## Building a Shiny app to show the impact of vaccines

Debates about vaccines are ongoing in many countries and the debate has reblossomed in Denmark after we’ve had five recent occurrences of measels. While that is nothing compared to the measles outbreak currently ravaging Japan it is still enough to worry the health authorities that it might result in an epidemic. Here we’ll use Shiny to create an app that shows the impact of contagious diseases and the influence of vaccination. Wrapping the computations in a Shiny app will allow non-R-users to tweak the input parameters themselves and observe the consequences of an outbreak. Hopefully, this can lead to a more informed discussion about vaccination.

## The SIR compartmental model

The **s**usceptible-**i**nfectious-**r**ecovered (SIR)
model
is one of the simplest compartmental models and it has previously been
successfully used to describe outbreaks of measles, the flu, small
pox, mumps, and rubella. The SIR model is easily extended
to accommodate immunities due to earlier infections or
vaccinations. Implementing the SIR model in R has previously been documented so in this post we will extend this previous work adding an additional
component to accommodate previously vaccinated individuals, and warp everything
in a Shiny app.

We will use a 4-compartment model where each individual in the population initially can be classified into 4 categories: susceptible (S), the infected (I), the recovered (R), and a group of previously vaccinated/immune (V) individuals. The number of individuals starting in compartment I will be very small since that is just the initial number of infected individuals, the initial number of persons in R will be zero since no one has yet had the disease and recovered from it as part of the *current* outbreak. The remaining individuals will be in either S (have not had the disease before) or V (vaccinated / immune from earlier infection). The setup is sketched in the diagram below. Adding the group V to the SIR model reduces the spread of the disease since the disease cannot infect individuals that it comes into contact with if they are already immune or vaccinated.

This variant of the SIR model is useful for modeling airborne diseases and in the model we disregard individuals who die from other causes than the disease, new vaccinations, and demographic changes in the population.1 Consequently, we are also assuming that the total population size is constant over the time period, and we will also assume that the transition rates \(\beta\) and \(\gamma\) are constant over the period, and that anyone in the population can get in contact with anyone else.

To compute the consequences of an outbreak we need to set some initial parameters for model. The parameters directly influencing the model are

- \(\beta\) - the transition rate from compartment \(S\) to \(I\). This rate is defined as the basic reproductive number, \(R0\), divided by the infection period (i.e., the average number of individuals each infected person will infect in an unprotected population divided by the number of days that the person can pass on the disease)
- \(\gamma\) - the transition rate from \(I\) to \(R\). This is equal to the inverse of the disease period since once the disease period is over, a person automatically transfers to the \(R\) group.

Consequently we need to allow the user to set the following

- The reproductive number \(R0\) - the average number of individuals that each infected person may infect, i.e., how contagious is the disease,
- the infection period,
- the population size,
- the number of individuals initially infected,
- the proportion of individuals in the immune/vaccine group \(V\). This percentage should be multiplied by the vaccine effectiveness if it is not 100%.
- the time frame to consider.

An example of these initial parameters is shown in the code below

```
# Set parameters
timeframe <- 200 # Look at development over 200 days
pinf <- 5 # Initial number of infected persons
popsize <- 5700000 # Population size (5.7 mio in Denmark)
pvac <- .90 # Proportion vaccinated
vaceff <- .95 # Effectiveness of vaccine
connum <- 15 # R0, the reproductive number. 15 is roughly measles
infper <- 14 # Infection period, 14 days
# Compute the proportions in each of the compartments at
# the initial outbreak
init <- c(S = 1 - pinf / popsize - pvac * vaceff,
I = pinf / popsize,
R = 0,
V = pvac * vaceff
)
# First set the parameters for beta and gamma
parameters <- c(beta = connum / infper,
gamma = 1 / infper)
## Time frame
times <- seq(0, timeframe, by = .2)
```

Wonderful!

Based on the model outlined above we can now set up the following set of coupled differential equations2 Without loss of generality we have divided by the total number of individuals in the population, \(N\), to obtain the *proportion* of individuals in each compartment.

\[\begin{array}{l} \frac{dS}{dt} = -\beta I(t)S(t) \\ \frac{dI}{dt} = \beta I(t)S(t) - \gamma I(t) = [\beta S(t) - \gamma] I(t)\\ \frac{dR}{dt} = \gamma I(t) \\ \frac{dV}{dt} = 0 \end{array}\]

If \(\beta S(t) - \gamma>0\) then the number of infected individuals will increase and an epidemic will ensure. If \(\beta S(t) - \gamma<0\) then the number of infected will decrease and the disease will die out by itself. Note that since \(S(t)\) decreases with time the outbreak will eventually die out by itself since the majority of the current population will at one point be in the recovered group, where they cannot get the disease again.

Now we are ready to define the coupled differential equations that describe the transitions between the compartments. This is formulated as a function that takes the time points, `time`

, the current `state`

(i.e., the distribution of the individuals in the 4 compartments), and the set of `parameters`

.

```
## Create a SIR function with an extra V component
sirv <- function(time, state, parameters) {
with(as.list(c(state, parameters)), {
dS <- -beta * S * I
dI <- beta * S * I - gamma * I
dR <- gamma * I
dV <- 0
return(list(c(dS, dI, dR, dV)))
})
}
```

Once we have the `sirv()`

function we can use the `ode()`

function from the `deSolve`

package to solve the coupled differential equations.

```
library("deSolve")
## Solve the set of coupled differential equations
out <- ode(y = init,
times = times,
func = sirv,
parms = parameters
)
head(as.data.frame(out))
```

```
time S I R V
1 0.0 0.1449991 8.771930e-07 0.000000e+00 0.855
2 0.2 0.1449991 8.920417e-07 1.263739e-08 0.855
3 0.4 0.1449991 9.071419e-07 2.548870e-08 0.855
4 0.6 0.1449990 9.224976e-07 3.855756e-08 0.855
5 0.8 0.1449990 9.381132e-07 5.184763e-08 0.855
6 1.0 0.1449990 9.539932e-07 6.536268e-08 0.855
```

The value for the basic reproductive number, \(R0\), depends on the type of disease and typical numbers can be seen in the table below.3 It is not easy to provide good estimates of the reproductive number, \(R0\), since it is difficult to gauge how many individuals a person has been in contact with and since we hardly have a population containing *only* susceptible individuals. Also, check out The failure of R0 by Li, Blakeley og Smith for other comments about \(R0\). A large value of \(R0\) means that the disease is highly contagious.

Disease | R0 |
---|---|

Measels | 12-18 |

Chickenpox | 10-12 |

Smallpox | 5-7 |

Rubella | 5-7 |

Mumps | 4-7 |

SARS | 2-5 |

Flu (Spanish flu) | 2-3 |

Now we have the full functionality and we just need to wrap it in a shiny app to make it easily accessible to anyone and to allow non-R-users to try out different parameter values and see their consequences. One way to see the results is to plot the proportion of the population in each of the four compartments:

```
library("ggplot2")
library("tidyverse")
ldata <- as.data.frame(out) %>% gather(key, value, -time)
head(ldata)
```

```
time key value
1 0.0 S 0.1449991
2 0.2 S 0.1449991
3 0.4 S 0.1449991
4 0.6 S 0.1449990
5 0.8 S 0.1449990
6 1.0 S 0.1449990
```

```
ggplot(data=ldata,
aes(x = time,
y = value,
group = key,
col = key
)) +
ylab("Proportion of full population") + xlab("Time (days)") +
geom_line(size = 2) +
scale_colour_manual(values = c("red", "green4", "black", "blue")) +
scale_y_continuous(labels = scales::percent, limits = c(0, 1))
```

The graph shows the result of the model and the distribution of individuals in the four groups over time. The interesting part is how large (and how quickly) a drop in the proportion of the susceptible that is observed. The blue line shows the actual proportion of vaccinated / previously immune individuals and it will be constant in this model. The new individuals in the recovered group will be added to the immune group by the time of the next outbreak.4 The reason for why the recovered and vaccinated groups are kept separate is that it might be interesting to add a societal cost from the current outbreak to end up in the recovered group so we need to be able to distinguish the two groups for this outbreak. The black curve shows how the susceptible individuals are moved to the infected (and subsequently to the recovered) group.

It is also possible to compute simple statistics that show the impact of the outbreak.

```
# Proportion of full population that got the
# disease by end of time frame
ldata %>% filter(time == max(time), key=="R") %>%
mutate(prop = round(100 * value, 2))
```

```
time key value prop
1 200 R 0.1137451 11.37
```

Here, 11.37% of the population will have had the disease as part of this outbreak. We can also compute the proportion of the susceptible, that have had the disease at the end of the time frame.

```
# Proportion of susceptible that will get
# the disease by end of time frame
as.data.frame(out) %>% filter(row_number() == n()) %>%
mutate(res = round(100*(R + I) / (S + I + R), 2)) %>% pull(res)
```

`[1] 81.84`

## What does it take to prevent an epidemic?

\(R0\) denotes the number of persons that an infected person will infect. If this number is less that 1 then the outbreak will die out by itself and if \(R0>1\) then an epidemic starts. Herd immunity is the percentage of vaccinated/immune that is necessary to keep the effective \(R0\) less than one from the initial outbreak. We can comp