forked from mrc-ide/nimue
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathREADME.Rmd
115 lines (83 loc) · 4.45 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
---
output: github_document
---
<!-- 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%"
)
library(nimue)
library(dplyr)
library(ggplot2)
```
# nimue
<!-- badges: start -->
[](https://www.repostatus.org/#wip)
[](https://github.com/mrc-ide/nimue/actions)
[](https://codecov.io/gh/mrc-ide/nimue)
<!-- badges: end -->
## IMPORTANT NOTES
:warning: This code is released with no support. Please submit any questions or bugs as [issues](https://github.com/mrc-ide/nimue/issues) and we will try to address them as quickly as possible.
:warning: This model is in active development and so parameter name and behaviours, and output file formats will change without notice.
:warning: The model is stochastic. Multiple runs with different seeds should be undertaken to see average behaviour.
:warning: As with any mathematical model, it is easy to misconfigure inputs and therefore get meaningless outputs. Please contact the authors if you intend to publish results using `nimue`.
Nimue is built on the shoulders of [squire](https://mrc-ide.github.io/squire/) and [sircovid](https://mrc-ide.github.io/sircovid/).
Nimue is named after the [Lady of the Lake](https://en.wikipedia.org/wiki/Lady_of_the_Lake)
## Installation
You can install the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("mrc-ide/nimue")
```
## Overview
Nimue is an extension to the [squire package](https://mrc-ide.github.io/squire/) that allows vaccination to be included.
For detailed information on the base model structure and parameterisation please visit the [squire webpage](https://mrc-ide.github.io/squire/).
Nimue adds an additional dimension to the squire model, allowing us to track the vaccination status across the modelled population. This allows us to caetgorise
people into 4 groups:
1. Unvaccinated - those who have not yet received a vaccinate course.
2. Vaccinated but not protected - those who have received a vaccine course, but are not yet protected buy the vaccine.
3. Vaccinated and protected - those who have received a vaccine course and are protected.
4. Previously vaccinated - those who were previously protected by the vaccinate but are no longer so.
#### Schematic of the vaccination model structure
<img src="man/figures/structure.png" align="center" width="500" />
Nimue includes the flexibility to model different generic vaccine profiles, distribution and prioritisation approaches:
1. Vaccine mode of action: infection blocking and/or disease blocking.
2. Vaccine efficacy.
3. Age varying-efficacy.
4. Duration of vaccine-acquired immunity.
5. Age-dependent targeting and prioritisation strategies.
## Simple run
Running Nimue is very similar to squire, in the most simple case we can specify a country and run a scenario:
```{r}
run1 <- run(country = "France")
```
To help us visualise the run we can format the raw nimue model output, in this case selecting just the output for infections:
```{r, warning=FALSE}
# Format output, selecting a subset and naming the run.
output1 <- format(run1, summaries = "infections", compartments = NULL) %>%
mutate(Run = "Simple run")
# Plot
ggplot(output1, aes(x = t, y = value)) +
geom_line(col = "darkblue", size = 1) +
ylab("Infections") +
xlab("Time") +
theme_bw()
```
We can add in a very simple vaccination scenario, with a highly efficacious (95%), infection blocking vaccine that is distributed very quickly (500,000 individuals per day), and compare the results:
```{r, warning=FALSE}
run2 <- run(country = "France", max_vaccine = 500000, vaccine_efficacy_infection = rep(0.95, 17))
output2 <- format(run2, summaries = "infections", compartments = NULL) %>%
mutate(Run = "Efficacious vaccine")
ggplot(bind_rows(output1, output2), aes(x = t, y = value, col = Run)) +
geom_line(size = 1) +
ylab("Infections") +
xlab("Time") +
theme_bw()
```
To see demonstrations of full vaccine functionality please see the articles on:
1. Efficacy
2. Supply
3. Coverage and Prioritisation