generated from epiverse-trace/packagetemplate
-
Notifications
You must be signed in to change notification settings - Fork 2
/
README.Rmd
170 lines (124 loc) · 7.88 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
---
output: github_document
bibliography: vignettes/references.json
link-citations: true
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# _superspreading_: Estimate individual-level variation in transmission <img src="man/figures/logo.svg" align="right" width="120"/>
<!-- badges: start -->
[](https://opensource.org/license/mit/)
[](https://github.com/epiverse-trace/superspreading/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/epiverse-trace/superspreading?branch=main)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
<!-- badges: end -->
`{superspreading}` is an R package that provides a set of functions to estimate
and understand individual-level variation the in transmission of infectious diseases
from data on secondary cases.
`{superspreading}` implements methods outlined in @lloyd-smithSuperspreadingEffectIndividual2005, @kucharskiEarlyDynamicsTransmission2020, and @kremerQuantifyingSuperspreadingCOVID192021, as well as additional functions.
`{superspreading}` is developed at the [Centre for the Mathematical Modelling of Infectious Diseases](https://www.lshtm.ac.uk/research/centres/centre-mathematical-modelling-infectious-diseases) at the [London School of Hygiene and Tropical Medicine](https://www.lshtm.ac.uk/) as part of [Epiverse-TRACE](https://data.org/initiatives/epiverse/).
## Installation
The easiest way to install the development version of `{superspreading}` from
[GitHub](https://github.com/) is to use the `{pak}` package:
``` r
# check whether {pak} is installed
if(!require("pak")) install.packages("pak")
pak::pak("epiverse-trace/superspreading")
```
## Quick start
```{r load-superspreading}
library(superspreading)
```
### Calculate the heterogeneity of transmission
Case study using data from early Ebola outbreak in Guinea in 2014, stratified by index and non-index cases, as in @kucharskiEffectivenessRingVaccination2016. Data on transmission from index and secondary cases for Ebola in 2014.
***Source***: @fayeChainsTransmissionControl2015 & @althausEbolaSuperspreading2015.
[`{fitdistrplus}`](https://CRAN.R-project.org/package=fitdistrplus) is a well-developed and stable R package that provides a variety of methods for fitting distribution models to data [@delignette-mullerFitdistrplusPackageFitting2015]. Therefore, it is used throughout the documentation of `{superspreading}` and is a recommended package for those wanting to fit distribution models, for example those supplied in `{superspreading}` (Poisson-lognormal and Poisson-Weibull). We recommend reading the `{fitdistrplus}` documentation (specifically `?fitdist`) to explore the full range of functionality.
In this example we fit the negative binomial distribution to estimate the reproduction number ($R$, which is the mean of the distribution) and the dispersion ($k$, which a measure of the variance of the distribution). The parameters are estimated via maximum likelihood (the default method for `fitdist()`).
```{r, estim-superspreading}
# we use {fitdistrplus} to fit the models
library(fitdistrplus)
# transmission events from index cases
index_case_transmission <- c(2, 17, 5, 1, 8, 2, 14)
# transmission events from secondary cases
secondary_case_transmission <- c(
1, 2, 1, 4, 4, 1, 3, 3, 1, 1, 4, 9, 9, 1, 2, 1, 1, 1, 4, 3, 3, 4, 2, 5,
1, 2, 2, 1, 9, 1, 3, 1, 2, 1, 1, 2
)
# Format data into index and non-index cases
# total non-index cases
n_non_index <- sum(c(index_case_transmission, secondary_case_transmission))
# transmission from all non-index cases
non_index_cases <- c(
secondary_case_transmission,
rep(0, n_non_index - length(secondary_case_transmission))
)
# Estimate R and k for index and non-index cases
param_index <- fitdist(data = index_case_transmission, distr = "nbinom")
# rename size and mu to k and R
names(param_index$estimate) <- c("k", "R")
param_index$estimate
param_non_index <- fitdist(data = non_index_cases, distr = "nbinom")
# rename size and mu to k and R
names(param_non_index$estimate) <- c("k", "R")
param_non_index$estimate
```
The reproduction number ($R$) is higher for index cases than for non-index cases, but the heterogeneity in transmission is higher for non-index cases (i.e. $k$ is lower).
### Calculate the probability of a large epidemic
Given the reproduction number ($R$) and the dispersion ($k$), the probability that
a infectious disease will cause an epidemic, in other words the probability it
does not go extinct, can be calculated using `probability_epidemic()`. Here we
use `probability_epidemic()` for the parameters estimated in the above section
for Ebola, assuming there are three initial infections seeding the potential outbreak.
```{r, prob-epidemic}
# Compare probability of a large outbreak when k varies according to
# index/non-index values, assuming 3 initial spillover infections
initial_infections <- 3
# Probability of an epidemic using k estimated from index cases
probability_epidemic(
R = param_index$estimate[["R"]],
k = param_index$estimate[["k"]],
num_init_infect = initial_infections
)
# Probability of an epidemic using k estimated from non-index cases
probability_epidemic(
R = param_non_index$estimate[["R"]],
k = param_non_index$estimate[["k"]],
num_init_infect = initial_infections
)
```
The probability of causing a sustained outbreak is high for the index cases, but
is zero for non-index cases (i.e. disease transmission will inevitably cease assuming transmission
dynamics do not change).
## Package vignettes
More details on how to use `{superspreading}` can be found in the [online documentation as package vignettes](https://epiverse-trace.github.io/superspreading/), under "Articles".
### Visualisation and plotting functionality
`{superspreading}` does not provide plotting functions, instead we provide example code chunks in the package's vignettes that can be used as a templates upon which data visualisations can be modified. We recommend users copy and edit the examples for their own purposes. (This is permitted under the package's MIT license). By default code chunks for plotting are folded, in order to unfold them and see the code simply click the code button at the top left of the plot.
## Help
To report a bug please open an [issue](https://github.com/epiverse-trace/superspreading/issues/new/choose)
## Contribute
Contributions to `{superspreading}` are welcomed. Please follow the [package contributing guide](https://github.com/epiverse-trace/.github/blob/main/CONTRIBUTING.md).
## Code of Conduct
Please note that the {superspreading} project is released with a
[Contributor Code of Conduct](https://github.com/epiverse-trace/.github/blob/main/CODE_OF_CONDUCT.md).
By contributing to this project, you agree to abide by its terms.
## Citing this package
```{r message=FALSE, warning=FALSE}
citation("superspreading")
```
## Related projects
This project has some overlap with other R packages:
- [`{bpmodels}`](https://github.com/epiverse-trace/bpmodels) is another Epiverse-TRACE
R package that analyses transmission chain data to infer the transmission process for either
the size or length of transmission chains. Two main differences between the packages
are: 1) `{superspreading}` has more functions to compute metrics that characterise
outbreaks and superspreading events (e.g. `probability_epidemic()` &
`probability_extinct()`); 2) `{bpmodels}` can simulate a branching process
(`chain_sim()`) with a specified process (e.g. negative binomial).
## References