hamiltonSeirOver65: Hamilton's SEIR over 65 app

get_assumptions_text <- function() {
  p(HTML(paste0("<p> This visualisation is standard model of an epidemic where ",
                "an individuals at any point in time can be in one of four ",
                "states: S, susceptible to infection; E, exposed to the ",
                "infection, and so infectious, but asymptomatic; I, infectious ",
                "and symptomatic; R, recovered from the infection and immune to ",
                "further infection. It is known as an SEIR model<p> Exposed and ",
                "Infectious people are the main actors in the system. They ",
                "interact a random number of times each day with Susceptible, ",
                "Exposed, Infectious, and Recovered people. The probability ",
                "that a given interaction is with a Susceptible person is the ",
                "fraction of people in the population that are Susceptible at ",
                "that time. When they interact with a Susceptible person, the ",
                "Susceptible person moves to being Exposed. An interaction with ",
                "an Exposed, Infectious or Recovered person leads to no change ",
                "in the system. We have extended this model to allow for two ",
                "populations (here represented as under 65 or over 65) which ",
                "can mix together at a set rate, which we call the 'Cross R0'.",
                "<p> Exposed people stay in that state for a random amount of ",
                "time, with an average given by the model parameters, whereupon ",
                "they become Infectious. Infectious people stay in that state ",
                "for a random amount of time, with an average given by the ",
                "model parameters, whereupon they become Recovered. Once there ",
                "are no Exposed or Infectious people left, the epidemic has ",
                "ended.<p> As the system is stochastic, significant variability ",
                "occurs when the number of Exposed and Infectious people is ",
                "small. When started with a small number of Exposed and ",
                "Infectious people, there is a chance that the epidemic dies out ",
                "before it can get going, or that it expands into a full-blown ",
                "epidemic. Towards the end of a full blown epidemic, there is ",
                "significant heterogeneity in the time until it ends. The closer ",
                "the effective replicative value is to 1, the greater this ",
                "variability. We have suppressed this variability in these plots ",
                "but they are available in some of other apps.<p> The forecasts ",
                "produced by this system are inherently unrealistic. By creating ",
                "such a prediction and presenting it to you makes this forecast ",
                "less likely to happen. The government are likely to act, or ",
                "people will react by themselves if there are large numbers of ",
                "deaths.<p> The code presented here has been written by academics ",
                "and not by professional coders. It may contain bugs or other ",
                "mistakes which we have not disovered yet. All the code for this ",
                "app is available in our <a href = ",
                "'https://github.com/hamilton-institute/covid19ireland'>GitHub</a> ",
                "repository which we encourage you to look at and improve.")))
}