Public Health Interventions in the COVID-19 Endgame: Insights from Percolation Theory

点击翻译按钮获取中文摘要

The COVID-19 pandemic calls for innovation in a broad range of scientific methods, including mathematical models. Thus far, most mathematical models developed during the pandemic have studied epidemics in a single, well-mixed population. However, on the far side of a flattened epidemic curve, disease spread will be characterized by sporadic, geographically dispersed outbreaks. It will be necessary to understand how this phase of the epidemic curve will change depending on what infectious disease interventions we apply, as we begin to re-open our economies. Percolation theory-the study of how fluids move through a material-is a physics-based theory that is much better suited to study epidemic dynamics in the later stages of the pandemic than many conventional mathematical modelling approaches. We propose to develop a percolation-based approach to understanding late-stage COVID-19 dynamics and how it responds to different testing, isolation, vaccination, and contact tracing approaches. Then, we will integrate the results of this percolation model into an agent-based network simulation of COVID-19 spread between population centres across Canadian provinces, including the impacts of physical distancing and school/workplace closure on infection spread. We will use realistic demographic and COVID-19 disease data as model inputs. The model will project cases, hospitalizations, and deaths under different approaches to re-opening the economy, such as on a county-by-county basis according to stipulated "trigger" conditions on the number of local cases. This will enable us to assess combined strategies of testing, physical distancing, and school/workplace closure. Most crucially, it will provide a tool that provincial decision-makers can use to determine how to re-open provincial economics without risking a resurgence of COVID-19 cases.