Reads in results from EpiNow2 and converts them into the RtD3 format. Supports
either input via a list object or from a file path/url.
readInEpiNow2(input_list, path, region_var = "region", regions)A list of results as returned by EpiNow2::regional_summary
A character string indicating the path (either file or URL) to the summary results
A character string that identifies the region name used.
A character string indicating the regions of interest to returns. Defaults to all regions.
A named list in the format required by summaryWidget along with a summary table.
# Read in each summary folder
base_path <- "https://raw.githubusercontent.com/epiforecasts/covid-rt-estimates/"
rtData <- readInEpiNow2(
path = paste0(base_path, "master/national/cases/summary"),
region_var = "country")
rtData
#> $summaryData
#> region New confirmed cases by infection date
#> 1: Afghanistan 108 (41 -- 500)
#> 2: Albania 40 (23 -- 80)
#> 3: Algeria 8 (3 -- 21)
#> 4: American Samoa 9 (0 -- 111)
#> 5: Andorra 51 (10 -- 378)
#> ---
#> 212: Venezuela 41 (21 -- 104)
#> 213: Vietnam 1413 (616 -- 3960)
#> 214: Yemen 0 (0 -- 0)
#> 215: Zambia 0 (0 -- 0)
#> 216: Zimbabwe 547 (156 -- 2004)
#> Expected change in daily cases Effective reproduction no.
#> 1: Likely increasing 1.3 (0.99 -- 2.1)
#> 2: Likely increasing 1 (0.86 -- 1.3)
#> 3: Likely increasing 1.3 (0.97 -- 1.8)
#> 4: Stable 0.99 (0.39 -- 2.1)
#> 5: Likely increasing 1.1 (0.62 -- 1.7)
#> ---
#> 212: Stable 1 (0.83 -- 1.3)
#> 213: Likely decreasing 0.88 (0.61 -- 1.3)
#> 214: Likely decreasing 0.48 (0.091 -- 1.2)
#> 215: Decreasing 0.061 (0.0042 -- 0.31)
#> 216: Likely increasing 1.4 (0.92 -- 2.1)
#> Rate of growth Doubling/halving time (days)
#> 1: 0.085 (-0.0031 -- 0.27) 8.2 (2.6 -- -220)
#> 2: 0.013 (-0.039 -- 0.091) 53 (7.6 -- -18)
#> 3: 0.08 (-0.0073 -- 0.2) 8.7 (3.4 -- -95)
#> 4: -0.0033 (-0.19 -- 0.28) -210 (2.5 -- -3.7)
#> 5: 0.018 (-0.11 -- 0.17) 39 (4.1 -- -6.2)
#> ---
#> 212: 0.0028 (-0.049 -- 0.084) 240 (8.3 -- -14)
#> 213: -0.034 (-0.11 -- 0.091) -21 (7.6 -- -6.1)
#> 214: -0.16 (-0.31 -- 0.06) -4.4 (12 -- -2.2)
#> 215: -0.33 (-0.39 -- -0.22) -2.1 (-3.2 -- -1.8)
#> 216: 0.11 (-0.023 -- 0.27) 6.3 (2.6 -- -30)
#>
#> $rtData
#> region date strat type median mean sd
#> 1: Afghanistan 2022-01-28 NA estimate 0.8617934 0.8761065 0.11383115
#> 2: Afghanistan 2022-01-29 NA estimate 0.8556268 0.8667321 0.10216471
#> 3: Afghanistan 2022-01-30 NA estimate 0.8494871 0.8573524 0.09216038
#> 4: Afghanistan 2022-01-31 NA estimate 0.8426965 0.8480854 0.08393362
#> 5: Afghanistan 2022-02-01 NA estimate 0.8362875 0.8390684 0.07755753
#> ---
#> 27333: Zimbabwe 2022-05-30 NA forecast 1.4244549 1.4510090 0.36346219
#> 27334: Zimbabwe 2022-05-31 NA forecast 1.4244549 1.4510090 0.36346219
#> 27335: Zimbabwe 2022-06-01 NA forecast 1.4244549 1.4510090 0.36346219
#> 27336: Zimbabwe 2022-06-02 NA forecast 1.4244549 1.4510090 0.36346219
#> 27337: Zimbabwe 2022-06-03 NA forecast 1.4244549 1.4510090 0.36346219
#> lower_90 lower_50 lower_20 upper_20 upper_50 upper_90
#> 1: 0.7161549 0.8012999 0.8384035 0.8862717 0.9358411 1.0873540
#> 2: 0.7198539 0.7984970 0.8347461 0.8781922 0.9221883 1.0527433
#> 3: 0.7204793 0.7968413 0.8297664 0.8710640 0.9085521 1.0214378
#> 4: 0.7190176 0.7919147 0.8248325 0.8627942 0.8973678 0.9953365
#> 5: 0.7179829 0.7884031 0.8187431 0.8543160 0.8858928 0.9726089
#> ---
#> 27333: 0.9167116 1.2103817 1.3373991 1.5078065 1.6578935 2.0945905
#> 27334: 0.9167116 1.2103817 1.3373991 1.5078065 1.6578935 2.0945905
#> 27335: 0.9167116 1.2103817 1.3373991 1.5078065 1.6578935 2.0945905
#> 27336: 0.9167116 1.2103817 1.3373991 1.5078065 1.6578935 2.0945905
#> 27337: 0.9167116 1.2103817 1.3373991 1.5078065 1.6578935 2.0945905
#>
#> $casesInfectionData
#> region date strat type median mean sd lower_90
#> 1: Afghanistan 2022-01-28 NA estimate 635.6 651.0 126.6 471.1
#> 2: Afghanistan 2022-01-29 NA estimate 618.4 633.9 120.4 462.2
#> 3: Afghanistan 2022-01-30 NA estimate 595.8 609.5 112.3 449.0
#> 4: Afghanistan 2022-01-31 NA estimate 570.4 582.2 103.9 435.2
#> 5: Afghanistan 2022-02-01 NA estimate 544.1 553.7 95.7 418.2
#> ---
#> 27333: Zimbabwe 2022-05-30 NA forecast 1557.5 7354.9 63967.7 133.4
#> 27334: Zimbabwe 2022-05-31 NA forecast 1724.8 10035.7 106103.9 130.7
#> 27335: Zimbabwe 2022-06-01 NA forecast 1902.9 13938.6 176793.5 128.9
#> 27336: Zimbabwe 2022-06-02 NA forecast 2102.2 19706.5 295621.7 126.7
#> 27337: Zimbabwe 2022-06-03 NA forecast 2351.6 28352.9 495691.6 124.2
#> lower_50 lower_20 upper_20 upper_50 upper_90
#> 1: 561.5 608.3 666.6 724.0 871.9
#> 2: 549.5 593.5 649.2 706.2 847.7
#> 3: 531.1 572.1 623.6 677.6 809.9
#> 4: 509.3 547.3 595.6 643.5 769.1
#> 5: 486.9 521.8 568.1 608.7 720.0
#> ---
#> 27333: 565.2 1040.7 2250.4 4164.5 21629.4
#> 27334: 595.6 1133.1 2540.4 4859.9 27634.1
#> 27335: 625.2 1237.4 2874.3 5686.8 35311.8
#> 27336: 657.2 1348.6 3248.3 6578.5 45076.0
#> 27337: 698.0 1465.8 3673.0 7695.7 57711.3
#>
#> $casesReportData
#> region date strat type median mean sd lower_90
#> 1: Afghanistan 2022-01-28 NA estimate 471.0 584.9 445.7 95.0
#> 2: Afghanistan 2022-01-29 NA estimate 310.0 383.6 293.3 65.9
#> 3: Afghanistan 2022-01-30 NA estimate 272.0 341.3 264.1 53.9
#> 4: Afghanistan 2022-01-31 NA estimate 531.0 648.2 503.4 104.9
#> 5: Afghanistan 2022-02-01 NA estimate 762.0 947.1 745.4 154.9
#> ---
#> 27333: Zimbabwe 2022-05-30 NA forecast 249.0 697.4 1978.0 24.0
#> 27334: Zimbabwe 2022-05-31 NA forecast 621.0 1881.6 6049.2 48.9
#> 27335: Zimbabwe 2022-06-01 NA forecast 912.5 3287.4 14667.6 74.0
#> 27336: Zimbabwe 2022-06-02 NA forecast 988.0 3773.4 20912.5 72.0
#> 27337: Zimbabwe 2022-06-03 NA forecast 1007.0 4865.4 25211.0 65.0
#> lower_50 lower_20 upper_20 upper_50 upper_90
#> 1: 269.0 388.0 575.0 778.2 1444.1
#> 2: 174.0 257.0 377.0 511.2 936.1
#> 3: 151.0 225.0 334.0 455.0 856.0
#> 4: 302.8 430.6 637.4 849.0 1604.2
#> 5: 417.8 617.0 926.4 1245.5 2402.5
#> ---
#> 27333: 97.0 175.0 362.0 617.2 2325.1
#> 27334: 239.0 432.0 888.0 1599.0 6425.6
#> 27335: 339.0 642.0 1343.4 2415.2 11189.9
#> 27336: 366.8 675.2 1444.4 2784.5 12872.2
#> 27337: 349.0 666.2 1501.4 2936.5 15063.3
#>
#> $obsCasesData
#> region date confirm
#> 1: Afghanistan 2022-01-28 301
#> 2: Albania 2022-01-28 1549
#> 3: Algeria 2022-01-28 2130
#> 4: American Samoa 2022-01-28 0
#> 5: Andorra 2022-01-28 0
#> ---
#> 26777: 2022-05-20 0
#> 26778: 2022-05-20 0
#> 26779: 2022-05-20 0
#> 26780: 2022-05-20 0
#> 26781: 2022-05-20 0
#>
france <- readInEpiNow2(
path = paste0(base_path, "master/national/cases/summary"),
region_var = "country",
regions = "France")
france
#> $summaryData
#> region New confirmed cases by infection date Expected change in daily cases
#> 1: France 19050 (8688 -- 36383) Likely decreasing
#> Effective reproduction no. Rate of growth
#> 1: 0.88 (0.61 -- 1.2) -0.033 (-0.11 -- 0.045)
#> Doubling/halving time (days)
#> 1: -21 (15 -- -6)
#>
#> $rtData
#> region date strat type median mean sd lower_90
#> 1: France 2022-01-28 NA estimate 0.7077474 0.7148735 0.07543965 0.6066662
#> 2: France 2022-01-29 NA estimate 0.7025683 0.7079581 0.06636947 0.6106171
#> 3: France 2022-01-30 NA estimate 0.6973103 0.7014724 0.05829336 0.6152980
#> 4: France 2022-01-31 NA estimate 0.6931728 0.6955274 0.05122777 0.6187650
#> 5: France 2022-02-01 NA estimate 0.6886802 0.6902387 0.04518645 0.6217061
#> ---
#> 123: France 2022-05-30 NA forecast 0.8839622 0.8864191 0.16852887 0.6103625
#> 124: France 2022-05-31 NA forecast 0.8839622 0.8864191 0.16852887 0.6103625
#> 125: France 2022-06-01 NA forecast 0.8839622 0.8864191 0.16852887 0.6103625
#> 126: France 2022-06-02 NA forecast 0.8839622 0.8864191 0.16852887 0.6103625
#> 127: France 2022-06-03 NA forecast 0.8839622 0.8864191 0.16852887 0.6103625
#> lower_50 lower_20 upper_20 upper_50 upper_90
#> 1: 0.6596165 0.6888232 0.7277506 0.7613219 0.8495634
#> 2: 0.6596907 0.6860281 0.7201484 0.7493823 0.8256539
#> 3: 0.6592762 0.6828454 0.7127718 0.7394193 0.8042868
#> 4: 0.6593623 0.6793969 0.7066325 0.7290580 0.7852724
#> 5: 0.6589813 0.6769954 0.7003684 0.7194838 0.7679247
#> ---
#> 123: 0.7837399 0.8449420 0.9183922 0.9873049 1.1671552
#> 124: 0.7837399 0.8449420 0.9183922 0.9873049 1.1671552
#> 125: 0.7837399 0.8449420 0.9183922 0.9873049 1.1671552
#> 126: 0.7837399 0.8449420 0.9183922 0.9873049 1.1671552
#> 127: 0.7837399 0.8449420 0.9183922 0.9873049 1.1671552
#>
#> $casesInfectionData
#> region date strat type median mean sd lower_90
#> 1: France 2022-01-28 NA estimate 244690.8 246467.2 22088.2 212785.0
#> 2: France 2022-01-29 NA estimate 228275.4 229748.0 20444.7 198445.2
#> 3: France 2022-01-30 NA estimate 210528.1 211779.7 18589.9 183080.9
#> 4: France 2022-01-31 NA estimate 193128.4 194145.8 16763.9 168089.2
#> 5: France 2022-02-01 NA estimate 176739.0 177438.2 15046.0 154020.5
#> ---
#> 123: France 2022-05-30 NA forecast 13844.2 19965.2 23273.5 2879.9
#> 124: France 2022-05-31 NA forecast 13435.8 20180.8 25771.5 2568.7
#> 125: France 2022-06-01 NA forecast 13024.9 20451.4 28609.8 2300.7
#> 126: France 2022-06-02 NA forecast 12614.2 20780.4 31845.9 2062.1
#> 127: France 2022-06-03 NA forecast 12227.6 21171.6 35547.1 1842.5
#> lower_50 lower_20 upper_20 upper_50 upper_90
#> 1: 231228.5 239374.2 249981.9 260081.2 286242.1
#> 2: 215545.7 223335.6 233208.9 242432.8 266466.4
#> 3: 198836.3 205890.1 215183.9 223492.7 244487.8
#> 4: 182323.7 189058.5 197136.0 205070.5 222990.7
#> 5: 166919.0 172942.1 180260.2 187488.2 202830.0
#> ---
#> 123: 8160.5 11458.7 16814.1 23886.4 54627.2
#> 124: 7654.3 10987.8 16469.2 23857.6 57019.4
#> 125: 7217.5 10548.5 16115.4 23675.4 59373.9
#> 126: 6802.6 10126.2 15774.2 23614.2 61896.0
#> 127: 6413.3 9712.8 15455.6 23555.0 64529.0
#>
#> $casesReportData
#> region date strat type median mean sd lower_90
#> 1: France 2022-01-28 NA estimate 301018.0 311799.3 94864.1 174257.5
#> 2: France 2022-01-29 NA estimate 305030.5 314555.0 94966.8 178146.7
#> 3: France 2022-01-30 NA estimate 315523.0 327937.1 98307.4 186613.8
#> 4: France 2022-01-31 NA estimate 254786.0 263554.6 78227.6 150345.1
#> 5: France 2022-02-01 NA estimate 54338.5 56027.3 15963.8 33212.9
#> ---
#> 123: France 2022-05-30 NA forecast 13913.0 16660.7 11594.8 4904.0
#> 124: France 2022-05-31 NA forecast 2911.5 3601.9 2756.2 936.9
#> 125: France 2022-06-01 NA forecast 22466.5 28271.4 23552.2 6857.6
#> 126: France 2022-06-02 NA forecast 18976.5 24938.5 22700.7 5516.4
#> 127: France 2022-06-03 NA forecast 17208.5 23551.3 24876.1 4659.9
#> lower_50 lower_20 upper_20 upper_50 upper_90
#> 1: 243424.2 278592.4 326289.4 368319.5 482151.1
#> 2: 247820.2 281947.2 327274.4 368742.0 483814.5
#> 3: 258085.0 292477.0 340605.4 386740.0 505144.8
#> 4: 207333.2 235181.2 276244.8 311721.0 403760.3
#> 5: 44681.8 50616.2 58543.8 65746.8 84670.2
#> ---
#> 123: 9275.8 11951.4 16028.0 20725.0 36983.1
#> 124: 1904.0 2489.0 3392.0 4396.8 8587.0
#> 125: 14379.2 19063.2 26182.4 34652.2 68376.1
#> 126: 12244.8 16249.4 22808.8 30318.2 61709.7
#> 127: 10542.8 14420.0 20836.8 28351.0 59546.5
#>
#> $obsCasesData
#> region date confirm
#> 1: France 2022-01-28 390453
#> 2: France 2022-01-29 337275
#> 3: France 2022-01-30 330747
#> 4: France 2022-01-31 240671
#> 5: France 2022-02-01 82378
#> ---
#> 109: France 2022-05-16 17301
#> 110: France 2022-05-17 5936
#> 111: France 2022-05-18 43727
#> 112: France 2022-05-19 29995
#> 113: France 2022-05-20 22962
#>