_regional()) ## week num_fac total_ed_visits visits pct_visits visit_type region source year ## 1 41 202 130377 814 0.006 ili Region 1 Emergency Departments 2019 ## 2 42 202 132385 912 0.007 ili Region 1 Emergency Departments 2019 ## 3 43 202 131866 883 0.007 ili Region 1 Emergency Departments 2019 ## 4 44 203 128256 888 0.007 ili Region 1 Emergency Departments 2019 ## 5 45 203 127466 979 0.008 ili Region 1 Emergency Departments 2019 ## 6 46 202 125306 1188 0.009 ili Region 1 Emergency Departments 2019
head(mortality_surveillance_data()) ## year week total_deaths deaths pct_deaths cause region source ## 1 2019 40 52452 0 0 COVID-19 National NCHS ## 2 2019 41 52860 0 0 COVID-19 National NCHS ## 3 2019 42 54129 0 0 COVID-19 National NCHS ## 4 2019 43 53914 0 0 COVID-19 National NCHS ## 5 2019 44 53980 0 0 COVID-19 National NCHS ## 6 2019 45 55468 0 0 COVID-19 National NCHS
pd <- provisional_death_counts() head(pd$by_week) ## week covid_deaths total_deaths percent_expected_deaths pneumonia_deaths pneumonia_and_covid_deaths ## 2 2020-02-01 0 56402 0.95 3618 0 ## 3 2020-02-08 0 56737 0.95 3601 0 ## 4 2020-02-15 0 55273 0.94 3580 0 ## 5 2020-02-22 0 54859 0.94 3427 0 ## 6 2020-02-29 5 54513 0.95 3464 3 ## 7 2020-03-07 18 53801 0.93 3552 11 ## all_influenza_deaths_j09_j11 ## 2 452 ## 3 483 ## 4 489 ## 5 502 ## 6 573 ## 7 555 head(pd$by_age) ## age_group covid_deaths total_deaths percent_expected_deaths pneumonia_deaths pneumonia_and_covid_deaths ## 12 All ages 4984 511424 0.89 36423 2341 ## 13 Under 1 yr 0 2727 0.65 19 0 ## 14 1-4 yr 1 552 0.76 27 1 ## 15 5-14 yr 1 809 0.73 26 0 ## 16 15-24 yr 6 4638 0.81 87 2 ## 17 25-34 yr 46 9624 0.86 257 21 ## all_influenza_deaths_j09_j11 ## 12 4541 ## 13 9 ## 14 26 ## 15 34 ## 16 35 ## 17 106 head(pd$by_state) ## state covid_deaths total_deaths percent_expected_deaths pneumonia_deaths pneumonia_and_covid_deaths ## 25 Alabama 14 9220 0.87 539 4 ## 26 Alaska 1 627 0.75 31 1 ## 27 Arizona 26 11862 0.97 748 13 ## 28 Arkansas 3 5938 0.92 372 2 ## 29 California 175 52505 0.94 4170 96 ## 30 Colorado 62 7787 0.98 493 33 ## all_influenza_deaths_j09_j11 ## 25 75 ## 26 3 ## 27 95 ## 28 62 ## 29 511 ## 30 77 head(pd$by_sex) ## sex covid_deaths total_deaths percent_expected_deaths pneumonia_deaths pneumonia_and_covid_deaths ## 79 Male 2993 262727 0.90 19129 1374 ## 80 Female 1991 248679 0.89 17294 967 ## 81 Unknown 0 18 0.82 0 0 ## all_influenza_deaths_j09_j11 ## 79 2262 ## 80 2279 ## 81 0