A city with its citizens and their social life are simulated where a contagious disease is spreading.
The city consists of :
- a number of people,
- their houses where the number of inhabitants differ from house to house (and happily no homeless person in our city),
- a number of work places with a random distribution of number of employees,
- a number of gathering places for people to socialize (and transmit diseases!!!), social people have many places to socialize, not-social people go home instead, and
- a municipality which might intervene with people's life.
Everyday people go happily from home to work to gathering places and back to home!
Meanwhile there is a disease (with tunable contagiosity rate) is spreading via contact between people! Infected people either die (not yet implemented, thanks god!) or get immune.
The results of the simulation are used to feed a neural network for predicting the probability of catching the disease for each individual based on a certain number of features.
You can start the simulation simply by
python simulation.py
in a directory with an inputfile.dat for the inputs. The input file has information about the disease and the city (as explained in the sample file).
In the upper panel of the above graph a snapshot of the city is shown where
- yellow circle: sick people
- black circle: not-infected
- gray circle: recovered
- blue squares: work-places
- red triangles: homes
- red cross: social-places
and in the lower panel the time evolution of different populations are depicted (in arbitrary units).