Summary

Row

confirmed

20,326

death

563 (2.8%)

recovered

0

active

19,763 (97.2%)

Row

Daily cumulative cases by type (Haiti only)

Comparison

Column

Daily New Confirmed Cases (Caribbean)

Cases distribution by type

Deaths Comparison

Column

Deaths distribution

Cluster Analysis

Cluster Analysis of Caribbean Coronavirus Time Series

  cluster 2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 2020-03-21
1       1  0.5309735  0.5309735  1.3274336  0.5309735  0.7964602  0.8849558
2       2  3.6312849  3.8175047  8.4729981  4.0968343  6.3314711  7.5418994
3       3  0.6374166  0.3805394  0.5486067  0.5581593  0.5930116  0.4249444
  2020-03-22 2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 2020-03-28
1  0.9734513  1.2389381  3.5398230  1.8584071   2.831858  1.5044248  2.3893805
2  9.0316574  8.7523277 12.9422719 13.1284916   4.003724 19.4599628 16.3873371
3  0.3805394  0.7166739  0.3805394  0.8594415   1.052808  0.3805394  0.4249444
  2020-03-29 2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04
1  1.9469027  3.2743363  1.7699115  2.9203540   2.743363  4.1592920  5.4867257
2  9.0316574 10.1489758  0.1862197 24.0223464   7.821229 12.0111732 14.5251397
3  0.8847411  0.6374166  0.9735511  0.4249444   1.690225  0.7610789  0.4693494
  2020-04-05 2020-04-06 2020-04-07 2020-04-08 2020-04-09 2020-04-10 2020-04-11
1   5.221239   4.424779  5.0442478   4.424779  5.1327434  3.6283186  4.3362832
2  22.253259  25.325885 13.0353818  19.459963 18.7150838 11.1731844 30.6331471
3   1.354090   1.097213  0.4693494   1.052808  0.6913742  0.3805394  0.4249444
  2020-04-12 2020-04-13 2020-04-14 2020-04-15 2020-04-16 2020-04-17 2020-04-18
1  4.3362832  5.4867257  5.6637168  4.4247788   4.690265   4.513274  4.6902655
2 13.2216015 34.6368715 19.5530726 32.2160149  26.536313   7.541899 23.9292365
3  0.5486067  0.9735511  0.9291461  0.5137543   9.396018   3.237682  0.6025643
  2020-04-19 2020-04-20 2020-04-21 2020-04-22 2020-04-23 2020-04-24 2020-04-25
1   4.159292   4.513274   4.690265  2.9203540   1.858407   4.336283   2.743363
2  22.718808  19.273743  16.573557 19.5530726  14.804469  11.545624  22.067039
3   4.017866   1.893144   8.615834  0.4693494   1.981954   4.246086   5.422556
  2020-04-26 2020-04-27 2020-04-28 2020-04-29 2020-04-30 2020-05-01 2020-05-02
1   3.097345   3.274336   6.637168  3.4513274   1.769912   1.592920   1.681416
2  29.888268  29.515829  27.094972 35.1024209  26.256983  22.905028  30.540037
3   3.291640   7.953117   2.565413  0.5137543   6.123483   4.626625   1.893144
  2020-05-03 2020-05-04 2020-05-05 2020-05-06 2020-05-07 2020-05-08 2020-05-09
1   2.389381   1.150442  1.2389381   1.150442   1.592920   1.946903   0.619469
2  26.908752  26.256983 47.2067039  43.389199  26.815642  24.860335  27.653631
3   6.665895   1.975760  0.7706315   1.925161   2.251743   1.893144   1.658731
  2020-05-10 2020-05-11 2020-05-12 2020-05-13 2020-05-14 2020-05-15 2020-05-16
1   1.858407  0.9734513   2.035398  0.9734513  0.8849558  0.6194690   1.238938
2  11.638734 39.1061452  34.636872 19.0875233 38.3612663 46.4618250  23.743017
3   2.112333  3.8722629   2.848113  4.9002940  3.9677896  0.3805394   3.346120
  2020-05-17 2020-05-18 2020-05-19 2020-05-20 2020-05-21 2020-05-22 2020-05-23
1  0.7964602  0.7964602   1.415929  0.9734513   0.619469   1.504425   1.061947
2 16.8528864 31.0055866  40.409683 35.3817505  25.418994  17.877095  42.830540
3  7.3487619  4.1802618   2.734003  0.3805394   5.854740  10.644283   8.376271
  2020-05-24 2020-05-25 2020-05-26 2020-05-27 2020-05-28 2020-05-29 2020-05-30
1  0.8849558   2.035398   1.858407   1.858407   3.451327  0.8849558   1.415929
2 32.2160149  43.202980  35.195531  35.195531  26.815642 16.8528864  26.908752
3  1.2208756  12.271520  13.362539  13.057898   5.582116  0.3805394  13.698673
  2020-05-31 2020-06-01 2020-06-02 2020-06-03 2020-06-04 2020-06-05 2020-06-06
1   1.150442   1.327434   3.628319   1.681416  0.8849558  0.5309735   0.619469
2  26.070764  36.312849  45.437616  37.802607 49.0689013 27.0018622  36.685289
3   7.126737   5.493306  15.291058  12.182710  9.4391540  5.8867561   6.835008
  2020-06-07 2020-06-08 2020-06-09 2020-06-10 2020-06-11 2020-06-12 2020-06-13
1  0.7964602  1.3274336  0.5309735  0.9734513   1.327434   1.061947  0.7079646
2 58.6592179 53.2588454 52.6070763 36.4059590  28.864060  38.733706 39.1061452
3  6.8192606  0.5486067 10.3272535  1.2208756  12.636312   5.927803  6.9777751
  2020-06-14 2020-06-15 2020-06-16 2020-06-17 2020-06-18 2020-06-19 2020-06-20
1   1.415929  0.9734513  0.4424779  0.3539823  0.3539823  0.3539823  0.1769912
2  50.372439 39.4785847 66.2011173 83.7988827 64.6182495 52.7932961 64.8044693
3  10.504873  3.3905249  5.1920235  0.2124722  7.0030748  7.2470409  7.2279356
  2020-06-21 2020-06-22 2020-06-23 2020-06-24 2020-06-25 2020-06-26 2020-06-27
1  0.2654867  0.4424779  0.5309735  0.2654867  0.7964602  0.1769912  0.7079646
2 47.5791434 58.1005587 79.7020484 70.2979516 41.3407821 70.1117318 76.3500931
3  6.1149753  8.4970969  3.6631491  1.0528083  8.4841859  3.0858844  3.4349299
  2020-06-28 2020-06-29 2020-06-30  2020-07-01 2020-07-02 2020-07-03
1  0.5309735  0.7964602  0.7964602   0.3539823  0.7964602   1.415929
2 75.5121043 88.6405959 96.5549348 115.6424581 65.5493482  28.212291
3  3.5934445  6.9491171  3.3905249   2.6165351  2.7401973   4.265713
   2020-07-04  2020-07-05  2020-07-06  2020-07-07 2020-07-08 2020-07-09
1   0.4424779   0.4424779   0.9734513   0.7079646   0.619469  0.2654867
2 107.9143389 112.0111732 104.8417132 111.7318436 132.122905 90.7821229
3   3.6187441   1.3889428   6.7017926   4.2943714   3.199994  1.2208756
  2020-07-10 2020-07-11 2020-07-12  2020-07-13  2020-07-14  2020-07-15
1  0.4424779   0.619469  0.2654867   0.4424779   0.1769912   0.1769912
2 74.4878957 127.281192 99.9068901 127.6536313 131.0055866 124.4878957
3  0.5486067   4.998657  6.2481902   1.5794737   3.8441273   0.7166739
   2020-07-16 2020-07-17 2020-07-18  2020-07-19  2020-07-20  2020-07-21
1   0.1769912  0.3539823   1.238938   0.4424779   0.3539823   0.8849558
2 102.6070764 78.3985102 116.108007 146.4618250 136.2197393 169.4599628
3   0.3805394  2.6356404   3.691807   2.5530248   5.6990612   5.0487335
  2020-07-22 2020-07-23 2020-07-24 2020-07-25  2020-07-26 2020-07-27 2020-07-28
1   1.592920   3.362832   2.123894   3.008850   0.8849558   1.061947   2.300885
2 187.430168 116.294227  49.813780 139.013035 161.4525140 161.545624 164.525140
3   1.179829   2.331000   1.052808   3.580533   2.7306447   1.585668   4.015031
  2020-07-29 2020-07-30 2020-07-31 2020-08-01 2020-08-02 2020-08-03 2020-08-04
1  1.2389381   2.212389   2.831858   2.300885   4.424779   4.867257   5.309735
2 77.1880819  81.471136 109.776536 127.188082  81.657356 109.310987  99.627561
3  0.5486067   2.289953   1.557010   4.030777   2.740197   2.816096   2.594071
  2020-08-05 2020-08-06 2020-08-07 2020-08-08 2020-08-09 2020-08-10 2020-08-11
1   5.840708   8.318584   4.247788   3.185841   4.159292   4.955752   5.663717
2  88.919926  71.508380  55.493482 105.307263  84.823091 126.163873  98.510242
3   3.754795   1.557010   5.920564   9.260489   4.588937   4.861561   2.489514
  2020-08-12 2020-08-13 2020-08-14 2020-08-15 2020-08-16 2020-08-17 2020-08-18
1   2.212389   4.336283   3.982301   6.637168   7.433628   1.592920   3.185841
2  71.229050  39.944134  36.033520  93.575419  82.309125  79.888268  64.711359
3   5.032987   4.036972   2.286595   3.304551   2.511978   6.266251   4.411317
  2020-08-19 2020-08-20 2020-08-21 2020-08-22 2020-08-23 2020-08-24 2020-08-25
1   5.840708   3.185841   2.477876   1.415929   4.247788    5.39823   5.309735
2  55.959032  41.713222  56.797020  31.750466  37.988827   39.75791  31.936685
3   4.490574   9.186904  16.771870  10.068287   1.668284   31.41282  14.739317
  2020-08-26 2020-08-27 2020-08-28 2020-08-29 2020-08-30 2020-08-31 2020-09-01
1   4.336283   5.309735    3.00885   5.486726   7.876106  4.6902655   2.920354
2  47.486034  44.227188   24.67412  60.428305  93.389199  0.1862197 200.000000
3  21.934713  12.979387   11.57134  25.153290  19.264743 42.3309999  17.799379
  2020-09-02 2020-09-03 2020-09-04 2020-09-05 2020-09-06 2020-09-07 2020-09-08
1   1.061947   3.893805   2.300885   7.345133   8.230088    3.80531    5.39823
2  51.955307  52.700186  21.787709  75.139665  72.625698   48.13780   80.16760
3   1.446259  38.570011  36.010792  11.996283  12.294730   14.73312   14.59035
  2020-09-09 2020-09-10 2020-09-11 2020-09-12 2020-09-13 2020-09-14 2020-09-15
1   2.831858    3.80531   6.902655  6.5486726   5.132743   6.371681    4.60177
2  52.979516   41.99255  64.618250 66.9459963  57.355680  55.586592   90.22346
3  24.407959   19.63857  14.026001  0.8689941  19.702076  27.040241   27.43936
  2020-09-16 2020-09-17 2020-09-18 2020-09-19 2020-09-20 2020-09-21 2020-09-22
1   3.274336   4.513274   7.256637   4.336283   3.628319   3.628319   5.575221
2  54.934823  46.089385  45.344507  43.668529  35.940410  44.320298  33.612663
3  19.375494  20.938699  35.906235  33.898981  32.617953  39.578414  27.994686
  2020-09-23 2020-09-24 2020-09-25 2020-09-26 2020-09-27 2020-09-28 2020-09-29
1   4.070796   2.389381   4.336283   5.929204   6.548673   4.336283   5.575221
2  40.037244  26.163873  21.880819  28.864060  48.417132  58.007449  53.724395
3  22.311895  21.220876  32.649447  24.056077  22.895354  28.095885  26.770452
  2020-09-30 2020-10-01 2020-10-02 2020-10-03 2020-10-04 2020-10-05 2020-10-06
1   2.654867   3.274336   3.451327   1.415929   1.769912   2.389381  0.5309735
2  51.675978  53.538175  29.608939  72.439479  67.504655  54.562384 51.9553073
3  40.567712  13.004687  12.969834  25.254489  16.217069  17.019195 20.1427676
  2020-10-07 2020-10-08 2020-10-09 2020-10-10 2020-10-11 2020-10-12 2020-10-13
1   2.743363   2.035398    1.59292   1.681416   2.477876   5.044248   4.690265
2  43.202980  34.171322   15.45624  60.986965  37.709497  35.847300  44.320298
3  17.491903  13.993985   14.21601  16.271027  33.908533  26.935161  17.555413
  2020-10-14 2020-10-15 2020-10-16 2020-10-17 2020-10-18 2020-10-19 2020-10-20
1   4.513274   3.451327   4.247788   5.663717   4.778761  5.2212389   4.955752
2  39.385475  29.888268  28.584730  39.664804  44.320298  0.1862197 106.703911
3  16.870233  14.022643  13.321716  11.270057  12.044047 13.8450229   8.111632
  2020-10-21 2020-10-22 2020-10-23 2020-10-24 2020-10-25 2020-10-26 2020-10-27
1   2.920354   2.654867   7.433628   4.424779   3.539823   3.185841   7.699115
2  47.486034  29.515829  15.456238  52.420857  32.029795  39.106145  63.966480
3   9.608490  12.145245  26.884562   6.865457   6.079078   7.607430   6.094825
  2020-10-28 2020-10-29 2020-10-30 2020-10-31 2020-11-01 2020-11-02 2020-11-03
1   4.336283   3.185841   5.840708   9.734513   3.628319   3.982301   3.539823
2  29.329609  24.208566  24.022346  40.130354  50.931099  44.413408  32.216015
3   6.599027  10.968775  12.985581  15.231130  15.570100   7.230249  21.388943
  2020-11-04 2020-11-05 2020-11-06 2020-11-07 2020-11-08 2020-11-09 2020-11-10
1   2.743363   4.690265   3.893805   3.362832   5.221239   4.867257   2.477876
2  50.093110  39.292365  49.255121  12.569832  34.636872  85.567970  62.569832
3   6.767094   5.698539   8.111632   9.786110   8.298804   6.281998   6.262892
  2020-11-11 2020-11-12 2020-11-13 2020-11-14 2020-11-15 2020-11-16 2020-11-17
1   2.035398   4.424779   2.566372   3.362832   1.946903   3.451327   3.185841
2  46.554935  44.692737  46.089385  42.923650  95.623836  56.052142  91.899441
3   5.422556   2.445109   9.253250  15.170455  10.547189  17.824679   7.908712
  2020-11-18 2020-11-19 2020-11-20 2020-11-21 2020-11-22 2020-11-23 2020-11-24
1   4.336283    3.00885   6.371681   6.814159   4.424779   3.185841   5.663717
2  59.683426   39.10615  26.350093  26.629423 142.178771  79.702048  81.657356
3   5.254489   10.74056  12.031136  11.644402  15.525695   7.962670  10.483678
  2020-11-25 2020-11-26 2020-11-27 2020-11-28 2020-11-29 2020-11-30 2020-12-01
1    5.39823    4.60177   8.672566   6.725664   6.725664   7.079646   9.292035
2   76.44320   48.04469  29.329609  83.426443  75.698324  62.569832  90.875233
3   13.48978   12.28182   9.208845  10.883323  12.608400   7.645641  10.309417
  2020-12-02 2020-12-03 2020-12-04 2020-12-05 2020-12-06 2020-12-07 2020-12-08
1   6.106195   11.06195   6.814159   11.06195   6.725664   7.699115   7.787611
2  74.394786   63.87337  45.903166   49.53445 145.344506  68.156425 105.586592
3   8.111632   10.57585   8.111632   14.53976  12.985581  11.168858  12.123304
  2020-12-09 2020-12-10 2020-12-11 2020-12-12 2020-12-13 2020-12-14 2020-12-15
1    6.19469    6.19469   8.584071   7.433628   8.938053   10.88496   11.68142
2  103.16574   45.90317  57.169460  73.463687  67.132216   58.19367  105.77281
3   14.83432   20.99034  13.049092  11.304909  18.183277   18.28783   15.14851
  2020-12-16 2020-12-17 2020-12-18 2020-12-19 2020-12-20 2020-12-21 2020-12-22
1    9.20354  10.265487   12.65487   10.35398   19.29204   16.28319   12.30088
2  123.18436  62.104283   81.84358   52.79330  107.91434  128.67784   84.35754
3   14.16205   9.542666   10.81981   13.65501   16.34693   15.17046   11.13684
  2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27 2020-12-28 2020-12-29
1   14.86726   20.35398   14.86726   7.699115   15.66372  17.168142  15.044248
2   76.81564   59.77654   80.16760 122.439479  112.38361 133.519553  69.646182
3   17.44235   12.29757   16.34693  15.142320   15.54480   7.477574   1.725077
  2020-12-30 2020-12-31 2021-01-01 2021-01-02 2021-01-03 2021-01-04 2021-01-05
1  17.699115  28.053097  19.911504   17.87611  27.876106   30.53097   32.38938
2  34.171322 190.316574  44.320298   49.34823 103.910614   97.39292  196.18250
3   6.326925   9.323477   5.926758   15.00239   2.901548   23.41918   27.78221
  2021-01-06 2021-01-07 2021-01-08 2021-01-09 2021-01-10 2021-01-11 2021-01-12
1  34.424779   38.23009  43.185841   48.76106  44.778761   48.49558   35.92920
2 135.940410  109.86965 140.316574  148.60335 102.886406  138.08194  220.76350
3   7.439363   15.70667   6.599027   12.50153   8.895174   22.68109   18.10118
  2021-01-13 2021-01-14 2021-01-15 2021-01-16 2021-01-17 2021-01-18 2021-01-19
1   57.61062   25.92920   29.29204   30.97345   36.19469   46.99115   50.26549
2  165.73557  172.16015  151.39665  142.73743  144.32030  137.24395  126.90875
3   27.36787   15.67466   13.95346   15.59592   20.17814   11.13684   22.40123
  2021-01-20 2021-01-21 2021-01-22 2021-01-23 2021-01-24 2021-01-25 2021-01-26
1   56.19469   50.26549   69.64602   73.09735   59.02655   58.40708   80.61947
2  134.07821  113.31471  106.51769  107.16946  107.63501  167.87709  199.44134
3   27.42414   11.64104   19.10444   18.36373   19.37214   23.34664   25.14090
  2021-01-27 2021-01-28 2021-01-29 2021-01-30 2021-01-31 2021-02-01 2021-02-02
1   89.64602   59.02655  58.407080   80.61947   89.64602   80.26549   92.47788
2  140.40968  107.63501 167.877095  199.44134  140.40968   95.62384   77.74674
3   27.15151   32.81751   9.901264   13.71233   24.63051   21.22088   35.51666
  2021-02-03 2021-02-04 2021-02-05 2021-02-06 2021-02-07 2021-02-08 2021-02-09
1   79.11504   72.30088   74.86726   72.74336   72.65487   57.87611   51.41593
2  126.44320  155.67970  137.70950  160.42831  116.48045   67.22533   39.10615
3   20.66040   30.84847   44.41415   58.26925   43.35231   36.01079   72.47287
  2021-02-10 2021-02-11 2021-02-12 2021-02-13 2021-02-14 2021-02-15 2021-02-16
1   76.01770   75.30973   72.92035   78.67257   71.41593   63.36283   83.00885
2   87.05773  115.73557   97.85847  105.40037   81.37803   74.11546   49.62756
3   37.71109   56.48350   50.06209   51.13684   40.30569   45.59062   81.04378
  2021-02-17 2021-02-18 2021-02-19 2021-02-20 2021-02-21 2021-02-22 2021-02-23
1   73.00885   81.76991   76.10619   83.00885   92.03540   74.24779   74.07080
2   59.86965   97.20670   76.25698   84.26443   89.47858   39.19926   30.63315
3   41.67448   49.62424   45.75869   59.99097   78.07536   51.68261   24.91835
  2021-02-24 2021-02-25 2021-02-26 2021-02-27 2021-02-28 2021-03-01 2021-03-02
1   61.94690   59.38053   77.52212   63.80531   54.77876   71.85841   88.31858
2   62.75605   69.55307   53.72439   74.95345   56.70391   35.56797   18.99441
3   34.55886   41.89314   34.49819   61.82732   75.17046   57.48235   42.37876
  2021-03-03 2021-03-04 2021-03-05 2021-03-06 2021-03-07 2021-03-08 2021-03-09
1   80.97345   71.50442   68.84956   66.46018    76.0177   77.34513   92.21239
2   53.35196   57.72812   64.80447   53.44507    54.7486   26.07076   23.55680
3   46.43768   57.74542   56.01079   88.78390   121.7251  147.77550   94.49819
  2021-03-10 2021-03-11 2021-03-12 2021-03-13 2021-03-14 2021-03-15 2021-03-16
1   68.40708   68.93805   67.52212   56.63717   80.97345   65.04425   70.17699
2   36.40596   70.39106   49.34823   15.36313   40.03724   23.74302   46.18250
3  138.58628  117.01919   72.32003   88.44777   98.86793  135.67466  102.80990
  2021-03-17 2021-03-18 2021-03-19 2021-03-20 2021-03-21 2021-03-22 2021-03-23
1   64.42478   61.06195   65.13274   72.03540   70.53097   63.62832   68.58407
2   53.16574   57.35568   48.78957   45.43762   44.22719   29.23650   37.43017
3  138.41769  108.19425  101.81389  117.55205  113.94339   93.99398   56.87023
  2021-03-24 2021-03-25 2021-03-26 2021-03-27 2021-03-28 2021-03-29 2021-03-30
1   65.22124   72.30088   73.71681   84.15929   81.41593   62.12389   89.29204
2   37.52328   36.31285   33.98510   23.37058   37.43017   18.62197   18.90130
3   74.43803   77.18726   55.85228   50.04440   82.97544   49.69958   58.77994
  2021-03-31 2021-04-01 2021-04-02 2021-04-03 2021-04-04 2021-04-05 2021-04-06
1   93.09735   89.73451   95.39823   91.15044  102.92035   94.42478   91.23894
2   32.02980   43.76164   54.56238   33.14711   14.89758   13.12849   15.73557
3   67.28578   53.15306   74.47480   83.32027   37.11337   60.56165   55.19324
  2021-04-07 2021-04-08 2021-04-09 2021-04-10 2021-04-11 2021-04-12 2021-04-13
1   85.13274   80.97345   90.08850   92.12389   84.95575   75.66372   93.89381
2   31.19181  101.95531   49.62756   58.10056   47.76536   31.84358   55.77281
3   12.63061   35.38923   42.00098   48.86875   65.90647   45.56165   28.64151
  2021-04-14 2021-04-15 2021-04-16 2021-04-17 2021-04-18 2021-04-19 2021-04-20
1   84.95575   88.93805   92.12389   90.88496   91.85841   93.89381  104.77876
2   58.10056   35.38175   46.08939   46.08939   46.83426   37.52328   29.60894
3   22.63061   32.37459   41.23293   36.99577   37.19942   42.63061   20.56165
  2021-04-21 2021-04-22 2021-04-23 2021-04-24 2021-04-25 2021-04-26 2021-04-27
1   89.11504  106.90265  109.91150   98.31858  104.95575   83.09735   95.92920
2   51.86220   49.53445   49.90689   44.22719   85.66108   50.37244    7.35568
3   14.52716   29.81962   24.35475   39.00992   24.45918   24.52716   13.58915
  2021-04-28 2021-04-29 2021-04-30 2021-05-01 2021-05-02 2021-05-03 2021-05-04
1   87.52212  101.76991   92.65487   81.06195   94.86726   82.56637   90.26549
2   31.56425   36.87151   32.40223   28.02607   55.40037   21.13594   36.31285
3   14.80921   21.25130   30.22447   34.52716   17.42145   29.87199   10.31328
  2021-05-05 2021-05-06 2021-05-07 2021-05-08 2021-05-09 2021-05-10 2021-05-11
1   89.46903   93.89381  102.92035   91.76991   94.69027   98.84956  94.867257
2   45.81006   58.10056   72.16015   59.86965   88.36127   52.23464  65.363128
3   19.28676   25.71048   15.73406   27.80303   20.90647   12.97544   7.607031
  2021-05-12 2021-05-13 2021-05-14 2021-05-15 2021-05-16 2021-05-17 2021-05-18
1  106.90265  105.04425  113.09735  122.47788  109.20354   93.62832  110.17699
2   64.15270   76.62942   98.69646   83.33333   85.38175   68.15642   58.75233
3   19.76205   18.35671   23.20788   18.30159   16.82060   16.46781   17.19146
  2021-05-19 2021-05-20 2021-05-21 2021-05-22 2021-05-23 2021-05-24 2021-05-25
1  118.58407   110.8850  106.01770  114.15929  108.14159  102.30088 114.336283
2   94.69274   133.1471   59.31099   98.32402   97.29981   78.11918  60.055866
3   19.71489    17.4582   19.30238   21.42372   15.49060   24.18938   4.871991
  2021-05-26 2021-05-27 2021-05-28 2021-05-29 2021-05-30 2021-05-31 2021-06-01
1 100.000000   97.61062  103.53982  105.22124   95.57522   97.43363   93.62832
2  98.603352  164.43203  116.85289  115.36313  128.95717   81.65736  115.08380
3   9.482146   13.83751   18.83751   19.08526   22.02395   19.86372   16.09879
  2021-06-02 2021-06-03 2021-06-04 2021-06-05 2021-06-06 2021-06-07 2021-06-08
1  105.48673   93.27434  100.00000  100.53097   96.28319  104.95575  102.38938
2  154.56238  134.07821  120.67039  118.06331  130.16760   84.45065   93.57542
3   14.45857   26.19281   21.02866   16.89625   15.89790   12.52802   28.91897
  2021-06-09 2021-06-10 2021-06-11 2021-06-12 2021-06-13 2021-06-14 2021-06-15
1  102.83186 102.566372  114.07080  121.50442 130.176991  119.46903 136.106195
2  134.17132 106.331471  105.21415  107.07635  74.860335   77.09497  85.195531
3   15.09616   3.665095   15.57696    9.35475   7.975439   35.24071   8.667238
  2021-06-16 2021-06-17 2021-06-18 2021-06-19 2021-06-20 2021-06-21 2021-06-22
1 124.247788  125.57522  131.15044 130.353982  127.16814  138.23009 131.858407
2 134.357542   91.52700  113.31471 166.201117   90.59590   79.98138  67.411546
3   8.837508   21.72322   10.73406   7.630612   10.73406   16.77283   4.182336
  2021-06-23 2021-06-24 2021-06-25 2021-06-26 2021-06-27 2021-06-28 2021-06-29
1  181.94690 166.460177  218.14159 212.743363 238.849558  229.20354  272.65487
2   82.58845  82.216015   87.33706  93.016760  65.921788   81.28492   66.57356
3   34.62914   8.147853   27.92675   9.699577   7.285784   19.72193   10.42782
  2021-06-30 2021-07-01 2021-07-02  2021-07-03  2021-07-04 2021-07-05
1  262.92035  261.32743  292.83186 307.6106195 311.5044248  272.21239
2   79.88827   90.59590  127.46741  75.9776536  52.7932961   54.93482
3   17.61652   11.72108   13.32027   0.2168188   0.2168188   38.61671
  2021-07-06 2021-07-07 2021-07-08 2021-07-09 2021-07-10 2021-07-11 2021-07-12
1 317.876106  324.33628 338.053097  568.40708  597.43363  612.74336 568.495575
2  66.480447   64.99069  82.495345   52.04842   62.38361   58.56611  80.446927
3   9.685797   12.06866   7.285784   14.58964   10.73406   15.90647   6.596129
  2021-07-13 2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18 2021-07-19
1 496.814159  538.14159  573.45133  571.76991  536.54867  555.75221  575.75221
2  49.906890   49.44134   49.90689   51.21043   42.36499   39.94413   15.27002
3   6.423715   16.53794   11.42372   12.28578   12.11337   14.52716   22.55405
  2021-07-20 2021-07-21 2021-07-22 2021-07-23  2021-07-24 2021-07-25 2021-07-26
1  536.10619  566.90265  685.48673  688.93805 684.3362832  783.53982  724.33628
2   22.16015   29.51583   56.98324   37.33706  34.2644320   35.84730   16.57356
3   11.61695   21.94096   26.16219   31.03908   0.2168188   51.59613   20.76254
  2021-07-27 2021-07-28 2021-07-29 2021-07-30 2021-07-31 2021-08-01 2021-08-02
1  699.46903  825.13274  761.76991  773.18584  785.48673  862.65487 821.238938
2   25.04655   29.51583   34.07821   30.91248   36.87151   33.79888  36.685289
3   13.66724   35.06492   35.01531   31.25130   36.59613   59.18234   3.769217
  2021-08-03 2021-08-04 2021-08-05
1  852.21239  828.67257  743.36283
2   17.78399   31.37803   30.44693
3   59.63619   16.76854   35.54327

Map

World map of cases, circles radius is on the log scale (use + and - icons to zoom in/out)

About

The Coronavirus Dashboard: the case of Haiti & Caribbean neighbours Cuba, Jamaica and the Dominican Republic

This Coronavirus dashboard: the case of Haiti compared to Caribbean neighbours Cuba, Jamaica and the Dominican Republic provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Haiti and the Caribbean. This dashboard is built with R using the R Markdown framework and was adapted from this dashboard by Rami Krispin. Our ambition is to refresh the data daily, however we rely on the [{coronavirus}] data being updated by Rami Krispin. The 14 Days Forecasts section will alternate between Caribbean countries every 4 Days. The Forecasts assume historical patterns that have been modelled will continue into the forecast period and does not take into account newly introduced measures to combat the pandemic. Although point forecasts are presented, for greater certainty refer to the uncertainty around the estimate as per the shaded area on the graphic. Because of space we present only a single model forecasts (ets), which often represents the worse case scenario.

Code

The code behind this dashboard is available on GitHub.

Data

The input data for this dashboard is the dataset available from the {coronavirus} R package. Make sure to download the development version of the package to have the latest data:

install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")

The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository.

Information

More information about this dashboard (and how to replicate it for your own country) can be found in this article.

Update

The data is as of Saturday August 07, 2021 and the dashboard has been updated on Sunday August 08, 2021.


Go back to statsandr.com (blog) or antoinesoetewey.com (personal website).

---
title: "Coronavirus Haiti Dashboard"
author: "Pat Stephenson"
date: "08/08/2021"
output: 


  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
    vertical_layout: fill
---




---
    
```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
data(coronavirus)
# View(coronavirus)
# max(coronavirus$date)
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "black"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(country == "Haiti") %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
   dplyr::mutate(unrecovered = confirmed - recovered - death , ifelse(is.na(death), 0, death)) %>%
 # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))
df_daily <- coronavirus %>%
  dplyr::filter(country == "Haiti") %>%
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(active = confirmed - death - recovered) %>%
  #dplyr::mutate(active = confirmed - death) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    recovered_cum = cumsum(recovered),
    active_cum = cumsum(active)
  )
df1 <- coronavirus %>% dplyr::filter(date == max(date))
```

Summary
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
  caption = "Total confirmed cases",
  icon = "fas fa-user-md",
  color = confirmed_color
)

```

















### death {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Death cases (death rate)",
  icon = "fas fa-heart-broken",
  color = death_color
)


```

### recovered {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$recovered), big.mark = ","), "", sep = " "),
  caption = "Total recovered",
  icon = "fas fa-user-md",
  color = recovered_color
)
```

### active {.value-box}

```{r}
valueBox(
    value = paste(format(sum(df$unrecovered, na.rm = TRUE), big.mark = ","), " (", 
                  round(100 * sum(df$unrecovered, na.rm = TRUE) / sum(df$confirmed), 1),"%)",
                  sep = "" ),
    caption = "Active cases (% of total cases)", 
     icon = "fas fa-ambulance",              
    color = active_color 
)
```


Row
-----------------------------------------------------------------------

### **Daily cumulative cases by type** (Haiti only)
    
```{r}
plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(
    x = ~date,
    # y = ~active_cum,
    y = ~confirmed_cum,
    type = "scatter",
    mode = "lines+markers",
    # name = "Active",
    name = "Confirmed",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Death",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  # plotly::add_annotations(
  #   x = as.Date("2020-02-04"),
  #   y = 1,
  #   text = paste("First case"),
  #   xref = "x",
  #   yref = "y",
  #   arrowhead = 5,
  #   arrowhead = 3,
  #   arrowsize = 1,
  #   showarrow = TRUE,
  #   ax = -10,
  #   ay = -90
  # ) %>%
 
  plotly::layout(
    title = "",
    yaxis = list(title = "Cumulative number of cases"),
    xaxis = list(title = "Date"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )

```

Comparison
=======================================================================


Column {data-width=400}
-------------------------------------

### **Daily New Confirmed Cases** (Caribbean)


```{r}
daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::filter(date >= "2020-02-29") %>%
  dplyr::mutate(country = country) %>%
  dplyr::group_by(date, country) %>%
  dplyr::summarise(total = sum(cases)) %>%
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total)
#----------------------------------------
# Plotting the data
daily_confirmed %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~Haiti,
    type = "scatter",
    mode = "lines+markers",
    name = "Haiti"
  ) %>%
  # plotly::add_trace(
  #   x = ~date,
  #   y = ~France,
  #   type = "scatter",
  #   mode = "lines+markers",
  #   name = "France"
  # ) %>%
  # plotly::add_trace(
  #   x = ~date,
  #   y = ~Spain,
  #   type = "scatter",
  #   mode = "lines+markers",
  #   name = "Spain"
  # ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~ `Dominican Republic`,
    type = "scatter",
    mode = "lines+markers",
    name = " Dominican Republic "
) %>%
    plotly::add_trace(
        x = ~date,
        y = ~Jamaica,
        type = "scatter",
        mode = "lines+markers",
        name = " Jamaica "
    ) %>%
    plotly::add_trace(
        x = ~date,
        y = ~Cuba,
        type = "scatter",
        mode = "lines+markers",
        name = " Cuba "
    ) %>%
   
        plotly::layout(
        title = "  ",
        legend = list(x = 0.1, y = 0.9),
        yaxis = list(title = "New confirmed cases"),
        xaxis = list(title = "Date"),
        # paper_bgcolor = "black",
        # plot_bgcolor = "black",
        # font = list(color = 'white'),
        hovermode = "compare",
        margin = list(
            # l = 60,
            # r = 40,
            b = 10,
            t = 10,
            pad = 2
        )
    )


```
 
### **Cases distribution by type**

```{r daily_summary}
df_EU <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(country == "Haiti" |
    country == "Dominican Republic" |
    country == "Jamaica" |
    country == "Cuba") %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
      dplyr::mutate(country = dplyr::if_else(country == "Canada", "Canada", country)) %>%
     
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))

plotly::plot_ly(
  data = df_EU,
  x = ~country,
  # y = ~unrecovered,
  y = ~ confirmed,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Total cases",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~death,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Death",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total cases"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )

```


Deaths Comparison
=======================================================================


Column {data-width=400}
-------------------------------------


 
### **Deaths distribution **


```{r deaths_summary}
df_EU <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(country == "Haiti" |
    country == "Dominican Republic" |
    country == "Jamaica" |
    country == "Cuba") %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
      dplyr::mutate(country = dplyr::if_else(country == "Canada", "Canada", country)) %>%
  
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))
plotly::plot_ly(
  data = df_EU,
  x = ~country,
  # y = ~unrecovered,
  y = ~ death,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Total deaths",
  marker = list(color = death_color)
) %>%
  
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total deaths"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )

```

 Cluster Analysis
=======================================================================

### **Cluster Analysis of Caribbean Coronavirus Time Series**

```{r Forecasts_summary}
  
   
 # CLUSTER ANALYSIS OF CARIBBEAN CORONAVIRUS TIME SERIES USING K-MEANS
library(tidyverse)
library(lubridate)
library(brotools)
library(tidyquant)

set.seed(123)
corona_Can <-readr::read_csv("00_data/coronatsCarib.csv")
corona_Can1 <- corona_Can %>%
    select(Country, date, New_Cases_N)
 

corona_wide <- corona_Can1 %>%
    pivot_wider(names_from = date, values_from = New_Cases_N)  %>%
    
    mutate_at(vars(-Country), as.numeric)
 

wss <- map_dbl(1:3, ~{kmeans(select(corona_wide, -Country), ., nstart=4,iter.max = 15 )$tot.withinss})
n_clust <- 1:3
#elbow_df <- as.data.frame(cbind("n_clust" = n_clust, "wss" = wss))
#ggplot(elbow_df) +
    #geom_line(aes(y = wss, x = n_clust), colour = "#82518c") +
    #theme_tq() 

clusters <- kmeans(select(corona_wide, -Country), centers = 3)
(centers <- rownames_to_column(as.data.frame(clusters$centers), "cluster"))

corona_wide <- corona_wide %>% 
    mutate(cluster = clusters$cluster)

corona_long <- corona_wide %>%
    pivot_longer(cols=c(-Country, -cluster), names_to = "date", values_to = "New_Cases_N") 
#corona_long  
centers_long <- centers %>%
    pivot_longer(cols = -cluster, names_to = "date", values_to = "New_Cases_N")   
#centers_long

ggplot() +
    geom_point(data = corona_long, aes(y = New_Cases_N, x = date, group = Country), colour = "#82518c") +
    facet_wrap(~cluster, nrow = 1) + 
    geom_point(data = centers_long, aes(y = New_Cases_N, x = date, group = cluster), col = "#b58900", size = 0.5) +
    theme_tq() + 
    
    labs(title = "Coronavirus Cases/Million Population for Caribbean countries 05/08/2021", 
         caption = "The different time series have been clustered using k-means.
                 Cluster 1: Cuba  \nCluster 2:Dominican R  \nCluster 3: Haiti & Jamaica ") +
    theme(plot.caption = element_text(colour = "black"))

   
```




Map
=======================================================================

### **World map of cases, circles radius is on the log scale** (*use + and - icons to zoom in/out*)

```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
  # dplyr::filter(country == "Haiti") %>%
  dplyr::filter(cases > 0) %>%
  dplyr::group_by(country, province, lat, long, type) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::mutate(log_cases = 2 * log(cases)) %>%
  dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
  purrr::walk(function(df) {
    map_object <<- map_object %>%
      addCircleMarkers(
        data = cv_data_for_plot.split[[df]],
        lng = ~long, lat = ~lat,
        #                 label=~as.character(cases),
        color = ~ pal(type),
        stroke = FALSE,
        fillOpacity = 0.8,
        radius = ~log_cases,
        popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
          feature.id = FALSE,
          row.numbers = FALSE,
          zcol = c("type", "cases", "country", "province")
        ),
        group = df,
        #                 clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
        labelOptions = labelOptions(
          noHide = F,
          direction = "auto"
        )
      )
  })
map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = FALSE)
  )
```





About
=======================================================================

**The Coronavirus Dashboard: the case of Haiti & Caribbean neighbours Cuba, Jamaica and the Dominican Republic**

This Coronavirus dashboard: the case of Haiti compared to Caribbean neighbours Cuba, Jamaica and the Dominican Republic provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Haiti and the Caribbean. This dashboard is built with R using the R Markdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin. Our ambition is to refresh the data daily, however we rely on the [`{coronavirus}`] data being updated by Rami Krispin. The 14 Days Forecasts section will alternate between Caribbean countries every 4 Days. The Forecasts assume historical patterns that have been modelled will continue into the forecast period and does not take into account newly introduced measures to combat the pandemic. Although point forecasts are presented, for greater certainty refer to the uncertainty around the estimate as per the shaded area on the graphic. Because of space we present only a single model forecasts (ets), which often represents the worse case scenario.

**Code**

The code behind this dashboard is available on [GitHub](https://github.com/AntoineSoetewey/coronavirus_dashboard){target="_blank"}.

**Data**

The input data for this dashboard is the dataset available from the [`{coronavirus}`](https://github.com/RamiKrispin/coronavirus){target="_blank"} R package. Make sure to download the development version of the package to have the latest data:

```
install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")
```

The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv){target="_blank"}.

**Information**

More information about this dashboard (and how to replicate it for your own country) can be found in this [article](https://statsandr.com/blog/how-to-create-a-simple-coronavirus-dashboard-specific-to-your-country-in-r/).

**Update**

The data is as of `r format(max(coronavirus$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`.


*Go back to [statsandr.com](https://statsandr.com/) (blog) or [antoinesoetewey.com](https://www.antoinesoetewey.com/) (personal website)*.