We use data from the 2017 Origin-Destination survey to build a representative contact network for the city of Sao Paulo, where individuals are connected by different social relations (school, work, neighborhood and households).
The network is used to devise a stochastic discrete time and state compartmental model for the spread of the COVID-19.
We employed the model to compare different mitigation strategies. The results show that even simple Monte Carlo planners greatly improve the performance over reactive strategies in terms of balancing the economical and health impacts of non-pharmaceutical interventions.
Read the paper in english, as published at ENIAC 2020 or the extended portuguese version.
Checkout the code at github.