Background Anticipating an initial shortage of vaccines for COVID-19, the Centers for Disease Control (CDC) in the United States developed priority vaccine allocations for specific demographic groups in the population. This study evaluates the performance of the CDC vaccine allocation strategy with respect to multiple potentially competing vaccination goals (minimizing mortality, cases, infections, and years of life lost (YLL)), under the same framework as the CDC allocation: four priority vaccination groups and population demographics stratified by age, comorbidities, occupation and living condition (congested or non-congested). Methods and findings We developed a compartmental disease model that incorporates key elements of the current pandemic including age-varying susceptibility to infection, age-varying clinical fraction, an active case-count dependent social distancing level, and time-varying infectivity (accounting for the emergence of more infectious virus strains). The CDC allocation strategy is compared to all other possibly optimal allocations that stagger vaccine roll-out in up to four phases (17.5 million strategies). The CDC allocation strategy performed well in all vaccination goals but never optimally. Under the developed model, the CDC allocation deviated from the optimal allocations by small amounts, with 0.19% more deaths, 4.0% more cases, 4.07% more infections, and 0.97% higher YLL, than the respective optimal strategies. The CDC decision to not prioritize the vaccination of individuals under the age of 16 was optimal, as was the prioritization of health-care workers and other essential workers over non-essential workers. Finally, a higher prioritization of individuals with comorbidities in all age groups improved outcomes compared to the CDC allocation. Conclusion The developed approach can be used to inform the design of future vaccine allocation strategies in the United States, or adapted for use by other countries seeking to optimize the effectiveness of their vaccine allocation strategies.

A stochastic compartmental network model of SARS-CoV-2 spread explores the simultaneous effects of policy choices in three domains: social distancing, hospital triaging, and testing. Considering policy domains together provides insight into how different policy decisions interact. The model incorporates important characteristics of COVID-19, the disease caused by SARS-CoV-2, such as heterogeneous risk factors and asymptomatic transmission, and enables a reliable qualitative comparison of policy choices despite the current uncertainty in key virus and disease parameters. Results suggest possible refinements to current policies, including emphasizing the need to reduce random encounters more than personal contacts, and testing low-risk symptomatic individuals before high-risk symptomatic individuals. The strength of social distancing of symptomatic individuals affects the degree to which asymptomatic cases drive the epidemic as well as the level of population-wide contact reduction needed to keep hospitals below capacity. The relative importance of testing and triaging also depends on the overall level of social distancing.

Disentangling the origin of species–genetic diversity correlations (SGDCs) is a challenging task that provides insight into the way that neutral and adaptive processes influence diversity at multiple levels. Genetic and species diversity are comprised by components that respond differently to the same ecological processes. Thus, it can be useful to partition species and genetic diversity into their different components to infer the mechanisms behind SGDCs. In this study, we applied such an approach using a high‐elevation Andean wetland system, where previous evidence identified neutral processes as major determinants of the strong and positive covariation between plant species richness and AFLP genetic diversity of the common sedge Carex gayana. To tease apart putative neutral and non‐neutral genetic variation of C. gayana, we identified loci putatively under selection from a dataset of 1,709 SNPs produced using restriction site‐associated DNA sequencing (RAD‐seq). Significant and positive relationships between local estimates of genetic and species diversities (α‐SGDCs) were only found with the putatively neutral loci datasets and with species richness, confirming that neutral processes were primarily driving the correlations and that the involved processes differentially influenced local species diversity components (i.e., richness and evenness). In contrast, SGDCs based on genetic and community dissimilarities (β‐SGDCs) were only significant with the putative non‐neutral datasets. This suggests that selective processes influencing C. gayana genetic diversity were involved in the detected correlations. Together, our results demonstrate that analyzing distinct components of genetic and species diversity simultaneously is useful to determine the mechanisms behind species–genetic diversity relationships.

Contact between people with similar opinions and characteristics occurs at a higher rate than among other people, a phenomenon known as homophily. The presence of clusters of unvaccinated people has been associated with increased incidence of infectious disease outbreaks despite high population-wide vaccination rates. The epidemiological consequences of homophily regarding other beliefs as well as correlations among beliefs or circumstances are poorly understood, however. Here, we use a simple compartmental disease model as well as a more complex COVID-19 model to study how homophily and correlation of beliefs and circumstances in a social interaction network affect the probability of disease outbreak and COVID-19-related mortality. We find that the current social context, characterized by the presence of homophily and correlations between who vaccinates, who engages in risk reduction, and individual risk status, corresponds to a situation with substantially worse disease burden than in the absence of heterogeneities. In the presence of an effective vaccine, the effects of homophily and correlation of beliefs and circumstances become stronger. Further, the optimal vaccination strategy depends on the degree of homophily regarding vaccination status as well as the relative level of risk mitigation high- and low-risk individuals practice. The developed methods are broadly applicable to any investigation in which node attributes in a graph might reasonably be expected to cluster or exhibit correlations.

We present a stochastic compartmental network model of SARS-CoV-2 and COVID-19 exploring the e↵ects of policy choices in three domains: social distancing, hospital triaging, and testing. We distinguished between high-risk and low-risk members of the population, and modeled di↵erences in social interactions due to context, risk level, infection status, and testing status. The model incorporates many of the currently important characteristics of the disease, including overcapacity in the healthcare system and uncertainties surrounding the proportion and transmission potential of asymptomatic cases. We compared current policy guidelines from public health agencies with alternative options, and investigated the e↵ects of policy decisions on the overall proportion of COVID-19-related deaths. Our results support current policies to contain the outbreak but also suggest possible refinements, including emphasizing the need to reduce public, random contacts more than private contacts, and testing low-risk symptomatic individuals before high-risk symptomatic individuals. Our model furthermore points to interactions among the three policy domains; the e cacy of a particular policy choice depends on other implemented policies. Finally, our results provide an explanation for why societies like Germany, with lower average rates of social contact, are more successful at containing the outbreak than highly social societies such as Italy, despite the implementation of similar policy measures.