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The deployment of vaccines across the US provides significant defense against serious illness and death from COVID-19. Over 70% of vaccine-eligible Americans are at least partially vaccinated, but there are pockets of the population that are under-vaccinated, such as in rural areas and some demographic groups (e.g. age, race, ethnicity). These unvaccinated pockets are extremely susceptible to the Delta variant, exacerbating the healthcare crisis and increasing the risk of new variants. In this paper, we describe a data-driven model that provides real-time support to Virginia public health officials by recommending mobile vaccination site placement in order to target under-vaccinated populations. Our strategy uses fine-grained mobility data, along with US Census and vaccination uptake data, to identify locations that are most likely to be visited by unvaccinated individuals. We further extend our model to choose locations that maximize vaccine uptake among hesitant groups. We show that the top recommended sites vary substantially across some demographics, demonstrating the value of developing customized recommendation models that integrate fine-grained, heterogeneous data sources. In addition, we used a statistically equivalent Synthetic Population to study the effect of combined demographics (eg, people of a particular race and age), which is not possible using US Census data alone. We validate our recommendations by analyzing the success rates of deployed vaccine sites, and show that sites placed closer to our recommended areas administered higher numbers of doses. Our model is the first of its kind to consider evolving mobility patterns in real-time for suggesting placement strategies customized for different targeted demographic groups. Our results will be presented at IAAI-22, but given the critical nature of the pandemic, we offer this extended version of that paper for more timely consideration of our approach and to cover additional findings. 

Polymer synthesis routes result in macromolecules with molecular weight dispersity ĐM that depends on the polymerization mechanism. The lowest dispersity polymers are those made by anionic and atom-transfer radical polymerization, which exhibit narrow distributions ĐM = Mw/Mn ∼ 1.02–1.04. Even for small dispersity, the chain length can vary by a factor of two from the average. The impact of chain length dispersity on the viscoelastic response remains an open question. Here, the effects of dispersity on stress relaxation and shear viscosity of entangled polyethylene melts are studied using molecular dynamics simulations. Melts with chain length dispersity, which follow a Schulz–Zimm (SZ) distribution with ĐM = 1.0–1.16, are studied for times up to 800 μs, longer than the terminal time. These systems are compared to those with binary and ternary distributions. The stress relaxation functions are extracted from the Green–Kubo relation and from stress relaxation following a uniaxial extension. At short and intermediate time scales, both the mean squared displacement and the stress relaxation function G(t) are independent of ĐM. At longer times, the terminal relaxation time decreases with increasing ĐM. In this time range, the faster motion of the shorter chains results in constraint release for the longer chains.