# T1 - Getting started¶

Installing and getting started with Covasim is quite simple.

To install, just type pip install covasim. If it worked, you should be able to import Covasim with import covasim as cv.

The basic design philosophy of Covasim is: common tasks should be simple. For example:

• Defining parameters
• Running a simulation
• Plotting results

This tutorial walks you through how to do these things.

Click [here](https://mybinder.org/v2/gh/institutefordiseasemodeling/covasim/HEAD?urlpath=lab%2Ftree%2Fdocs%2Ftutorials%2Ftut_intro.ipynb) to open an interactive version of this notebook.

## Hello world¶

To create, run, and plot a sim with default options is just:

In [ ]:
import covasim as cv

sim = cv.Sim()
sim.run()
fig = sim.plot()


## Defining parameters and running simulations¶

Parameters are defined as a dictionary. The most common parameters to modify are the population size, the initial number of people infected, and the start and end dates of the simulation. We can define those as:

In [ ]:
pars = dict(
pop_size = 50e3,
pop_infected = 100,
start_day = '2020-04-01',
end_day = '2020-06-01',
)


Running a simulation is pretty easy. In fact, running a sim with the parameters we defined above is just:

In [ ]:
sim = cv.Sim(pars)
sim.run()


This will generate a results dictionary sim.results. For example, the number of new infections per day is sim.results['new_infections'].

Rather than creating a parameter dictionary, any valid parameter can also be passed to the sim directly. For example, exactly equivalent to the above is:

In [ ]:
sim = cv.Sim(pop_size=50e3, pop_infected=100, start_day='2020-04-01', end_day='2020-06-01')
sim.run()


You can mix and match too – pass in a parameter dictionary with default options, and then include other parameters as keywords (including overrides; keyword arguments take precedence). For example:

In [ ]:
sim = cv.Sim(pars, pop_infected=10) # Use parameters defined above, except start with 10 infected people
sim.run()


## Plotting results¶

As you saw above, plotting the results of a simulation is rather easy too:

In [ ]:
fig = sim.plot()


## Full usage example¶

Many of the details of this example will be explained in later tutorials, but to give you a taste, here's an example of how you would run two simulations to determine the impact of a custom intervention aimed at protecting the elderly.

In [ ]:
import covasim as cv

# Custom intervention -- see Tutorial 5
def protect_elderly(sim):
if sim.t == sim.day('2020-04-01'):
elderly = sim.people.age>70
sim.people.rel_sus[elderly] = 0.0

pars = dict(
pop_type = 'hybrid', # Use a more realistic population model
location = 'japan', # Use population characteristics for Japan
pop_size = 50e3, # Have 50,000 people total in the population
pop_infected = 100, # Start with 100 infected people
n_days = 90, # Run the simulation for 90 days
verbose = 0, # Do not print any output
)

# Running with multisims -- see Tutorial 3
s1 = cv.Sim(pars, label='Default')
s2 = cv.Sim(pars, interventions=protect_elderly, label='Protect the elderly')
msim = cv.MultiSim([s1, s2])
msim.run()
fig = msim.plot(['cum_deaths', 'cum_infections'])