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The COVID-19 pandemic has caused more than 1,000,000 reported deaths globally, of which more than 200,000 have been reported in the United States as of October 1, 2020. Public health interventions have had significant impacts in reducing transmission and in averting even more deaths. Nonetheless, in many jurisdictions, the decline of cases and fatalities after apparent epidemic peaks has not been rapid. Instead, the asymmetric decline in cases appears, in most cases, to be consistent with plateau- or shoulder-like phenomena—a qualitative observation reinforced by a symmetry analysis of US state-level fatality data. Here we explore a model of fatality-driven awareness in which individual protective measures increase with death rates. In this model, fast increases to the peak are often followed by plateaus, shoulders, and lag-driven oscillations. The asymmetric shape of model-predicted incidence and fatality curves is consistent with observations from many jurisdictions. Yet, in contrast to model predictions, we find that population-level mobility metrics usually increased from low levels before fatalities reached an initial peak. We show that incorporating fatigue and long-term behavior change can reconcile the apparent premature relaxation of mobility reductions and help understand when post-peak dynamics are likely to lead to a resurgence of cases.

Viruses that infect microbial hosts have traditionally been studied in laboratory settings with a focus on either obligate lysis or persistent lysogeny. In the environment, these infection archetypes are part of a continuum that spans antagonistic to beneficial modes. In this Review, we advance a framework to accommodate the context-dependent nature of virus–microorganism interactions in ecological communities by synthesizing knowledge from decades of virology research, eco-evolutionary theory and recent technological advances. We discuss that nuanced outcomes, rather than the extremes of the continuum, are particularly likely in natural communities given variability in abiotic factors, the availability of suboptimal hosts and the relevance of multitrophic partnerships. We revisit the ‘rules of life’ in terms of how long-term infections shape the fate of viruses and microbial cells, populations and ecosystems.In this Review, Correa and colleagues revisit the rules of life for viruses of microorganisms by advancing a conceptual framework that recognizes virus–host interactions across a continuum of infection modalities and by examining the influence of these modalities on viruses, their hosts and ecosystems.

Multiple epidemiological models have been proposed to predict the spread of Ebola in West Africa. These models include consideration of counter-measures meant to slow and, eventually, stop the spread of the disease. Here, we examine one component of Ebola dynamics that is of ongoing concern – the transmission of Ebola from the dead to the living. We do so by applying the toolkit of mathematical epidemiology to analyze the consequences of post-death transmission. We show that underlying disease parameters cannot be inferred with confidence from early-stage incidence data (that is, they are not “identifiable”) because different parameter combinations can produce virtually the same epidemic trajectory. Despite this identifiability problem, we find robustly that inferences that don't account for post-death transmission tend to underestimate the basic reproductive number – thus, given the observed rate of epidemic growth, larger amounts of post-death transmission imply larger reproductive numbers. From a control perspective, we explain how improvements in reducing post-death transmission of Ebola may reduce the overall epidemic spread and scope substantially. Increased attention to the proportion of post-death transmission has the potential to aid both in projecting the course of the epidemic and in evaluating a portfolio of control strategies.

A hallmark of Lotka–Volterra models, and other ecological models of predator–prey interactions, is that in predator–prey cycles, peaks in prey abundance precede peaks in predator abundance. Such models typically assume that species life history traits are fixed over ecologically relevant time scales. However, the coevolution of predator and prey traits has been shown to alter the community dynamics of natural systems, leading to novel dynamics including antiphase and cryptic cycles. Here, using an eco-coevolutionary model, we show that predator–prey coevolution can also drive population cycles where the opposite of canonical Lotka–Volterra oscillations occurs: predator peaks precede prey peaks. These reversed cycles arise when selection favors extreme phenotypes, predator offense is costly, and prey defense is effective against low-offense predators. We present multiple datasets from phage–cholera, mink–muskrat, and gyrfalcon–rock ptarmigan systems that exhibit reversed-peak ordering. Our results suggest that such cycles are a potential signature of predator–prey coevolution and reveal unique ways in which predator–prey coevolution can shape, and possibly reverse, community dynamics.

Quantifying diversity is of central importance for the study of structure, function and evolution of microbial communities. The estimation of microbial diversity has received renewed attention with the advent of large-scale metagenomic studies. Here, we consider what the diversity observed in a sample tells us about the diversity of the community being sampled. First, we argue that one cannot reliably estimate the absolute and relative number of microbial species present in a community without making unsupported assumptions about species abundance distributions. The reason for this is that sample data do not contain information about the number of rare species in the tail of species abundance distributions. We illustrate the difficulty in comparing species richness estimates by applying Chao’s estimator of species richness to a set of in silico communities: they are ranked incorrectly in the presence of large numbers of rare species. Next, we extend our analysis to a general family of diversity metrics (‘Hill diversities’), and construct lower and upper estimates of diversity values consistent with the sample data. The theory generalizes Chao’s estimator, which we retrieve as the lower estimate of species richness. We show that Shannon and Simpson diversity can be robustly estimated for the in silico communities. We analyze nine metagenomic data sets from a wide range of environments, and show that our findings are relevant for empirically-sampled communities. Hence, we recommend the use of Shannon and Simpson diversity rather than species richness in efforts to quantify and compare microbial diversity.

Bacteriophage (or ‘phage’ – viruses that infect and kill bacteria) are increasingly considered as a therapeutic alternative to treat antibiotic-resistant bacterial infections. However, bacteria can evolve resistance to phage, presenting a significant challenge to the near- and long-term success of phage therapeutics. Application of mixtures of multiple phages ( i.e. , ‘cocktails’) has been proposed to limit the emergence of phage-resistant bacterial mutants that could lead to therapeutic failure. Here, we combine theory and computational models of in vivo phage therapy to study the efficacy of a phage cocktail, composed of two complementary phages motivated by the example of Pseudomonas aeruginosa facing two phages that exploit different surface receptors, LUZ19v and PAK_P1. As confirmed in a Luria-Delbr ck fluctuation test, this motivating example serves as a model for instances where bacteria are extremely unlikely to develop simultaneous resistance mutations against both phages. We then quantify therapeutic outcomes given single- or double-phage treatment models, as a function of phage traits and host immune strength. Building upon prior work showing monophage therapy efficacy in immunocompetent hosts, here we show that phage cocktails comprised of phage targeting independent bacterial receptors can improve treatment outcome in immunocompromised hosts and reduce the chance that pathogens simultaneously evolve resistance against phage combinations. The finding of phage cocktail efficacy is qualitatively robust to differences in virus-bacteria interactions and host immune dynamics. Altogether, the combined use of theory and computational analysis highlights the influence of viral life history traits and receptor complementarity when designing and deploying phage cocktails in immunocompetent and immunocompromised hosts.

Key Points Virus-mediated lysis of host cells results in the generation of dissolved organic carbon (DOC), dissolved organic nitrogen (DON) and dissolved organic phosphorus (DOP) via a process that is known as the 'viral shunt'. Previous quantitative estimates of the contribution of the viral shunt to biogeochemical cycles focused on host cellular constituents and overlooked the contribution of virus particles. In this Analysis article, we develop a biophysical scaling model that predicts the elemental contents and compositions of virus particles. This scaling model was validated using detailed sequence and structural contents of intact bacteriophage particles. Viruses are predicted to be enriched in phosphorus, so much so that the total phosphorus content in a burst of released viruses may approach that of the phosphorus content in an uninfected host. As a consequence, cellular debris may be depleted in phosphorus compared with the stoichiometry of hosts. Furthermore, by extrapolating the model to the ecosystem scale, marine viruses are predicted to contain an important fraction (for example, >5%) of the total DOP pool in some systems (for example, in surface waters, when virus density exceeds 3.5 × 10 10 and the DOP concentration is approximately 100 nM). Weitz and colleagues use a biophysical scaling model of intact virus particles to quantify differences in the elemental stoichiometry of marine viruses compared with their microbial hosts. They propose that, under certain circumstances, marine virus populations could make a previously unrecognised and important contribution to the reservoir and cycling of oceanic phosphorus. In marine environments, virus-mediated lysis of host cells leads to the release of cellular carbon and nutrients and is hypothesized to be a major driver of carbon recycling on a global scale. However, efforts to characterize the effects of viruses on nutrient cycles have overlooked the geochemical potential of the virus particles themselves, particularly with respect to their phosphorus content. In this Analysis article, we use a biophysical scaling model of intact virus particles that has been validated using sequence and structural information to quantify differences in the elemental stoichiometry of marine viruses compared with their microbial hosts. By extrapolating particle-scale estimates to the ecosystem scale, we propose that, under certain circumstances, marine virus populations could make an important contribution to the reservoir and cycling of oceanic phosphorus.

The reproduction number 𝓡 and the growth rate r are critical epidemiological quantities. They are linked by generation intervals, the time between infection and onward transmission. Because generation intervals are difficult to observe, epidemiologists often substitute serial intervals, the time between symptom onset in successive links in a transmission chain. Recent studies suggest that such substitution biases estimates of 𝓡 based on r. Here we explore how these intervals vary over the course of an epidemic, and the implications for 𝓡 estimation. Forward-looking serial intervals, measuring time forward from symptom onset of an infector, correctly describe the renewal process of symptomatic cases and therefore reliably link 𝓡 with r. In contrast, backward-looking intervals, which measure time backward, and intrinsic intervals, which neglect population-level dynamics, give incorrect 𝓡 estimates. Forward-looking intervals are affected both by epidemic dynamics and by censoring, changing in complex ways over the course of an epidemic.We present a heuristic method for addressing biases that arise from neglecting changes in serial intervals. We apply the method to early (21 January to February 8, 2020) serial interval-based estimates of 𝓡 for the COVID-19 outbreak in China outside Hubei province; using improperly defined serial intervals in this context biases estimates of initial 𝓡 by up to a factor of 2.6. This study demonstrates the importance of early contact tracing efforts and provides a framework for reassessing generation intervals, serial intervals, and 𝓡 estimates for COVID-19.

Bacteriophages are the most abundant biological life forms on Earth. However, relatively little is known regarding which bacteriophages infect and exploit which bacteria. A recent meta-analysis showed that empirically measured phage-bacteria infection networks are often significantly nested, on average, and not modular. A perfectly nested network is one in which phages can be ordered from specialist to generalist such that the host range of a given phage is a subset of the host range of the subsequent phage in the ordering. The same meta-analysis hypothesized that modularity, in which groups of phages specialize on distinct groups of hosts, should emerge at larger geographic and/or taxonomic scales. In this paper, we evaluate the largest known phage-bacteria interaction data set, representing the interaction of 215 phage types with 286 host types sampled from geographically separated sites in the Atlantic Ocean. We find that this interaction network is highly modular. In addition, some of the modules identified in this data set are nested or contain submodules, indicating the presence of multi-scale structure, as hypothesized in the earlier meta-analysis. We examine the role of geography in driving these patterns and find evidence that the host range of phages and the phage permissibility of bacteria is driven, in part, by geographic separation. We conclude by discussing approaches to disentangle the roles of ecology and evolution in driving complex patterns of interaction between phages and bacteria.

The processes responsible for the evolution of key innovations, whereby lineages acquire qualitatively new functions that expand their ecological opportunities, remain poorly understood. We examined how a virus, bacteriophage λ, evolved to infect its host, Eschenchia coli, through a novel pathway. Natural selection promoted the fixation of mutations in the virus's host-recognition protein, J, that improved fitness on the original receptor, LamB, and set the stage for other mutations that allowed infection through a new receptor, OmpF. These viral mutations arose after the host evolved reduced expression of LamB, whereas certain other host mutations prevented the phage from evolving the new function.This study shows the complex interplay between genomic processes and ecological conditions that favor the emergence of evolutionary innovations.