As COVID‐19 vaccine is being rolled out in the US, public health authorities are gradually reopening the economy. To date, there is no consensus on a common approach among local authorities. Here, a high‐resolution agent‐based model is proposed to examine the interplay between the increased immunity afforded by the vaccine roll‐out and the transmission risks associated with reopening efforts. The model faithfully reproduces the demographics, spatial layout, and mobility patterns of the town of New Rochelle, NY — representative of the urban fabric of the US. Model predictions warrant caution in the reopening under the current rate at which people are being vaccinated, whereby increasing access to social gatherings in leisure locations and households at a 1% daily rate can lead to a 28% increase in the fatality rate within the next three months. The vaccine roll‐out plays a crucial role on the safety of reopening: doubling the current vaccination rate is predicted to be sufficient for safe, rapid reopening.

The potential waning of the vaccination immunity to COVID‐19 could pose threats to public health, as it is tenable that the timing of such waning would synchronize with the near‐complete restoration of normalcy. Should also testing be relaxed, a resurgent COVID‐19 wave in winter 2021/2022 might be witnessed. In response to this risk, an additional vaccine dose, the booster shot, is being administered worldwide. A projected study with an outlook of 6 months explores the interplay between the rate at which boosters are distributed and the extent to which testing practices are implemented, using a highly granular agent‐based model tuned on a medium‐sized US town. Theoretical projections indicate that the administration of boosters at the rate at which the vaccine is currently administered could yield a severe resurgence of the pandemic. Projections suggest that the peak levels of mid‐spring 2021 in the vaccination rate may prevent such a scenario to occur, although exact agreement between observations and projections should not be expected due to the continuously evolving nature of the pandemic. This study highlights the importance of testing, especially to detect asymptomatic individuals in the near future, as the release of the booster reaches full speed.

Clustering of cells is an essential component of many biological processes from tissue formation to cancer metastasis. We develop a minimal, Vicsek-based model of cellular interactions that robustly and accurately captures the variable propensity of different cells to form groups when confined. We calibrate and validate the model with experimental data on clustering affinities of four lines of tumor cells. We then show that cell clustering or separation tendencies are retained in environments with higher cell number densities and in cell mixtures. Finally, we calibrate our model with experimental measurements on the separation of cells treated with anti-clustering agents and find that treated cells maintain their distances in denser suspensions. We show that the model reconstructs several cell interaction mechanisms, which makes it suitable for exploring the dynamics of cell cluster formation as well as cell separation. Insight: We developed a model of cellular interactions that captures the clustering and separation of cells in an enclosure. Our model is particularly relevant for microfluidic systems with confined cells and we centered our work around one such emerging assay for the detection and research on clustering breast cancer cells. We calibrated our model using the existing experimental data and used it to explore the functionality of the assay under a broader set of conditions than originally considered. Future usages of our model can include purely theoretical and computational considerations, exploring experimental devices, and supporting research on small to medium-sized cell clusters.

Circulating tumor cells are central to metastasis, a particularly malign spread of cancer beyond its original location. While rare, there is growing evidence that the clusters of circulating tumor cells are significantly more harmful than individual cells. Microfluidic platforms constitute the core of circulating tumor cell cluster research, allowing cluster detection, analysis, and treatment. In this work, we propose a new mathematical model of circulating tumor cell clusters and apply it to simulate the dynamics of the aggregates inside a microfluidic channel with the external flow of a fluid. We leverage our previous model of the interactions of circulating tumor cells with varying clustering affinities and introduce explicit bonds between the cells that makeup a cluster. We show that the bonds have a visible impact on the cluster dynamics and that they enable the reproduction of known cluster flow and deformation patterns. Furthermore, we demonstrate that the dynamics of these aggregates are sensitive to bond properties, as well as initialization and flow conditions. We believe that our modeling framework represents a valuable mesoscopic formulation with an impact beyond circulating tumor cell clusters, as cell aggregates are common in both nature and applications.

We analyze the vectorial network model, a stochastic protocol that describes collective motion of groups of agents, randomly mixing in a planar space. Motivated by biological and technical applications, we focus on a heterogeneous form of the model, where agents have different propensity to interact with others. By linearizing the dynamics about a synchronous state and leveraging an eigenvalue perturbation argument, we establish a closed-form expression for the mean-square convergence rate to the synchronous state in the absence of additive noise. These closed-form findings are extended to study the effect of added noise on the agents' coordination, captured by the polarization of the group. Our results reveal that heterogeneity has a detrimental effect on both the convergence rate and the polarization, which is nonlinearly moderated by the average number of connections in the group. Numerical simulations are provided to support our theoretical findings.