Individual-level data contributed from around the world

beoutbreakprepared_data(quietly = TRUE)

Source

https://github.com/beoutbreakprepared/nCoV2019

Arguments

quietly: logical(1) defaults to TRUE. If FALSE, warnings generated during parsing will be displayed. These often relate to nonstandard date values that occur idiosyncratically.

Value

tidy data.frame of content

Note

This is individual level data, collected from diverse sources. Data may be messy and we have made limited attempts at clean up.

Citation

misckraemer2020epidemiological, author = nCoV-2019 Data Working Group, title = Epidemiological Data from the nCoV-2019 Outbreak: Early Descriptions from Publicly Available Data, howpublished = Accessed on yyyy-mm-dd from http://virological.org/t/epidemiological-data-from-the-ncov-2019-outbreak-early-descriptions-from-publicly-available-data/337, year = 2020

ARTICLEXu2020-wb, title = "Open access epidemiological data from the COVID-19 outbreak", author = "Xu, Bo and Kraemer, Moritz U G and Open COVID-19 Data Curation Group", journal = "The Lancet infectious diseases", volume = 20, number = 5, pages = "534", month = may, year = 2020, url = "http://dx.doi.org/10.1016/S1473-3099(20)30119-5", file = "All Papers/X/Xu et al. 2020 - Open access epidemiological data from the COVID-19 outbreak.pdf", language = "en", issn = "1473-3099, 1474-4457", pmid = "32087115", doi = "10.1016/S1473-3099(20)30119-5", pmc = "PMC7158984"

Other data-import: acaps_government_measures_data(), acaps_secondary_impact_data(), apple_mobility_data(), cci_us_vaccine_data(), cdc_aggregated_projections(), cdc_excess_deaths(), cdc_social_vulnerability_index(), coronadatascraper_data(), coronanet_government_response_data(), cov_glue_lineage_data(), cov_glue_newick_data(), cov_glue_snp_lineage(), covidtracker_data(), descartes_mobility_data(), ecdc_data(), econ_tracker_consumer_spending, econ_tracker_employment, econ_tracker_unemp_data, economist_excess_deaths(), financial_times_excess_deaths(), google_mobility_data(), government_policy_timeline(), jhu_data(), jhu_us_data(), kff_icu_beds(), nytimes_county_data(), oecd_unemployment_data(), owid_data(), param_estimates_published(), test_and_trace_data(), us_county_geo_details(), us_county_health_rankings(), us_healthcare_capacity(), us_hospital_details(), us_state_distancing_policy(), usa_facts_data(), who_cases()

Other case-tracking: align_to_baseline(), bulk_estimate_Rt(), combined_us_cases_data(), coronadatascraper_data(), covidtracker_data(), ecdc_data(), estimate_Rt(), jhu_data(), nytimes_county_data(), owid_data(), plot_epicurve(), test_and_trace_data(), usa_facts_data(), who_cases()

Other individual-cases: cov_glue_lineage_data(), cov_glue_newick_data()

Examples

res = beoutbreakprepared_data()
#> Warning: One or more parsing issues, see `problems()` for details
colnames(res)
#>  [1] "ID"                       "age"                     
#>  [3] "sex"                      "city"                    
#>  [5] "province"                 "country"                 
#>  [7] "latitude"                 "longitude"               
#>  [9] "geo_resolution"           "date_onset_symptoms"     
#> [11] "date_admission_hospital"  "date_confirmation"       
#> [13] "symptoms"                 "lives_in_Wuhan"          
#> [15] "travel_history_dates"     "travel_history_location" 
#> [17] "reported_market_exposure" "additional_information"  
#> [19] "chronic_disease_binary"   "chronic_disease"         
#> [21] "source"                   "sequence_available"      
#> [23] "outcome"                  "date_death_or_discharge" 
#> [25] "notes_for_discussion"     "location"                
#> [27] "admin3"                   "admin2"                  
#> [29] "admin1"                   "country_new"             
#> [31] "admin_id"                 "data_moderator_initials" 
#> [33] "travel_history_binary"   
head(res)
#> # A tibble: 6 × 33
#>   ID        age   sex   city  province country latitude longitude geo_resolution
#>   <chr>     <chr> <chr> <chr> <chr>    <chr>      <dbl>     <dbl> <chr>         
#> 1 000-1-1   NA    male  Shek… Hong Ko… China      22.4      114.  point         
#> 2 000-1-10  78    male  Vo E… Veneto   Italy      45.3       11.7 point         
#> 3 000-1-100 61    fema… NA    NA       Singap…     1.35     104.  admin0        
#> 4 000-1-10… NA    NA    Zhen… Henan    China      34.6      113.  admin2        
#> 5 000-1-10… NA    NA    Ping… Jiangxi  China      27.5      114.  admin2        
#> 6 000-1-10… NA    NA    Yich… Jiangxi  China      28.3      115.  admin2        
#> # … with 24 more variables: date_onset_symptoms <date>,
#> #   date_admission_hospital <date>, date_confirmation <date>, symptoms <chr>,
#> #   lives_in_Wuhan <chr>, travel_history_dates <date>,
#> #   travel_history_location <chr>, reported_market_exposure <chr>,
#> #   additional_information <chr>, chronic_disease_binary <lgl>,
#> #   chronic_disease <chr>, source <chr>, sequence_available <lgl>,
#> #   outcome <chr>, date_death_or_discharge <date>, …