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Create a SISe model

Source: R/SISe.R
SISe.Rd

Create an ‘SISe’ model to be used by the simulation framework.

Usage

SISe(
  u0,
  tspan,
  events = NULL,
  phi = NULL,
  upsilon = NULL,
  gamma = NULL,
  alpha = NULL,
  beta_t1 = NULL,
  beta_t2 = NULL,
  beta_t3 = NULL,
  beta_t4 = NULL,
  end_t1 = NULL,
  end_t2 = NULL,
  end_t3 = NULL,
  end_t4 = NULL,
  epsilon = NULL
)

Arguments

u0

A data.frame with the initial state in each node, i.e., the number of individuals in each compartment in each node when the simulation starts (see ‘Details’). The parameter u0 can also be an object that can be coerced to a data.frame, e.g., a named numeric vector will be coerced to a one row data.frame.

tspan

A vector (length >= 1) of increasing time points where the state of each node is to be returned. Can be either an integer or a Date vector. A Date vector is coerced to a numeric vector as days, where tspan[1] becomes the day of the year of the first year of tspan. The dates are added as names to the numeric vector.

events

a data.frame with the scheduled events, see SimInf_model.

phi

A numeric vector with the initial environmental infectious pressure in each node. Will be repeated to the length of nrow(u0). Default is NULL which gives 0 in each node.

upsilon

Indirect transmission rate of the environmental infectious pressure

gamma

The recovery rate from infected to susceptible

alpha

Shed rate from infected individuals

beta_t1

The decay of the environmental infectious pressure in interval 1.

beta_t2

The decay of the environmental infectious pressure in interval 2.

beta_t3

The decay of the environmental infectious pressure in interval 3.

beta_t4

The decay of the environmental infectious pressure in interval 4.

end_t1

vector with the non-inclusive day of the year that ends interval 1 in each node. Will be repeated to the length of nrow(u0).

end_t2

vector with the non-inclusive day of the year that ends interval 2 in each node. Will be repeated to the length of nrow(u0).

end_t3

vector with the non-inclusive day of the year that ends interval 3 in each node. Will be repeated to the length of nrow(u0).

end_t4

vector with the non-inclusive day of the year that ends interval 4 in each node. Will be repeated to the length of nrow(u0).

epsilon

The background environmental infectious pressure

Value

SISe

Details

The ‘SISe’ model contains two compartments; number of susceptible (S) and number of infectious (I). Additionally, it contains an environmental compartment to model shedding of a pathogen to the environment. Consequently, the model has two state transitions,

$$S \stackrel{\upsilon \varphi S}{\longrightarrow} I$$

$$I \stackrel{\gamma I}{\longrightarrow} S$$

where the transition rate per unit of time from susceptible to infected is proportional to the concentration of the environmental contamination \(\varphi\) in each node. Moreover, the transition rate from infected to susceptible is the recovery rate \(\gamma\), measured per individual and per unit of time. Finally, the environmental infectious pressure in each node is evolved by,

$$\frac{d\varphi(t)}{dt} = \frac{\alpha I(t)}{N(t)} - \beta(t) \varphi(t) + \epsilon$$

where \(\alpha\) is the average shedding rate of the pathogen to the environment per infected individual and \(N = S + I\) the size of the node. The seasonal decay and removal of the pathogen is captured by \(\beta(t)\). It is also possible to include a small background infectious pressure \(\epsilon\) to allow for other indirect sources of environmental contamination. The environmental infectious pressure \(\varphi(t)\) in each node is evolved each time unit by the Euler forward method. The value of \(\varphi(t)\) is saved at the time-points specified in tspan.

The argument u0 must be a data.frame with one row for each node with the following columns:

S

The number of sucsceptible in each node

I

The number of infected in each node

Beta

The time dependent beta is divided into four intervals of the year


where 0 <= day < 365

Case 1: END_1 < END_2 < END_3 < END_4
INTERVAL_1 INTERVAL_2     INTERVAL_3     INTERVAL_4     INTERVAL_1
[0, END_1) [END_1, END_2) [END_2, END_3) [END_3, END_4) [END_4, 365)

Case 2: END_3 < END_4 < END_1 < END_2
INTERVAL_3 INTERVAL_4     INTERVAL_1     INTERVAL_2     INTERVAL_3
[0, END_3) [END_3, END_4) [END_4, END_1) [END_1, END_2) [END_2, 365)

Case 3: END_4 < END_1 < END_2 < END_3
INTERVAL_4 INTERVAL_1     INTERVAL_2     INTERVAL_3     INTERVAL_4
[0, END_4) [END_4, END_1) [END_1, END_2) [END_2, END_3) [END_3, 365)