Assessing the Effectiveness of Lockdowns and Mask Mandates on Reducing COVID-19 Infections in Essential Worker Populations

Throughout history pandemics have been a constant threat for mankind. Still, the COVID-19 disease resulted in the worst health crisis in more than a century. This suggests that more tools are needed to give adequate guidance for decision-making. Several epidemic models have been proposed to understand and forecast the dynamics of spread of infectious diseases. Only a few, however, account for the role of "essential" workers, who must keep interacting face-to-face with others during a pandemic. In this work, we consider how these "essential" workers are disproportionately affected by the pandemic, and how they may act as a reservoir for the disease. We further consider the effect of non-pharmaceutical intervention policies (such as mask mandates and lockdowns), incorporated in a model we term the Local-Policy SIR model. We assume mask mandates are applied only at essential locations, whereas lockdowns function only at non-essential locations. We find this model suggests mask mandates may not be effective without simultaneous lockdowns as a result of the fast dynamics of non-essential risky workers at early stages of an outbreak. We also find that essential workers may play a fundamental role in the dynamics because they may act as reservoirs of the infection during periods of lockdowns. Our results also suggest that partial lockdowns combined with a high mask mandate compliance may prevent an outbreak. We then investigate the effect of delays in enacting these policies and found that even late implementations benefit the entire population, but the benefit decreases for larger delays. Using a gradient descent algorithm, we extracted the mobility for non-essential workers from active infection curves for New York, Texas, and California. This permits an estimation of the share of each subpopulation in the infection and the hazard ratio of essential vs non-essential workers as a function of time. We show that "essential" workers are likely unfairly affected by the pandemic. The LP-SIR model is also applied to neighborhoods composed of heterogeneous populations. We find evidence that a neighborhood composed mainly of "essential" workers is at disadvantage with respect to a neighborhood composed mainly of "non-essential" workers during an outbreak.