Using network science to propose strategies for effectively dealing with pandemics: The COVID-19 example
Helena A Herrmann, Jean-Marc Schwartz
The University of Manchester
Summary
The global spread of Coronavirus Disease 2019 (COVID-19) is overwhelming many health-care systems. As a result, epidemiological models are being used to inform policy on how to effectively deal with this pandemic. We note that the majority of existing models do not take into account differences in the amount of interactions between individuals (i.e. the underlying human interaction network). Using network science we demonstrate how this network of interactions can be used to predict the spread of the virus and to inform policy on the most successful mitigation and suppression strategies. Although applicable to disease modelling in general, our results emphasize how network science can improve the predictive power of current COVID-19 epidemiological models. We provide commented source code for all our analyses so that they can easily be integrated into current and future epidemiological models.
Source Code
The SIRModelsetUp file provides an illustrative example of an SIR model run on a scale-free network.
The SIRModelSimulations file generates the data for SIR models ran on 3 different networks.
(A scale-free network, a Mitigated Hub network were all nodes to have a degree of 8 or less, and a Mitigated Random network
where edges were removed randomly from nodes in the network).
All outputs are stored as pickle files.
The SIRModelAnalysis files generates figures from the simulation saved when running the SIRModelSimulations file.
Pre-print
A snapshopt of the code at the time of the pre-print submission can be found here:
All of the simulations used to generate the figures in our pre-print are stored in the ManuscriptData zip file.
