as.data.frame() method for <multi_epiparameter> class
Source: R/coercion.R
as.data.frame.multi_epiparameter.Rdas.data.frame() method for <multi_epiparameter> class
Usage
# S3 method for class 'multi_epiparameter'
as.data.frame(x, ...)Arguments
- x
A
<multi_epiparameter>object.- ...
dots Not used, extra arguments supplied will cause a warning.
Details
The <data.frame> returned will contain some atomic columns (i.e. one
object per row), and other columns that are lists (i.e. multiple objects per
row). The list columns can contain lists or S3 objects (e.g. <bibentry>
object in the citation column).
Examples
db <- epiparameter_db()
#> Returning 125 results that match the criteria (100 are parameterised).
#> Use subset to filter by entry variables or single_epiparameter to return a single entry.
#> To retrieve the citation for each use the 'get_citation' function
as.data.frame(db)
#> disease pathogen
#> 1 Adenovirus Adenovirus
#> 2 Human Coronavirus Human_Cov
#> 3 SARS SARS-Cov-1
#> 4 Influenza Influenza-A
#> 5 Influenza Influenza-A
#> 6 Influenza Influenza-B
#> 7 Measles Measles Virus
#> 8 Parainfluenza Parainfluenza Virus
#> 9 RSV RSV
#> 10 Rhinovirus Rhinovirus
#> 11 Influenza Influenza-A
#> 12 Influenza Influenza-A
#> 13 RSV RSV
#> 14 RSV RSV
#> 15 Influenza Influenza-A-H1N1
#> 16 Influenza Influenza-A-H1N1
#> 17 Influenza Influenza-A-H7N9
#> 18 Influenza Influenza-A-H7N9
#> 19 Influenza Influenza-A-H7N9
#> 20 Influenza Influenza-A-H7N9
#> 21 Influenza Influenza-A-H7N9
#> 22 Influenza Influenza-A-H1N1
#> 23 Influenza Influenza-A-H1N1Pdm
#> 24 Influenza Influenza-A-H1N1Pdm
#> 25 Influenza Influenza-A-H1N1
#> 26 Influenza Influenza-A-H1N1
#> 27 Marburg Virus Disease Marburg Virus
#> 28 Marburg Virus Disease Marburg Virus
#> 29 Marburg Virus Disease Marburg Virus
#> 30 Marburg Virus Disease Marburg Virus
#> 31 Marburg Virus Disease Marburg Virus
#> 32 SARS SARS-Cov-1
#> 33 SARS SARS-Cov-1
#> 34 Smallpox Smallpox-Variola-Major
#> 35 Smallpox Smallpox-Variola-Major
#> 36 Smallpox Smallpox-Variola-Minor
#> 37 Smallpox Smallpox-Variola-Minor
#> 38 Mpox Monkeypox Virus
#> 39 Pneumonic Plague Yersinia Pestis
#> 40 Hantavirus Pulmonary Syndrome Hantavirus (Andes Virus)
#> 41 Ebola Virus Disease Ebola Virus
#> 42 Dengue Dengue Virus
#> 43 Dengue Dengue Virus
#> 44 Dengue Dengue Virus
#> 45 Zika Virus Disease Zika Virus
#> 46 Chikungunya Chikungunya Virus
#> 47 Dengue Dengue Virus
#> 48 Dengue Dengue Virus
#> 49 Japanese Encephalitis Japanese Encephalitis Virus
#> 50 Rift Valley Fever Rift Valley Fever Virus
#> 51 West Nile Fever West Nile Virus
#> 52 West Nile Fever West Nile Virus
#> 53 West Nile Fever West Nile Virus
#> 54 Yellow Fever Yellow Fever Viruses
#> 55 Yellow Fever Yellow Fever Viruses
#> 56 Mpox Mpox Virus
#> 57 Mpox Mpox Virus
#> 58 Mpox Mpox Virus
#> 59 Mpox Mpox Virus
#> 60 Mpox Mpox Virus
#> 61 Mpox Mpox Virus
#> 62 Mpox Mpox Virus
#> 63 Ebola Virus Disease Ebola Virus-Zaire Subtype
#> 64 Ebola Virus Disease Ebola Virus-Zaire Subtype
#> 65 Ebola Virus Disease Ebola Virus
#> 66 Ebola Virus Disease Ebola Virus
#> 67 Ebola Virus Disease Ebola Virus
#> 68 Ebola Virus Disease Ebola Virus
#> 69 Ebola Virus Disease Ebola Virus
#> 70 Ebola Virus Disease Ebola Virus
#> 71 Ebola Virus Disease Ebola Virus
#> 72 Ebola Virus Disease Ebola Virus
#> 73 Ebola Virus Disease Ebola Virus
#> 74 Ebola Virus Disease Ebola Virus
#> 75 Ebola Virus Disease Ebola Virus
#> 76 Ebola Virus Disease Ebola Virus
#> 77 Ebola Virus Disease Ebola Virus
#> 78 Ebola Virus Disease Ebola Virus
#> 79 MERS MERS-Cov
#> 80 MERS MERS-Cov
#> 81 MERS MERS-Cov
#> 82 MERS MERS-Cov
#> 83 MERS MERS-Cov
#> 84 MERS MERS-Cov
#> 85 MERS MERS-Cov
#> 86 MERS MERS-Cov
#> 87 COVID-19 SARS-CoV-2
#> 88 COVID-19 SARS-CoV-2
#> 89 COVID-19 SARS-CoV-2
#> 90 COVID-19 SARS-CoV-2
#> 91 COVID-19 SARS-CoV-2
#> 92 COVID-19 SARS-CoV-2
#> 93 COVID-19 SARS-CoV-2
#> 94 COVID-19 SARS-CoV-2
#> 95 COVID-19 SARS-CoV-2
#> 96 COVID-19 SARS-CoV-2
#> 97 COVID-19 SARS-CoV-2
#> 98 COVID-19 SARS-CoV-2
#> 99 COVID-19 SARS-CoV-2
#> 100 COVID-19 SARS-CoV-2
#> 101 COVID-19 SARS-CoV-2
#> 102 COVID-19 SARS-CoV-2
#> 103 COVID-19 SARS-CoV-2
#> 104 COVID-19 SARS-CoV-2
#> 105 COVID-19 SARS-CoV-2
#> 106 COVID-19 SARS-CoV-2
#> 107 COVID-19 SARS-CoV-2
#> 108 COVID-19 SARS-CoV-2
#> 109 COVID-19 SARS-CoV-2
#> 110 COVID-19 SARS-CoV-2
#> 111 COVID-19 SARS-CoV-2
#> 112 COVID-19 SARS-CoV-2
#> 113 COVID-19 SARS-CoV-2
#> 114 Mpox Mpox Virus
#> 115 Mpox Mpox Virus Clade I
#> 116 Mpox Mpox Virus
#> 117 Mpox Mpox Virus Clade I
#> 118 Mpox Mpox Virus Clade IIa
#> 119 Mpox Mpox Virus Clade IIb
#> 120 Mpox Mpox Virus
#> 121 Mpox Mpox Virus
#> 122 Mpox Mpox Virus
#> 123 Chikungunya Chikungunya Virus
#> 124 Chikungunya Chikungunya Virus
#> 125 Chikungunya Chikungunya Virus
#> epi_name prob_distribution uncertainty summary_stats
#> 1 incubation period lN(1.7, .... list(unc.... c(`25` =....
#> 2 incubation period lN(1.2, .... list(unc.... c(`25` =....
#> 3 incubation period lN(1.4, .... list(unc.... c(`5` = ....
#> 4 incubation period lN(0.34,.... list(unc.... c(`5` = ....
#> 5 incubation period lN(0.64,.... list(unc.... c(`5` = ....
#> 6 incubation period lN(-0.51.... list(unc.... c(`5` = ....
#> 7 incubation period lN(2.5, .... list(unc.... c(`5` = ....
#> 8 incubation period lN(0.96,.... list(unc.... c(`25` =....
#> 9 incubation period lN(1.5, .... list(unc.... c(`5` = ....
#> 10 incubation period lN(0.64,.... list(unc.... c(`5` = ....
#> 11 incubation period lN(0.38,.... list(unc.... c(`5` = ....
#> 12 incubation period lN(0.36,.... list(unc.... c(`5` = ....
#> 13 incubation period lN(1.5, .... list(unc.... c(`5` = ....
#> 14 incubation period lN(1.5, .... list(unc.... c(`5` = ....
#> 15 incubation period Γ(3.3, 2) list(ci_.... c(`95` =....
#> 16 incubation period Weibull(.... list(ci_.... c(`95` =....
#> 17 incubation period Weibull(.... list(unc.... 3.4, c(3....
#> 18 incubation period Γ(1.9, 0.41) list(unc.... 4.5, c(2....
#> 19 incubation period weibull list(ci_.... 3.5, c(3....
#> 20 incubation period Weibull(.... list(ci_.... 3.7, c(3....
#> 21 incubation period Weibull(.... list(ci_.... 3.3, c(2....
#> 22 incubation period lnorm list(ci_.... 4.3, c(2....
#> 23 incubation period Γ(18, 8.5) list(unc.... 2.05, 0.49
#> 24 serial interval Γ(2.6, 1) list(unc.... 2.51, 1.55
#> 25 incubation period lN(0.34,.... list(unc.... c(`5` = ....
#> 26 generation time Weibull(.... list(ci_.... c(`5` = ....
#> 27 incubation period NA list(ci_.... 2, 26
#> 28 incubation period NA list(ci_.... 7, 2, 13
#> 29 serial interval NA list(ci_.... c(`25` =....
#> 30 onset to death NA list(ci_.... 8, 2, 16
#> 31 serial interval Γ(2.8, 0.31) list(unc.... 9, c(8.2....
#> 32 offspring distribution NB(0.16,.... list(ci_....
#> 33 offspring distribution NB(0.17,.... list(ci_....
#> 34 offspring distribution NB(0.37,.... list(ci_....
#> 35 offspring distribution NB(0.32,.... list(ci_....
#> 36 offspring distribution NB(0.65,.... list(ci_....
#> 37 offspring distribution NB(0.72,.... list(ci_....
#> 38 offspring distribution NB(0.58,.... list(ci_....
#> 39 offspring distribution NB(1.4, .... list(ci_....
#> 40 offspring distribution NB(1.7, 0.7) list(ci_....
#> 41 offspring distribution NB(5.1, .... list(ci_....
#> 42 incubation period lN(2.6, .... list(unc.... 15, c(10....
#> 43 incubation period lN(1.8, .... list(unc.... 6.5, c(4....
#> 44 incubation period lN(1.8, .... list(ci_.... 5.97, c(....
#> 45 incubation period lN(1.8, .... list(unc.... c(`5` = ....
#> 46 incubation period lN(1.1, .... list(unc.... c(`25` =....
#> 47 incubation period lN(1.7, .... list(unc.... c(`25` =....
#> 48 incubation period lN(1.7, .... list(unc.... c(`25` =....
#> 49 incubation period lN(2.1, .... list(unc.... c(`25` =....
#> 50 incubation period lN(1.4, .... list(unc.... c(`25` =....
#> 51 incubation period lN(0.96,.... list(unc.... c(`5` = ....
#> 52 incubation period lN(1.1, .... list(unc.... c(`25` =....
#> 53 incubation period lN(2.4, .... list(unc.... c(`25` =....
#> 54 incubation period lN(1.5, .... list(unc.... c(`5` = ....
#> 55 incubation period lN(1.5, .... list(unc.... c(`5` = ....
#> 56 incubation period lN(2.1, .... list(unc.... 9, c(6.6....
#> 57 incubation period lN(2, 0.055) list(unc.... 7.6, c(6....
#> 58 incubation period Γ(2.4, 0.27) list(ci_.... 9.1, c(6....
#> 59 incubation period lN(1.8, .... list(ci_.... 7.5, c(6....
#> 60 incubation period lN(1.5, .... list(ci_.... 5.6, c(4....
#> 61 serial interval Γ(2.9, 0.34) list(ci_.... 8.5, c(7....
#> 62 serial interval Γ(2.8, 0.4) list(ci_.... 7, c(5.8....
#> 63 incubation period lN(2.5, .... list(unc.... 12.7, 4.31
#> 64 onset to death Γ(2.4, 0.3) list(ci_.... 9.3, c(6....
#> 65 incubation period Γ(1.6, 0.15) list(unc.... 10.3, c(....
#> 66 incubation period Γ(0.93, .... list(unc.... 12.6, c(....
#> 67 incubation period Γ(1.7, 0.17) list(unc.... 10, c(9.....
#> 68 incubation period Γ(1.5, 0.14) list(unc.... 10.4, c(....
#> 69 serial interval Γ(2.2, 0.15) list(unc.... 14.2, c(....
#> 70 serial interval Γ(4.9, 0.32) list(unc.... 15.5, c(....
#> 71 serial interval Γ(2.1, 0.14) list(unc.... 15.1, c(....
#> 72 serial interval Γ(1.9, 0.15) list(unc.... 12.4, c(....
#> 73 hospitalisation to death Γ(1.2, 0.27) list(unc.... 4.3, c(4....
#> 74 hospitalisation to discharge Γ(2.4, 0.22) list(unc.... 11.2, c(....
#> 75 notification to death Γ(0.49, .... list(unc.... 3.5, c(3....
#> 76 notification to discharge Γ(1.8, 0.16) list(unc.... 10.9, c(....
#> 77 onset to death Γ(1.6, 0.2) list(unc.... 8.2, c(7....
#> 78 onset to discharge Γ(2.9, 0.19) list(unc.... 15.1, c(....
#> 79 incubation period lN(1.7, .... list(unc.... c(`5` = ....
#> 80 serial interval lN(2, 0.32) list(unc.... c(`5` = ....
#> 81 onset to hospitalisation NA list(ci_.... 5, 1, 10
#> 82 onset to death NA list(ci_.... 11, 5, 27
#> 83 onset to ventilation NA list(ci_.... 7, 3, 11
#> 84 onset to death Γ(2, 0.13) list(unc.... 14.6, c(....
#> 85 incubation period gamma list(ci_.... 6.7, c(6....
#> 86 serial interval Γ(20, 1.6) list(unc.... 12.6, c(....
#> 87 incubation period NA list(ci_.... 5.84, c(....
#> 88 incubation period NA list(ci_.... 5.74, c(....
#> 89 incubation period NA list(ci_.... 6.5, c(5....
#> 90 serial interval NA list(ci_.... 5.2, c(4....
#> 91 serial interval lN(1.4, .... list(unc.... 4.7, c(3....
#> 92 serial interval Weibull(.... list(unc.... 4.8, c(3....
#> 93 incubation period Weibull(.... list(unc.... c(`2.5` ....
#> 94 serial interval N(4.6, 19) list(ci_.... c(`95` =....
#> 95 incubation period NA list(ci_.... 6.38, c(....
#> 96 incubation period Weibull(.... list(unc.... 6.4, c(4....
#> 97 incubation period lN(1.7, .... list(ci_....
#> 98 incubation period lN(1.6, .... list(ci_.... 5.8, c(5....
#> 99 incubation period lN(1.5, .... list(unc.... 5, c(4.2....
#> 100 incubation period lN(1.6, .... list(unc.... 5.6, c(5....
#> 101 onset to hospitalisation Γ(0.62, .... list(unc.... 3.3, c(2....
#> 102 onset to hospitalisation Γ(2.3, 0.35) list(unc.... 6.5, c(5....
#> 103 onset to death lN(2.6, .... list(unc.... 14.5, c(....
#> 104 hospitalisation to death Weibull(.... list(unc.... 8.9, c(7....
#> 105 incubation period lN(1.5, 0.4) list(unc.... 5.6, c(4....
#> 106 onset to hospitalisation lN(0.95,.... list(unc.... 9.7, c(5....
#> 107 onset to hospitalisation lN(1.7, .... list(unc.... 6.6, c(5....
#> 108 onset to death lN(2.9, .... list(unc.... 20.2, c(....
#> 109 hospitalisation to death lN(2.2, .... list(unc.... 13, c(8.....
#> 110 incubation period lN(1.6, .... list(unc.... 5.5, c(`....
#> 111 incubation period lN(1.7, .... list(unc.... c(`2.5` ....
#> 112 incubation period lN(1.7, .... list(unc.... c(`2.5` ....
#> 113 incubation period lN(1.6, .... list(unc.... c(`2.5` ....
#> 114 serial interval Γ(14, 2.5) list(unc.... 5.6, c(1....
#> 115 serial interval NA list(ci_.... c(`25` =....
#> 116 serial interval NA list(ci_.... c(`25` =....
#> 117 incubation period NA list(ci_.... c(`25` =....
#> 118 incubation period NA list(ci_.... c(`25` =....
#> 119 incubation period NA list(ci_.... 8.26, c(....
#> 120 incubation period NA list(ci_.... 8.13, c(....
#> 121 incubation period NA list(ci_.... 8.08, c(....
#> 122 incubation period NA list(ci_.... 8.23, c(....
#> 123 generation time NA list(ci_.... 14, 6.2
#> 124 generation time Γ(8.6, 0.69) list(unc.... 12.4, c(....
#> 125 case fatality risk NA list(ci_.... 1.3
#> citation metadata method_assess
#> 1 list(aut.... days, 14.... TRUE, FA....
#> 2 list(aut.... days, 13.... TRUE, FA....
#> 3 list(aut.... days, 15.... TRUE, FA....
#> 4 list(aut.... days, 15.... TRUE, FA....
#> 5 list(aut.... days, 90.... TRUE, FA....
#> 6 list(aut.... days, 78.... TRUE, FA....
#> 7 list(aut.... days, 55.... TRUE, FA....
#> 8 list(aut.... days, 11.... TRUE, FA....
#> 9 list(aut.... days, 24.... TRUE, FA....
#> 10 list(aut.... days, 28.... TRUE, FA....
#> 11 list(aut.... days, 15.... TRUE, FA....
#> 12 list(aut.... days, 15.... TRUE, FA....
#> 13 list(aut.... days, 24.... TRUE, FA....
#> 14 list(aut.... days, 24.... TRUE, FA....
#> 15 list(aut.... days, 72.... TRUE, FA....
#> 16 list(aut.... days, 72.... TRUE, FA....
#> 17 list(aut.... days, 22.... TRUE, FA....
#> 18 list(aut.... days, 22.... TRUE, FA....
#> 19 list(aut.... days, 39.... TRUE, FA....
#> 20 list(aut.... days, 17.... TRUE, FA....
#> 21 list(aut.... days, 22.... TRUE, FA....
#> 22 list(aut.... days, 31.... FALSE, F....
#> 23 list(aut.... days, 16.... TRUE, FA....
#> 24 list(aut.... days, 58.... TRUE, FA....
#> 25 list(aut.... days, 12.... TRUE, FA....
#> 26 list(aut.... days, 16.... TRUE, FA....
#> 27 list(aut.... days, 76.... FALSE, F....
#> 28 list(aut.... days, 18.... FALSE, F....
#> 29 list(aut.... days, 38.... FALSE, F....
#> 30 list(aut.... days, 77.... FALSE, F....
#> 31 list(aut.... days, 37.... FALSE, F....
#> 32 list(aut.... No units.... There is....
#> 33 list(aut.... No units.... There is....
#> 34 list(aut.... No units.... There is....
#> 35 list(aut.... No units.... There is....
#> 36 list(aut.... No units.... There is....
#> 37 list(aut.... No units.... There is....
#> 38 list(aut.... No units.... There is....
#> 39 list(aut.... No units.... There is....
#> 40 list(aut.... No units.... There is....
#> 41 list(aut.... No units.... There is....
#> 42 list(aut.... days, 14.... TRUE, FA....
#> 43 list(aut.... days, 14.... TRUE, FA....
#> 44 list(aut.... days, 15.... TRUE, FA....
#> 45 list(aut.... days, 25.... TRUE, FA....
#> 46 list(aut.... days, 21.... TRUE, FA....
#> 47 list(aut.... days, 16.... TRUE, FA....
#> 48 list(aut.... days, 12.... TRUE, FA....
#> 49 list(aut.... days, 6,.... TRUE, FA....
#> 50 list(aut.... days, 23.... TRUE, FA....
#> 51 list(aut.... days, 18.... TRUE, FA....
#> 52 list(aut.... days, 8,.... TRUE, FA....
#> 53 list(aut.... days, 6,.... TRUE, FA....
#> 54 list(aut.... days, 91.... TRUE, FA....
#> 55 list(aut.... days, 80.... TRUE, FA....
#> 56 list(aut.... days, 18.... FALSE, F....
#> 57 list(aut.... days, 22.... TRUE, FA....
#> 58 list(aut.... days, 30.... FALSE, F....
#> 59 list(aut.... days, 35.... FALSE, F....
#> 60 list(aut.... days, 36.... FALSE, F....
#> 61 list(aut.... days, 57.... FALSE, F....
#> 62 list(aut.... days, 40.... FALSE, F....
#> 63 list(aut.... days, 19.... FALSE, F....
#> 64 list(aut.... days, 14.... TRUE, FA....
#> 65 list(aut.... days, 17.... TRUE, FA....
#> 66 list(aut.... days, 49.... TRUE, FA....
#> 67 list(aut.... days, 95.... TRUE, FA....
#> 68 list(aut.... days, 79.... TRUE, FA....
#> 69 list(aut.... days, 30.... FALSE, F....
#> 70 list(aut.... days, 37.... FALSE, F....
#> 71 list(aut.... days, 14.... FALSE, F....
#> 72 list(aut.... days, 11.... FALSE, F....
#> 73 list(aut.... days, 11.... FALSE, F....
#> 74 list(aut.... days, 10.... FALSE, F....
#> 75 list(aut.... days, 25.... FALSE, F....
#> 76 list(aut.... days, 13.... FALSE, F....
#> 77 list(aut.... days, 27.... FALSE, F....
#> 78 list(aut.... days, 13.... FALSE, F....
#> 79 list(aut.... days, 23.... TRUE, FA....
#> 80 list(aut.... days, 23.... TRUE, FA....
#> 81 list(aut.... days, 23.... FALSE, F....
#> 82 list(aut.... days, 23.... FALSE, F....
#> 83 list(aut.... days, 23.... FALSE, F....
#> 84 list(aut.... days, 18.... FALSE, F....
#> 85 list(aut.... days, 16.... TRUE, FA....
#> 86 list(aut.... days, 99.... TRUE, FA....
#> 87 list(aut.... days, 59.... FALSE, F....
#> 88 list(aut.... days, 62.... FALSE, F....
#> 89 list(aut.... days, 14.... FALSE, F....
#> 90 list(aut.... days, 39.... FALSE, F....
#> 91 list(aut.... days, 28.... TRUE, TR....
#> 92 list(aut.... days, 18.... TRUE, TR....
#> 93 list(aut.... days, 17.... TRUE, FA....
#> 94 list(aut.... days, 13.... TRUE, FA....
#> 95 list(aut.... days, 28.... FALSE, F....
#> 96 list(aut.... days, 19.... TRUE, FA....
#> 97 list(aut.... days, 13.... FALSE, F....
#> 98 list(aut.... days, 12.... FALSE, F....
#> 99 list(aut.... days, 52.... TRUE, FA....
#> 100 list(aut.... days, 15.... TRUE, FA....
#> 101 list(aut.... days, 15.... TRUE, FA....
#> 102 list(aut.... days, 34.... TRUE, FA....
#> 103 list(aut.... days, 34.... TRUE, FA....
#> 104 list(aut.... days, 39.... TRUE, FA....
#> 105 list(aut.... days, 52.... TRUE, TR....
#> 106 list(aut.... days, 15.... TRUE, TR....
#> 107 list(aut.... days, 34.... TRUE, TR....
#> 108 list(aut.... days, 34.... TRUE, TR....
#> 109 list(aut.... days, 39.... TRUE, TR....
#> 110 list(aut.... days, 18.... TRUE, FA....
#> 111 list(aut.... days, 99.... TRUE, FA....
#> 112 list(aut.... days, 10.... TRUE, FA....
#> 113 list(aut.... days, 73.... TRUE, FA....
#> 114 list(aut.... days, 42.... FALSE, T....
#> 115 list(aut.... days, 16.... FALSE, F....
#> 116 list(aut.... days, 34.... FALSE, F....
#> 117 list(aut.... days, 16.... FALSE, F....
#> 118 list(aut.... days, 27.... FALSE, F....
#> 119 list(aut.... days, 11.... FALSE, F....
#> 120 list(aut.... days, NA.... FALSE, F....
#> 121 list(aut.... days, NA.... FALSE, F....
#> 122 list(aut.... days, NA.... FALSE, F....
#> 123 list(aut.... days, NA.... FALSE, F....
#> 124 list(aut.... days, 41.... FALSE, F....
#> 125 list(aut.... deaths p.... There is....
#> notes
#> 1 Analysis on data from Commission on Acute Respiratory Disease. Experimental transmission of minor respiratory illness to human volunteers by filter-passing agents. I. Demonstration of two types of illness characterized by long and short incubation periods and diff erent clinical features. J Clin Invest 1947; 26: 957–82.
#> 2 Analysis on data from Bradburne AF, Bynoe ML, Tyrrell DA. Eff ects of a “new” human respiratory virus in volunteers. Br Med J 1967; 3: 767–69.
#> 3 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets
#> 4 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets
#> 5 These estimates for the incubation period of influenza A from Lessler et al. 2009 are different from the estimates from the complete data, as they remove Henle et al. 1945 J Immunol, as it is an outlier in the dataset (n=61). Values found at the bottom Table 3.
#> 6 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets
#> 7 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets
#> 8 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets
#> 9 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets
#> 10 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets
#> 11 Data from Lessler et al 2009 using double interval-censored analysis. Not open source
#> 12 Data from Lessler et al 2009 using single interval-censored analysis. Not open source
#> 13 Data from Lessler et al 2009 using double interval-censored analysis. Not open source
#> 14 Data from Lessler et al 2009 using single interval-censored analysis. Not open source
#> 15 Gamma and weibull distributions had equally good fit to the data. This entry is the gamma distribution. Gamma, exponential. Not open source.
#> 16 Gamma and weibull distributions had equally good fit to the data. This entry is the weibull distribution. Weibull, exponential
#> 17 This study used an original data set and a modified data set. This weibull distribution was fitted to the modified data set and it is recommended to use this one.
#> 18 This study used an original data set and a modified data set. This gamma distribution was fitted to the original data set and it is recommended to use the weibull distribution that was fitted to the modified data set.
#> 19 This study fit the weibull distribution to estimate the parameters for the complete data set, those who had a fatal outcome and those with a non-fatal outcome. This is the distribution fit to the complete unpartitioned data.
#> 20 This study fit the weibull distribution to estimate the parameters for the complete data set, those who had a fatal outcome and those with a non-fatal outcome. This is the distribution fit to the fatal outcome data.
#> 21 This study fit the weibull distribution to estimate the parameters for the complete data set, those who had a fatal outcome and those with a non-fatal outcome. This is the distribution fit to the non-fatal outcome data.
#> 22 The mid-point of the exposure time was used to approximate an exact exposure time instead of interval-censoring. This can lead to a possible bias (overestimation) in incubation times. It was ambiguously reported whether the mean is the mean of the distribution or the meanlog parameter of the lognormal distribution.
#> 23 No additional notes
#> 24 No additional notes
#> 25 No additional notes.
#> 26 The parameters of the weibull are stated without reporting the uncertainty around them. The parameter estimates and sample size is reported in the supplementary appendix.
#> 27 This paper did not fit a distribution to the incubation period data and only reported a lower and upper range of the data. This is present in the database as there are no other studies that report the incubation period for Marburg virus. There is another incubation period reported from the same paper for a subset of the data which report the median and interquartile range but again do not fit a distribution to the data.
#> 28 This paper did not fit a distribution to the incubation period data and only reported a median and range for a subset of the data. This is present in the database as there are no other studies that report the incubation period for Marburg virus. This paper also reports the maximum and minimum for the complete data set.
#> 29 This paper did not fit a distribution to the serial interval data and only reported a median and interquartile range. This is present in the database as there are no other studies that report the serial interval for Marburg virus.
#> 30 This paper reports the median and range of the symptom onset to death delay but did not fit a parametric distribution to the data. This is included in the database as it is the only reported symptom onset to death reported in the literature
#> 31 The generation time is estimated from non-human viral load data. This paper reports the generation time but assumes the generation time and serial interval are the same it is classified as serial interval here based on Van Kerkove et al. 2015 <10.1038/sdata.2015.19>. The sample size is take from Van Kerkove et al. 2015.
#> 32 Parameter estimates are retrieved from the supplementary tables.
#> 33 No additional notes
#> 34 No additional notes
#> 35 No additional notes
#> 36 No additional notes
#> 37 Estimate of R0 taken from original study and CI of dispersion calculated mean of Z and proportion of zeros known
#> 38 In the model comparison the geometric model was the better fit to the monkeypox data, however, only the parameters of the negative binomial were reported as so are stored in the database.
#> 39 In the model comparison the geometric model was the better fit to the Pneumonic Plague data, however, only the parameters of the negative binomial were reported as so are stored in the database.
#> 40 In the model comparison the geometric model was the better fit to the Hantavirus data, however, only the parameters of the negative binomial were reported as so are stored in the database. The uncertainty for the dispersion parameter is currently not stored in the database as the upper bound for the confidence interval is infinite, and currently infinite values are not supported.
#> 41 In the model comparison the poisson model was the better fit to the Ebola data, however, only the parameters of the negative binomial were reported as so are stored in the database. The uncertainty for the dispersion parameter is currently not stored in the database as the upper bound for the confidence interval is infinite, and currently infinite values are not supported.
#> 42 Extrinsic incubation period for data at 25 degrees celcius
#> 43 Extrinsic incubation period for data at 30 degrees celcius
#> 44 Standard deviation, meanlog and sdlog is taken from Siraj et al. 2017 <10.1371/journal.pntd.0005797>
#> 45 Pooled analysis on several data sets, see Lessler et al. 2016 for references of datasets
#> 46 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets
#> 47 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets
#> 48 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. This is a subset of data containing only mosquito-transmitted infections
#> 49 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets
#> 50 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. Of the 18 samples at least 17 of them are not trasmitted by mosquitoes
#> 51 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets
#> 52 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. This is a subset of data containing only mosquito-transmitted infections
#> 53 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. This is a subset of data containing only tramsission by transplant or transfusion.
#> 54 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets
#> 55 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. This is a subset of data containing only mosquito-transmitted infections
#> 56 No additional notes
#> 57 Uses the methods described by Lessler (10.2471/BLT.16.174540) and Reich (10.1002/sim.3659). Estimated from time from exposure to first symptom onset
#> 58 No additional notes
#> 59 Meanlog, sdlog, and fitted distribution from supplementary material. Uses cases from Charniga 2022 + extra cases. Incubation period as exposure to rash onset.
#> 60 Meanlog, sdlog, and fitted distribution from supplementary material. Uses cases from Charniga 2022 + extra cases. Incubation period as exposure to symptom onset.
#> 61 Shape and scale from supp. material. Serial interval as exposure to symptom onset
#> 62 Shape and scale from supp. material. Serial interval as exposure to rash onset
#> 63 The paper reports lower and upper supported ranges for the mean and standard deviation but it is not clear if these are confidence intervals or not so are not included in the database
#> 64 Data extracted from Appendix. The mean, sd, shape and scale are taken from the paper, the conversion between the two does not match exactly. The data used to estimate the onset-to-death distribution is not from the DRC outbreak but from the west african outbreak.
#> 65 Data extracted from Appendix. This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100
#> 66 This data comes from the entire period of the Guinea ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100
#> 67 This data comes from the entire period of the Liberia ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100
#> 68 This data comes from the entire period of the Seirra Leone ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100
#> 69 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100
#> 70 This data comes from the entire period of the Guinea ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100
#> 71 This data comes from the entire period of the Liberia ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100
#> 72 This data comes from the entire period of the Sierra Leone ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100
#> 73 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100
#> 74 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100
#> 75 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100
#> 76 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100
#> 77 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100
#> 78 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100
#> 79 The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23.
#> 80 The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23.
#> 81 A distribution was not fitted to the data, instead the median and range observed are reported. The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23.
#> 82 A distribution was not fitted to the data, instead the median and range observed are reported. The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23.
#> 83 A distribution was not fitted to the data, instead the median and range observed are reported. The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23.
#> 84 The distribution parameters were jointly inferred with the risk factors of mortality.
#> 85 No additional notes
#> 86 No additional notes
#> 87 The estimate of the incubation period is from a non-parametric bootstrap approach that does not fit a parametric distribution.
#> 88 This estimated mean incubation period is from a meta-analysis of 15 other incubation period estimates. Only the mean is reported and a distribution cannot be specified as the meta-mean is estimated from a random-effects model.
#> 89 This estimated mean incubation period is from a meta-analysis of 14 other incubation period estimates. Only the mean is reported and a distribution cannot be specified as the meta-mean is estimated from a random-effects model.
#> 90 This estimated mean serial interval is from a meta-analysis of 23 other serial interval estimates. Only the mean is reported and a distribution cannot be specified as the meta-mean is estimated from a random-effects model.
#> 91 These estimates are from fitting to the entire dataset of contact pairs, including pairs that are uncertain.
#> 92 These estimates are from fitting to a subset of the dataset of contact pairs, only including pairs that are the most certain.
#> 93 No additional notes.
#> 94 No additional notes.
#> 95 This estimated mean incubation period is from a meta-analysis of 99 other incubation period estimates. Only the mean is reported and a distribution cannot be specified as the meta-mean is estimated from a random-effects model.
#> 96 No additional notes
#> 97 The incubation period parameters are estimated from a meta-analysis of other studies that estimated the incubation period using a lognormal distribution. This is the full set of data (N=9).
#> 98 The incubation period parameters are estimated from a meta-analysis of other studies that estimated the incubation period using a lognormal distribution. This is the data set with Backer removed as they did not have a defined exposure window (N=8).
#> 99 This dataset excludes Wuhan residents (to have a more precise exposure interval). This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions.
#> 100 This dataset includes Wuhan residents (which have a less precise exposure interval). This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions.
#> 101 This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions.
#> 102 This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions.
#> 103 This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions.
#> 104 This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions.
#> 105 This is excluding Wuhan residents from the dataset as this provides a more precise exposure interval. This method applies right-truncation but only fits a lognormal distribution.
#> 106 This dataset includes only surviving patients. This method applies right-truncation but only fits a lognormal distribution.
#> 107 This dataset includes only deceased patients. This method applies right-truncation but only fits a lognormal distribution.
#> 108 This method applies right-truncation but only fits a lognormal distribution.
#> 109 This method applies right-truncation but only fits a lognormal distribution.
#> 110 This is the complete data set.
#> 111 This is a subset of the data, including only those cases with a known onset of fever to be sure that the onset of symptoms is not from another pathogen.
#> 112 This is a subset of the data, including only cases that are detected outside of mainland China.
#> 113 This is a subset of the data, including only cases that are detected inside mainland China.
#> 114 Data from Kraemer et al 10.1016/S1473-3099(22)00359-0
#> 115 Systematic review
#> 116 Systematic review
#> 117 Systematic review
#> 118 Systematic review
#> 119 Systematic review
#> 120 SEIR model from 10.1016/j.mbs.2008.06.005 where the IP is assumed to follow a gamma distribution
#> 121 SEIR model from 10.1016/j.mbs.2008.06.005 where the IP is assumed to follow a gamma distribution
#> 122 SEIR model from 10.1016/j.mbs.2008.06.005 where the IP is assumed to follow a gamma distribution
#> 123 Database entry per communication K. Charniga, Z. Cucunubá & Laura Gomez Bermeo.
#> 124 No additional notes.
#> 125 Case fatality risk is given in deaths per 1,000 cases. It was calculated as a cumulative case fatality ratio. CFR is a population-wide estimate. Odds of chikungunya-related death were not significantly different between males and females. Odds of chikungunya-related death were significantly higher for 55-74 years old, and >75 years old compared to <18.