This simulated outbreak of Ebola Virus Disease matches some key properties of the West African Ebola outbreak of 2014-2015. Specifically, care was taken to use realistic delays (incubation period, serial interval, time to hospitalisation, etc.) and reproduction number (see references).
ebola_sim ebola_sim_clean
An object of class list of length 2.
An object of class list of length 2.
This dataset is used for teaching purposes during Imperial College's short course on infectious disease modelling. The exercise aims to simulate the response to an Ebola outbreak taking place in a single large city, and evaluate the impact of an intervention (increased bed capacity).
Note that to ensure realism, some errors have been introduced in this dataset. These can be
identified as negative incubation periods (delay from infection to onset of symptoms). See
example for a simple way to identify these cases. The dataset ebola_sim_clean is the same
dataset, only dates of infection and onset have been set to 'NA'.
WHO Ebola Response Team. 2014. Ebola virus disease in West Africa--the first 9 months of the epidemic and forward projections. The New England journal of medicine 371:1481–1495.
WHO Ebola Response Team, J. Agua-Agum, A. Ariyarajah, B. Aylward, I. M. Blake, R. Brennan, A. Cori, C. A. Donnelly, I. Dorigatti, C. Dye, T. Eckmanns, N. M. Ferguson, P. Formenty, C. Fraser, E. Garcia, T. Garske, W. Hinsley, D. Holmes, S. Hugonnet, S. Iyengar, T. Jombart, R. Krishnan, S. Meijers, H. L. Mills, Y. Mohamed, G. Nedjati-Gilani, E. Newton, P. Nouvellet, L. Pelletier, D. Perkins, S. Riley, M. Sagrado, J. Schnitzler, D. Schumacher, A. Shah, M. D. Van Kerkhove, O. Varsaneux, and N. Wijekoon Kannangarage. 2015. West African Ebola epidemic after one year--slowing but not yet under control. The New England journal of medicine 372:584–587.
Data simulated by Pierre Nouvellet (p.nouvellet@imperial.ac.uk). Transfer to R and documentation by Thibaut Jombart (thibautjombart@gmail.com).
## identify mistakes in data entry (negative incubation period) mistakes <- which(ebola_sim$linelist$date_of_onset <= ebola_sim$linelist$date_of_infection) mistakes#> [1] 46 63 110 219 326 364 385 469 694 708 721 728 748 769 919 #> [16] 998 1097 1119 1216 1267 1305 1395 1469 1514 1597 1663 1692 1744 1766 1808 #> [31] 1959 2133 2241 2315 2855 2859 2941 3306 3642 3659 3699 3704 3730 3754 3787 #> [46] 3946 3994 4203 4262 4273 4686 4772 4881 4916 4993 5031 5097 5311 5815ebola_sim$linelist[mistakes, ]#> case_id generation date_of_infection date_of_onset date_of_hospitalisation #> 46 3f1aaf 4 2014-05-18 2014-05-18 2014-05-25 #> 63 ce9c02 5 2014-05-27 2014-05-27 2014-05-29 #> 110 700e84 6 2014-06-10 2014-06-10 2014-06-16 #> 219 8bd1e8 8 2014-07-10 2014-07-10 2014-07-11 #> 326 085bd1 8 2014-07-14 2014-07-14 2014-07-27 #> 364 5dc1e2 6 2014-07-29 2014-07-29 2014-07-31 #> 385 9c1ae9 9 2014-08-01 2014-08-01 2014-08-02 #> 469 6be0c7 11 2014-08-05 2014-08-05 2014-08-08 #> 694 78e5b6 10 2014-08-14 2014-08-14 2014-08-24 #> 708 228e8b 11 2014-08-21 2014-08-21 2014-08-24 #> 721 899d6c 10 2014-08-20 2014-08-20 2014-08-25 #> 728 2be2fa 13 2014-08-22 2014-08-22 2014-08-25 #> 748 137b57 10 2014-08-23 2014-08-23 2014-08-26 #> 769 9f2e47 10 2014-08-23 2014-08-23 2014-08-27 #> 919 9bdd46 10 2014-08-30 2014-08-30 2014-09-03 #> 998 2816f0 13 2014-09-03 2014-09-03 2014-09-05 #> 1097 1c1e91 14 2014-09-05 2014-09-05 2014-09-09 #> 1119 f461ef 12 2014-08-31 2014-08-31 2014-09-10 #> 1216 1cc5e0 13 2014-09-11 2014-09-11 2014-09-12 #> 1267 dfe4f5 12 2014-09-09 2014-09-09 2014-09-14 #> 1305 aee2e3 13 2014-09-12 2014-09-12 2014-09-15 #> 1395 34f7ac 12 2014-09-15 2014-09-15 2014-09-17 #> 1469 f90b8b 12 2014-09-18 2014-09-18 2014-09-18 #> 1514 dc0798 16 2014-09-19 2014-09-19 2014-09-19 #> 1597 898509 14 2014-09-20 2014-09-20 2014-09-21 #> 1663 1306c1 17 2014-09-22 2014-09-22 2014-09-22 #> 1692 7e1951 14 2014-09-21 2014-09-21 2014-09-23 #> 1744 893b3a 20 2014-09-22 2014-09-22 2014-09-24 #> 1766 1d1c7d 15 2014-09-21 2014-09-21 2014-09-25 #> 1808 922899 11 2014-09-20 2014-09-20 2014-09-26 #> 1959 737799 17 2014-09-26 2014-09-26 2014-09-29 #> 2133 01e6cd 14 2014-10-02 2014-10-02 2014-10-02 #> 2241 e8a743 12 2014-10-04 2014-10-04 2014-10-05 #> 2315 eeecde 15 2014-10-06 2014-10-06 2014-10-06 #> 2855 4e8443 13 2014-10-19 2014-10-19 2014-10-20 #> 2859 5993d0 20 2014-10-19 2014-10-19 2014-10-20 #> 2941 657aa1 11 2014-10-20 2014-10-20 2014-10-23 #> 3306 b1b620 21 2014-11-01 2014-11-01 2014-11-01 #> 3642 baa1e5 19 2014-11-09 2014-11-09 2014-11-12 #> 3659 e41a89 17 2014-11-07 2014-11-07 2014-11-13 #> 3699 8d591b 16 2014-11-13 2014-11-13 2014-11-14 #> 3704 2cbe04 19 2014-11-13 2014-11-13 2014-11-14 #> 3730 f1551b 18 2014-11-14 2014-11-14 2014-11-15 #> 3754 8bd6eb 15 2014-11-15 2014-11-15 2014-11-16 #> 3787 00031d 18 2014-11-17 2014-11-17 2014-11-17 #> 3946 03d33a 20 2014-11-23 2014-11-23 2014-11-25 #> 3994 32af98 20 2014-11-25 2014-11-25 2014-11-26 #> 4204 9db989 21 2014-12-04 2014-12-04 2014-12-06 #> 4263 c1a29b 21 2014-12-06 2014-12-06 2014-12-09 #> 4274 ab2d3f 22 2014-12-08 2014-12-08 2014-12-09 #> 4687 7375d2 23 2015-01-01 2015-01-01 2015-01-02 #> 4773 b69258 19 2015-01-07 2015-01-07 2015-01-08 #> 4882 25e201 22 2015-01-13 2015-01-13 2015-01-16 #> 4917 1873c3 22 2015-01-17 2015-01-17 2015-01-18 #> 4994 ef70cd 21 2015-01-22 2015-01-22 2015-01-24 #> 5032 ca9ec0 26 2015-01-25 2015-01-25 2015-01-26 #> 5098 0ee1ae 20 2015-01-29 2015-01-29 2015-02-01 #> 5312 b51296 23 2015-02-19 2015-02-19 2015-02-20 #> 5816 87be9a 30 2015-04-18 2015-04-18 2015-04-19 #> date_of_outcome outcome gender #> 46 2014-05-25 <NA> f #> 63 2014-06-17 Death m #> 110 2014-06-28 <NA> m #> 219 2014-07-16 <NA> m #> 326 2014-07-30 <NA> m #> 364 2014-08-02 Death f #> 385 2014-08-23 <NA> f #> 469 <NA> Recover m #> 694 2014-08-27 <NA> f #> 708 2014-08-30 <NA> m #> 721 2014-08-26 <NA> f #> 728 <NA> <NA> m #> 748 2014-09-21 Recover m #> 769 2014-09-03 Recover f #> 919 2014-09-11 Recover m #> 998 2014-09-19 <NA> m #> 1097 2014-09-10 <NA> m #> 1119 2014-08-22 Recover m #> 1216 2014-09-22 Death f #> 1267 2014-09-26 Death m #> 1305 2014-09-27 Recover f #> 1395 2014-09-23 Recover f #> 1469 2014-09-13 Recover m #> 1514 2014-10-17 <NA> f #> 1597 2014-10-12 Recover f #> 1663 2014-09-29 Recover f #> 1692 2014-10-01 Recover m #> 1744 2014-10-04 <NA> m #> 1766 2014-10-02 Death m #> 1808 2014-10-01 Death m #> 1959 2014-10-11 Recover m #> 2133 2014-10-11 <NA> f #> 2241 2014-09-29 Death f #> 2315 2014-10-14 Death f #> 2855 2014-10-27 Recover m #> 2859 2014-11-01 <NA> m #> 2941 2014-11-06 Death m #> 3306 2014-12-08 Recover m #> 3642 2014-11-19 <NA> f #> 3659 <NA> Recover f #> 3699 <NA> <NA> f #> 3704 2014-11-15 Death f #> 3730 <NA> Recover f #> 3754 2014-12-07 Death f #> 3787 2014-11-24 Death f #> 3946 2014-12-13 Recover f #> 3994 2014-12-08 Recover m #> 4204 2014-12-12 Death m #> 4263 2014-12-13 Recover m #> 4274 2014-12-16 Death f #> 4687 2015-01-03 Death m #> 4773 <NA> Death m #> 4882 <NA> <NA> m #> 4917 2015-01-29 Death f #> 4994 2015-01-25 Death f #> 5032 2015-02-05 <NA> f #> 5098 2015-02-08 <NA> m #> 5312 2015-02-25 <NA> m #> 5816 2015-05-11 Recover f #> hospital lon lat #> 46 Connaught Hospital -13.24655 8.484859 #> 63 Connaught Hospital -13.22437 8.470248 #> 110 Connaught Hospital -13.26127 8.463422 #> 219 <NA> -13.24826 8.470268 #> 326 Connaught Hospital -13.25968 8.464691 #> 364 Military Hospital -13.24803 8.464861 #> 385 Rokupa Hospital -13.26582 8.458813 #> 469 other -13.21285 8.470534 #> 694 Connaught Hospital -13.23718 8.470879 #> 708 Connaught Hospital -13.22360 8.470980 #> 721 <NA> -13.23917 8.475379 #> 728 Connaught Hospital -13.26205 8.463343 #> 748 Connaught Hospital -13.26271 8.481300 #> 769 Connaught Hospital -13.21363 8.456702 #> 919 <NA> -13.22648 8.485700 #> 998 Connaught Hospital -13.21981 8.451784 #> 1097 other -13.26476 8.460112 #> 1119 Connaught Hospital -13.21816 8.479614 #> 1216 Connaught Hospital -13.21869 8.484936 #> 1267 other -13.23263 8.467949 #> 1305 Connaught Hospital -13.21060 8.479839 #> 1395 Connaught Hospital -13.20947 8.450530 #> 1469 Rokupa Hospital -13.26562 8.468240 #> 1514 Connaught Hospital -13.24065 8.479631 #> 1597 other -13.24704 8.465761 #> 1663 Connaught Hospital -13.22336 8.483234 #> 1692 other -13.21925 8.483758 #> 1744 Connaught Hospital -13.23310 8.466730 #> 1766 <NA> -13.21249 8.478603 #> 1808 <NA> -13.22741 8.473975 #> 1959 Mitylira Hospital -13.25798 8.483630 #> 2133 Military Hospital -13.21157 8.467951 #> 2241 Military Hospital -13.26574 8.449613 #> 2315 Connaught Hospital -13.26144 8.465503 #> 2855 other -13.26615 8.475470 #> 2859 Rokupa Hospital -13.25292 8.456837 #> 2941 Mitylira Hospital -13.26967 8.462541 #> 3306 Connaught Hospital -13.21515 8.487530 #> 3642 <NA> -13.22693 8.484813 #> 3659 Connaught Hospital -13.25436 8.456684 #> 3699 Military Hospital -13.25993 8.454327 #> 3704 Princess Christian Maternity Hospital (PCMH) -13.26583 8.462764 #> 3730 other -13.26714 8.476049 #> 3754 Military Hospital -13.22479 8.471030 #> 3787 <NA> -13.24831 8.486500 #> 3946 Connaught Hospital -13.23711 8.476159 #> 3994 Connaught Hospital -13.26369 8.479313 #> 4204 <NA> -13.21466 8.488438 #> 4263 other -13.25438 8.459362 #> 4274 Connaught Hospital -13.21589 8.462556 #> 4687 <NA> -13.25850 8.455848 #> 4773 Princess Christian Maternity Hospital (PCMH) -13.26536 8.472692 #> 4882 <NA> -13.25422 8.474586 #> 4917 <NA> -13.25600 8.481602 #> 4994 Connaught Hospital -13.22143 8.462763 #> 5032 <NA> -13.21703 8.461714 #> 5098 <NA> -13.26896 8.462674 #> 5312 Connaught Hospital -13.26148 8.465018 #> 5816 other -13.21062 8.451409## check that ebola_sim_clean is identical after removing mistakes identical(ebola_sim_clean$linelist, ebola_sim$linelist[-mistakes, ])#> [1] FALSE