Increasing evidence indicates that superspreading plays a dominant role in COVID-19 transmission. Recent estimates suggest that the dispersion parameter k for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is on the order of 0.1, which corresponds to about 10% of cases being the source of 80% of infections. To investigate how overdispersion might affect the outcome of various mitigation strategies, we developed an agent-based model with a social network that allows transmission through contact in three sectors: “close” (a small, unchanging group of mutual contacts as might be found in a household), “regular” (a larger, unchanging group as might be found in a workplace or school), and “random” (drawn from the entire model population and not repeated regularly). We assigned individual infectivity from a gamma distribution with dispersion parameter k. We found that when k was low (i.e., greater heterogeneity, more superspreading events), reducing random sector contacts had a far greater impact on the epidemic trajectory than did reducing regular contacts; when k was high (i.e., less heterogeneity, no superspreading events), that difference disappeared. These results suggest that overdispersion of COVID-19 transmission gives the virus an Achilles’ heel: Reducing contacts between people who do not regularly meet would substantially reduce the pandemic, while reducing repeated contacts in defined social groups would be less effective.
We develop the geometric description of submanifolds in Newton-Cartan spacetime. This provides the necessary starting point for a covariant spacetime formulation of Galilean-invariant hydrodynamics on curved surfaces. We argue that this is the natural geometrical framework to study fluid membranes in thermal equilibrium and their dynamics out of equilibrium. A simple model of fluid membranes that only depends on the surface tension is presented and, extracting the resulting stresses, we show that perturbations away from equilibrium yield the standard result for the dispersion of elastic waves. We also find a generalization of the Canham-Helfrich bending energy for lipid vesicles that takes into account the requirements of thermal equilibrium.
Contact tracing is suggested as an effective strategy for controlling an epidemic without severely limiting personal mobility. Here, we explore how social structure affects contact tracing of COVID-19. Using smartphone proximity data, we simulate the spread of COVID-19 and find that heterogeneity in the social network and activity levels of individuals decreases the severity of an epidemic and improves the effectiveness of contact tracing. As a mitigation strategy, contact tracing depends strongly on social structure and can be remarkably effective, even if only frequent contacts are traced. In perspective, this highlights the necessity of incorporating social heterogeneity into models of mitigation strategies.
How do flat sheets of cells form gut and neural tubes? Across systems, several mechanisms are at play: cells wedge, form actomyosin cables, or intercalate. As a result, the cell sheet bends, and the tube elongates. It is unclear to what extent each mechanism can drive tube formation on its own. To address this question, we computationally probe if one mechanism, either cell wedging or intercalation, may suffice for the entire sheet-to-tube transition. Using a physical model with epithelial cells represented by polarized point particles, we show that either cell intercalation or wedging alone can be sufficient and that each can both bend the sheet and extend the tube. When working in parallel, the two mechanisms increase the robustness of the tube formation. The successful simulations of the key features in Drosophila salivary gland budding, sea urchin gastrulation, and mammalian neurulation support the generality of our results.
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