Early Warning Signals for COVID-19 Using Probabilistic Risk Awareness Framework
The Covid-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries, resulting in widespread lockdowns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity without triggering a second outbreak. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restriction, and public health.Many approaches model infection and encounters as binary events. With such approaches, called binary contact tracing, once a case is confirmed by a positive lab test result, it is propagated to people who were contacts of the infected person, typically recommending that these individuals should self-quarantine. This approach ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier self-quarantine. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate risk predictions.Methods which may use such information have been proposed, but these typically require access to the graph of social interactions and/or centralization of sensitive personal data, which is incompatible with reasonable privacy and security constraints.In this work, we use an agent-based epidemiological simulation to develop and test ML methods that can be deployed to a smartphone to locally predict an individual’s risk of infection from their contact history and other information, while respecting strong privacy and security constraints. We use this risk score to provide personalized recommendations to the user via an app, an approach we call Probabilistic Risk Awareness (PRA).We show that PRA can significantly reduce spread of the disease compared to other methods, for equivalent average mobility and realistic assumptions about app adoption, and thereby save lives.
Healthy world Tools, practices and systems