Network-Based Analysis of Early Pandemic Mitigation Strategies: Solutions, and Future Directions

Despite the large amount of literature on mitigation strategies for pandemic spread, in practice, we are still limited by naïve strategies, such as lockdowns, that are not effective in controlling the spread of the disease in long term. One major reason behind adopting basic strategies in real-world settings is that, in the early stages of a pandemic, we lack knowledge of the behavior of a disease, and so cannot tailor a more sophisticated response. In this study, we design different mitigation strategies for early stages of a pandemic and perform a comprehensive analysis among them. We then propose a novel community-based isolation method and show its efficacy in reducing the speed of the spread by a large margin as compared to current methods. We also show that the test-trace-isolation strategy can outperform lockdown and random test-trace in reducing the economic impact and spread of the disease if combined with k-hop neighborhood ranking. The novelty of our work lies in using network structural properties (local and global) to design a strategy for the early stages of a pandemic. Our results encourage further investigation into community-based mitigation strategies and shed more light on the differences between current methods of choice in practical setting.