Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases
This work addresses the need for more granular epidemic simulation tools to evaluate individual-level containment policies, particularly for public health officials and policymakers, though it represents an incremental advancement by applying existing simulation and reinforcement learning techniques to this domain.
The researchers tackled the problem of simulating infectious disease spread with insufficient granularity for individual-level policy analysis by developing Pyfectious, an individual-level simulator that models population structure and disease propagation at the individual level. Using COVID-19 statistics, they demonstrated the simulator's adaptability for making predictions and deriving optimal control policies through reinforcement learning, showing that their method can lead to significantly fewer infected individuals and protect healthcare systems from saturation.
Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating impacts of the outbreak. Many existing simulators are based on compartment models that divide people into a few subsets and simulate the dynamics among those subsets using hypothesized differential equations. However, these models lack the requisite granularity to study the effect of intelligent policies that influence every individual in a particular way. In this work, we introduce a simulator software capable of modeling a population structure and controlling the disease's propagation at an individualistic level. In order to estimate the confidence of the conclusions drawn from the simulator, we employ a comprehensive probabilistic approach where the entire population is constructed as a hierarchical random variable. This approach makes the inferred conclusions more robust against sampling artifacts and gives confidence bounds for decisions based on the simulation results. To showcase potential applications, the simulator parameters are set based on the formal statistics of the COVID-19 pandemic, and the outcome of a wide range of control measures is investigated. Furthermore, the simulator is used as the environment of a reinforcement learning problem to find the optimal policies to control the pandemic. The obtained experimental results indicate the simulator's adaptability and capacity in making sound predictions and a successful policy derivation example based on real-world data. As an exemplary application, our results show that the proposed policy discovery method can lead to control measures that produce significantly fewer infected individuals in the population and protect the health system against saturation.