Get data on the relative circulation of each influenza A subtype
Source:R/data_import_funs.R
get_country_cocirculation_data.Rdget_country_cocirculation_data() imports data on the fraction of influenza A cases in a specific country and year that were caused by each influenza A subtype (H1N1, H2N2, or H3N2), or group (group 1 or group 2). Group 1 contains H1N1 and H2N2, and group 2 contains H2N2.
Usage
get_country_cocirculation_data(
country,
max_year,
min_samples = 30,
output_format = "tibble"
)Arguments
- country
country of interest. Run
show_available_countries()for a list of valid inputs.- max_year
last year of interest. Results will be generated from 1918:max_year.
- min_samples
if fewer than
min_samples(default 30) are reported in the country and year of interest, the function will substitute data from the corresponding WHO region.- output_format
can be 'tibble' (the default) or 'matrix' (used mainly for convenience within other functions)
Value
A matrix with rows showing the calendar year, the fraction of influenza A-positive specimens of each subtype (rows A/H1N1, A/H2N2, and A/H3N2), and of each HA group (rows group 1, and group 2). Row A should always be 1, as it shows the sum of subtype-specific fractions. Row B is a placeholder whose values are all NA.
Details
The data come from three sources:
Historical assumptions: From 1918-1956, we assume only H1N1 circulated. From 1957-1967, we assume only H2N2 circulated. From 1968-1976, we assume only H3N2 circulated.
Thompson et al. JAMA, 2003: From 1977-1996 we pull data on the relative dominance of H1N1 and H3N2 from Table 1 of Thompson et al. 2003, which reports surveillance data collected in the United States.
From 1997-present, we pull in country or region-specific data from WHO Flu Mart on the fraction of specimens collected in routine influenza surveillance that test positive for each subtype. Country-specific data are the default. Regional data, then global data are used if the number of country or region-specific specimens is insufficient.
get_template_data()imports the data for 1918-1996.get_country_inputs_1997_to_present()andget_regional_inputs_1997_to_present()import the data for 1997 on.
See also
doi:10.1126/science.aag1322 Gostic et al. Science, (2016) for detailed methods.
Examples
get_country_cocirculation_data("United States", "2019")
#> # A tibble: 105 × 9
#> year `A/H1N1` `A/H2N2` `A/H3N2` A B group1 group2 data_from
#> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl> <dbl> <chr>
#> 1 1918 1 0 0 1 NA 1 0 Historical_assump…
#> 2 1919 1 0 0 1 NA 1 0 Historical_assump…
#> 3 1920 1 0 0 1 NA 1 0 Historical_assump…
#> 4 1921 1 0 0 1 NA 1 0 Historical_assump…
#> 5 1922 1 0 0 1 NA 1 0 Historical_assump…
#> 6 1923 1 0 0 1 NA 1 0 Historical_assump…
#> 7 1924 1 0 0 1 NA 1 0 Historical_assump…
#> 8 1925 1 0 0 1 NA 1 0 Historical_assump…
#> 9 1926 1 0 0 1 NA 1 0 Historical_assump…
#> 10 1927 1 0 0 1 NA 1 0 Historical_assump…
#> # … with 95 more rows
get_country_cocirculation_data("Laos", "2022", min_samples = 40)
#> # A tibble: 105 × 9
#> year `A/H1N1` `A/H2N2` `A/H3N2` A B group1 group2 data_from
#> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl> <dbl> <chr>
#> 1 1918 1 0 0 1 NA 1 0 Historical_assump…
#> 2 1919 1 0 0 1 NA 1 0 Historical_assump…
#> 3 1920 1 0 0 1 NA 1 0 Historical_assump…
#> 4 1921 1 0 0 1 NA 1 0 Historical_assump…
#> 5 1922 1 0 0 1 NA 1 0 Historical_assump…
#> 6 1923 1 0 0 1 NA 1 0 Historical_assump…
#> 7 1924 1 0 0 1 NA 1 0 Historical_assump…
#> 8 1925 1 0 0 1 NA 1 0 Historical_assump…
#> 9 1926 1 0 0 1 NA 1 0 Historical_assump…
#> 10 1927 1 0 0 1 NA 1 0 Historical_assump…
#> # … with 95 more rows