Implicit feedback policies for COVID-19: why "zero-COVID" policies remain elusive.

Successful epidemic modeling requires understanding the implicit feedback control strategies used by populations to modulate the spread of contagion. While such strategies can be replicated with intricate modeling assumptions, here we propose a simple model where infection dynamics are described by a three parameter feedback policy. Rather than model individuals as directly controlling the contact rate which governs the spread of disease, we model them as controlling the contact rate's derivative, resulting in a dynamic rather than kinematic model. The feedback policy used by populations across the United States which best fits observations is proportional-derivative control, where learned parameters strongly correlate with observed interventions (e.g., vaccination rates and mobility restrictions). However, this results in a non-zero "steady-state" of case counts, implying current mitigation strategies cannot eradicate COVID-19. Hence, we suggest making implicit policies a function of cumulative cases, resulting in proportional-integral-derivative control with higher potential to eliminate COVID-19.

Public health implications of opening National Football League stadiums during the COVID-19 pandemic.

SignificanceUsing data from 2020, we measure the public health impact of allowing fans into sports stadiums during the COVID-19 pandemic; these results may inform future policy decisions regarding large outdoor gatherings during public health crises. Second, we demonstrate the utility of robust synthetic control in this context. Synthetic control and other statistical approaches may be used to exploit the underlying low-dimensional structure of the COVID-19 data and serve as useful instruments in analyzing the impact of mitigation strategies adopted by different communities. As with all statistical methods, reliable outcomes depend on proper implementation strategies and well-established robustness tests; in the absence of these safeguards, these statistical methods are likely to produce specious or misleading conclusions.

A Latent Source Model for Patch-Based Image Segmentation.

Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work. We bridge this gap by providing a theoretical performance guarantee for nearest-neighbor and weighted majority voting segmentation under a new probabilistic model for patch-based image segmentation. Our analysis relies on a new local property for how similar nearby patches are, and fuses existing lines of work on modeling natural imagery patches and theory for nonparametric classification. We use the model to derive a new patch-based segmentation algorithm that iterates between inferring local label patches and merging these local segmentations to produce a globally consistent image segmentation. Many existing patch-based algorithms arise as special cases of the new algorithm.