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  1. We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Even optimistic estimates suggest that most countries will likely take 6 to 24 months to vaccinate their citizens. These time estimates and the emergence of new viral strains urge us to find quick and effective ways to allocate the vaccines and contain the pandemic. While current approaches use combinations of age-based and occupation-based prioritizations, our strategy marks a departure from such largely aggregate vaccine allocation strategies. We propose a novel agent-based modeling approach motivated by recent advances in (i) science ofmore »real-world networks that point to efficacy of certain vaccination strategies and (ii) digital technologies that improve our ability to estimate some of these structural properties. Using a realistic representation of a social contact network for the Commonwealth of Virginia, combined with accurate surveillance data on spatio-temporal cases and currently accepted models of within- and between-host disease dynamics, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals' degree (number of social contacts) and total social proximity time is signi ficantly more effective than the currently used age-based allocation strategy in terms of number of infections, hospitalizations and deaths. Our results suggest that in just two months, by March 31, 2021, compared to age-based allocation, the proposed degree-based strategy can result in reducing an additional 56{110k infections, 3.2{5.4k hospitalizations, and 700{900 deaths just in the Commonwealth of Virginia. Extrapolating these results for the entire US, this strategy can lead to 3{6 million fewer infections, 181{306k fewer hospitalizations, and 51{62k fewer deaths compared to age-based allocation. The overall strategy is robust even: (i) if the social contacts are not estimated correctly; (ii) if the vaccine efficacy is lower than expected or only a single dose is given; (iii) if there is a delay in vaccine production and deployment; and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are signi ficant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed.« less
  2. The COVID-19 pandemic brought to the forefront an unprecedented need for experts, as well as citizens, to visualize spatio-temporal disease surveillance data. Web application dashboards were quickly developed to fill this gap, including those built by JHU, WHO, and CDC, but all of these dashboards supported a particular niche view of the pandemic (ie, current status or specific regions). In this paper1, we describe our work developing our own COVID-19 Surveillance Dashboard, available at https://nssac.bii.virginia.edu/covid19/dashboard/, which offers a universal view of the pandemic while also allowing users to focus on the details that interest them. From the beginning, our goalmore »was to provide a simple visual way to compare, organize, and track near-real-time surveillance data as the pandemic progresses. Our dashboard includes a number of advanced features for zooming, filtering, categorizing and visualizing multiple time series on a single canvas. In developing this dashboard, we have also identified 6 key metrics we call the 6Cs standard which we propose as a standard for the design and evaluation of real-time epidemic science dashboards. Our dashboard was one of the first released to the public, and remains one of the most visited and highly used. Our group uses it to support federal, state and local public health authorities, and it is used by people worldwide to track the pandemic evolution, build their own dashboards, and support their organizations as they plan their responses to the pandemic. We illustrate the utility of our dashboard by describing how it can be used to support data story-telling – an important emerging area in data science.« less
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  6. Abstract The coherent photoproduction of $$\mathrm{J}/\psi $$ J / ψ and $${\uppsi '}$$ ψ ′ mesons was measured in ultra-peripheral Pb–Pb collisions at a center-of-mass energy $$\sqrt{s_{\mathrm {NN}}}~=~5.02$$ s NN = 5.02  TeV  with the ALICE detector. Charmonia are detected in the central rapidity region for events where the hadronic interactions are strongly suppressed. The $$\mathrm{J}/\psi $$ J / ψ is reconstructed using the dilepton ( $$l^{+} l^{-}$$ l + l - ) and proton–antiproton decay channels, while for the $${\uppsi '}$$ ψ ′   the dilepton and the $$l^{+} l^{-} \pi ^{+} \pi ^{-}$$ l + l - πmore »+ π - decay channels are studied. The analysis is based on an event sample corresponding to an integrated luminosity of about 233 $$\mu {\mathrm{b}}^{-1}$$ μ b - 1 . The results are compared with theoretical models for coherent $$\mathrm{J}/\psi $$ J / ψ and $${\uppsi '}$$ ψ ′ photoproduction. The coherent cross section is found to be in a good agreement with models incorporating moderate nuclear gluon shadowing of about 0.64 at a Bjorken- x of around $$6\times 10^{-4}$$ 6 × 10 - 4 , such as the EPS09 parametrization, however none of the models is able to fully describe the rapidity dependence of the coherent $$\mathrm{J}/\psi $$ J / ψ cross section including ALICE measurements at forward rapidity. The ratio of $${\uppsi '}$$ ψ ′ to $$\mathrm{J}/\psi $$ J / ψ coherent photoproduction cross sections was also measured and found to be consistent with the one for photoproduction off protons.« less
    Free, publicly-accessible full text available August 1, 2022
  7. Free, publicly-accessible full text available August 1, 2022