# Dynamic Markov integration¶

In contrast to Gillespie’s simulation one can write down an approximate equation for the temporal evolution of the nodes’ probability to be infected, (see Eq. (1) in Epidemic Threshold in Continuous-Time Evolving Networks) which can then be integrated. This is slower than the Gillespie simulation but leads to smaller fluctuations which makes it easier to obtain the epidemic threshold. It is based on the assumption that neighboring infections states are uncorrelated and factorize to $$\left< i_u i_v\right>=\left< i_u \right>\left<i_v\right>$$. This assumption is usually justified in non-sparse annealed systems but leads to drastic differences in other systems. Since temporal contact networks are often neither, use this model with care.

## How it works¶

Let’s denote the probability that node u is infected at time t as $$p_u(t)$$. Further, we assume that these probabilities are uncorrelated and that the maximum integration time step $$\Delta t_\mathrm{max}$$ is small. For an SIS-process with infection rate $$\eta$$ and recovery rate $$\varrho$$, the temporal evolution is then governed by

$p_u(t+\Delta t) = 1 - q_u(t,\Delta t)\times\Big[1-p_u(t)\times\left(1-\varrho\Delta t\right)\Big]$

where

$q_u(t, \Delta t) = \prod_{v=1}^N\Big[1-\eta\Delta t A_{uv}(t)p_v(t)\Big]$

is the probability that none of u’s neighbors infects u during this time step.

Here, $$\Delta t=\mathrm{min}\{\Delta t_{\mathrm{network\ change}}, \Delta t_{\mathrm{max}}\}$$ is either the time until the network changes next, or the maximally allowed integration time step $$\Delta t_\mathrm{max}$$, whichever is smaller.

## How it’s implemented¶

In the C++-core _tacoma, the Markov function is implemented to be supplied with a temporal_network and a markov integration model.

The interface between the Markov integration function and a hypothetical Markov model model is defined with the following functions.

• model.update_network : This is used by the Markov integration function to tell the model that the network has been updated. The model then has to update its states internally.
• model.step : The Markov integration function passes the current time and the desired $$\Delta t$$ for the integration to this function. The model then has perform the integration step according to its rules.

Further necessary functions include

• model.simulation_ended : ask the model whether the simulation is essentially over (if the accumulated probability is smaller than some defined threshold model.minimum_I)
• model.update_observables : ask the model to update its internal observals because the simulation is about to end
• model.print : a function to offer a status check for the Gillespie function’s verbose argument
• model.reset : the possibility to wind back the model, e.g. for using the same model instance for a second simulation.

## How to integrate for an SIS-Model¶

First, generate a Markov SIS-object.

mv_SIS = tc.MARKOV_SIS(
N, #numbder of nodes
t_run_total, # run time of the integration
infection_rate,
recovery_rate,
minimum_I, # minimal accumulated probability that nodes are infected
#(below that, the integration is stopped)
number_of_initally_infected=N//2,
seed = seed)


Then, we can integrate and plot the results

tc.markov_epidemics(temporal_network, mv_SIS, max_dt=0.01)
pl.plot(mv_SIS.time, mv_SIS.I)


We can also integrate the equation on a model, on the fly, e.g. the edge activity model

AM = tc.EdgeActivityModel(N, # number of nodes
k/(N-1.), # network density
omega, # edge activity rate
save_temporal_network=False)

tc.markov_epidemics(AM, mv_SIS, max_dt=0.01)
pl.plot(mv_SIS.time, mv_SIS.I)


## How to develop an own model¶

# TODO (this chapter is a bit complicated)