Speeding in the slow lane: Phylogenetic comparative analyses reveal that not all human life history traits are exceptional.

Humans are thought to exhibit an unusual suite of life history traits relative to other primates, with a longer lifespan, later age at first reproduction, and shorter interbirth interval. These assumptions are key components of popular hypotheses about human life history evolution, but they have yet to be investigated phylogenetically. We applied two phylogenetic comparative methods to investigate whether these human life history traits differ from expectations based on other primates: one fits and selects between Brownian and Ornstein-Uhlenbeck models of trait evolution; the other tests for phylogenetic outliers by predicting phenotypic characteristics based on trait covariation and phylogeny for a species of interest. We found that humans have exceptionally short interbirth intervals, long lifespans, and high birth masses. We failed to find evidence that humans have a delayed age at first reproduction relative to body mass or other covariates. Overall, our results support several previous assertions about the uniqueness of human life history characteristics and the importance of cooperative breeding and socioecology in human life history evolution. However, we suggest that several hypotheses about human life history need to be revised in light of our finding that humans do not have a delayed age at first reproduction.

Mechanistic models to meet the challenge of climate change in plant-pathogen systems.

Evidence that climate change will impact the ecology and evolution of individual plant species is growing. However, little, as yet, is known about how climate change will affect interactions between plants and their pathogens. Climate drivers could affect the physiology, and thus demography, and ultimately evolutionary processes affecting both plant hosts and their pathogens. Because the impacts of climate drivers may operate in different directions at different scales of infection, and, furthermore, may be nonlinear, abstracting across these processes may mis-specify outcomes. Here, we use mechanistic models of plant-pathogen interactions to illustrate how counterintuitive outcomes are possible, and we introduce how such framing may contribute to understanding climate effects on plant-pathogen systems. We discuss the evidence-base derived from wild and agricultural plant-pathogen systems that could inform such models, specifically in the direction of estimates of physiological, demographic and evolutionary responses to climate change. We conclude by providing an overview of knowledge gaps and directions for future research in this important area. This article is part of the theme issue 'Infectious disease ecology and evolution in a changing world'.

Assessing the risk of vaccine-driven virulence evolution in SARS-CoV-2.

The evolution of SARS-CoV-2 virulence, or lethality, threatens to exacerbate the burden of COVID-19 on society. How might COVID-19 vaccines alter selection for increased SARS-CoV-2 virulence? Framing current evidence surrounding SARS-CoV-2 biology and COVID-19 vaccines in the context of evolutionary theory indicates that prospects for virulence evolution remain uncertain. However, differential effects of vaccinal immunity on transmission and disease severity between respiratory compartments could select for increased virulence. To bound expectations for this outcome, we analyse an evo-epidemiological model. Synthesizing model predictions with vaccine efficacy data, we conclude that while vaccine-driven virulence remains a theoretical possibility, the risk is low if vaccines provide sustained robust protection against infection. Furthermore, we found that any increases in transmission concomitant with increases in virulence would be unlikely to threaten prospects for herd immunity in a highly immunized population. Given that virulence evolution would nevertheless impact unvaccinated individuals and populations with low vaccination rates, it is important to achieve high vaccination rates worldwide and ensure that vaccinal immunity provides robust protection against both infection and disease, potentially through the use of booster doses.

Predicting the effects of climate change on the cross-scale epidemiological dynamics of a fungal plant pathogen.

The potential for climate change to exacerbate the burden of human infectious diseases is increasingly recognized, but its effects on infectious diseases of plants have received less attention. Understanding the impacts of climate on the epidemiological dynamics of plant pathogens is imperative, as these organisms play central roles in natural ecosystems and also pose a serious threat to agricultural production and food security. We use the fungal 'flax rust' pathogen (Melampsora lini) and its subalpine wildflower host Lewis flax (Linum lewisii) to investigate how climate change might affect the dynamics of fungal plant pathogen epidemics using a combination of empirical and modeling approaches. Our results suggest that climate change will initially slow transmission at both the within- and between-host scales. However, moderate resurgences in disease spread are predicted as warming progresses, especially if the rate of greenhouse gas emissions continues to increase at its current pace. These findings represent an important step towards building a holistic understanding of climate effects on plant infectious disease that encompasses demographic, epidemiological, and evolutionary processes. A core result is that neglecting processes at any one scale of plant pathogen transmission may bias projections of climate effects, as climate drivers have variable and cascading impacts on processes underlying transmission that occur at different scales.

Infectious disease in an era of global change.

The twenty-first century has witnessed a wave of severe infectious disease outbreaks, not least the COVID-19 pandemic, which has had a devastating impact on lives and livelihoods around the globe. The 2003 severe acute respiratory syndrome coronavirus outbreak, the 2009 swine flu pandemic, the 2012 Middle East respiratory syndrome coronavirus outbreak, the 2013-2016 Ebola virus disease epidemic in West Africa and the 2015 Zika virus disease epidemic all resulted in substantial morbidity and mortality while spreading across borders to infect people in multiple countries. At the same time, the past few decades have ushered in an unprecedented era of technological, demographic and climatic change: airline flights have doubled since 2000, since 2007 more people live in urban areas than rural areas, population numbers continue to climb and climate change presents an escalating threat to society. In this Review, we consider the extent to which these recent global changes have increased the risk of infectious disease outbreaks, even as improved sanitation and access to health care have resulted in considerable progress worldwide.

Challenges in modeling the emergence of novel pathogens.

The emergence of infectious agents with pandemic potential present scientific challenges from detection to data interpretation to understanding determinants of risk and forecasts. Mathematical models could play an essential role in how we prepare for future emergent pathogens. Here, we describe core directions for expansion of the existing tools and knowledge base, including: using mathematical models to identify critical directions and paths for strengthening data collection to detect and respond to outbreaks of novel pathogens; expanding basic theory to identify infectious agents and contexts that present the greatest risks, over both the short and longer term; by strengthening estimation tools that make the most use of the likely range and uncertainties in existing data; and by ensuring modelling applications are carefully communicated and developed within diverse and equitable collaborations for increased public health benefit.

Variation in SARS-CoV-2 outbreaks across sub-Saharan Africa.

A surprising feature of the SARS-CoV-2 pandemic to date is the low burdens reported in sub-Saharan Africa (SSA) countries relative to other global regions. Potential explanations (for example, warmer environments1, younger populations2-4) have yet to be framed within a comprehensive analysis. We synthesized factors hypothesized to drive the pace and burden of this pandemic in SSA during the period from 25 February to 20 December 2020, encompassing demographic, comorbidity, climatic, healthcare capacity, intervention efforts and human mobility dimensions. Large diversity in the probable drivers indicates a need for caution in interpreting analyses that aggregate data across low- and middle-income settings. Our simulation shows that climatic variation between SSA population centers has little effect on early outbreak trajectories; however, heterogeneity in connectivity, although rarely considered, is likely an important contributor to variance in the pace of viral spread across SSA. Our synthesis points to the potential benefits of context-specific adaptation of surveillance systems during the ongoing pandemic. In particular, characterizing patterns of severity over age will be a priority in settings with high comorbidity burdens and poor access to care. Understanding the spatial extent of outbreaks warrants emphasis in settings where low connectivity could drive prolonged, asynchronous outbreaks resulting in extended stress to health systems.

Disease and healthcare burden of COVID-19 in the United States.

As of 24 April 2020, the SARS-CoV-2 epidemic has resulted in over 830,000 confirmed infections in the United States1. The incidence of COVID-19, the disease associated with this new coronavirus, continues to rise. The epidemic threatens to overwhelm healthcare systems, and identifying those regions where the disease burden is likely to be high relative to the rest of the country is critical for enabling prudent and effective distribution of emergency medical care and public health resources. Globally, the risk of severe outcomes associated with COVID-19 has consistently been observed to increase with age2,3. We used age-specific mortality patterns in tandem with demographic data to map projections of the cumulative case burden of COVID-19 and the subsequent burden on healthcare resources. The analysis was performed at the county level across the United States, assuming a scenario in which 20% of the population of each county acquires infection. We identified counties that will probably be consistently, heavily affected relative to the rest of the country across a range of assumptions about transmission patterns, such as the basic reproductive rate, contact patterns and the efficacy of quarantine. We observed a general pattern that per capita disease burden and relative healthcare system demand may be highest away from major population centers. These findings highlight the importance of ensuring equitable and adequate allocation of medical care and public health resources to communities outside of major urban areas.

High variation expected in the pace and burden of SARS-CoV-2 outbreaks across sub-Saharan Africa.

A surprising feature of the SARS-CoV-2 pandemic to date is the low burdens reported in sub-Saharan Africa (SSA) countries relative to other global regions. Potential explanations (e.g., warmer environments1, younger populations2-4) have yet to be framed within a comprehensive analysis accounting for factors that may offset the effects of climate and demography. Here, we synthesize factors hypothesized to shape the pace of this pandemic and its burden as it moves across SSA, encompassing demographic, comorbidity, climatic, healthcare and intervention capacity, and human mobility dimensions of risk. We find large scale diversity in probable drivers, such that outcomes are likely to be highly variable among SSA countries. While simulation shows that extensive climatic variation among SSA population centers has little effect on early outbreak trajectories, heterogeneity in connectivity is likely to play a large role in shaping the pace of viral spread. The prolonged, asynchronous outbreaks expected in weakly connected settings may result in extended stress to health systems. In addition, the observed variability in comorbidities and access to care will likely modulate the severity of infection: We show that even small shifts in the infection fatality ratio towards younger ages, which are likely in high risk settings, can eliminate the protective effect of younger populations. We highlight countries with elevated risk of 'slow pace', high burden outbreaks. Empirical data on the spatial extent of outbreaks within SSA countries, their patterns in severity over age, and the relationship between epidemic pace and health system disruptions are urgently needed to guide efforts to mitigate the high burden scenarios explored here.

Vaccine-driven virulence evolution: consequences of unbalanced reductions in mortality and transmission and implications for pertussis vaccines.

Many vaccines have heterogeneous effects across individuals. Additionally, some vaccines do not prevent infection, but reduce disease-associated mortality and transmission. Both of these factors will alter selection pressures on pathogens and thus shape the evolution of pathogen virulence. We use a mathematical modelling framework to show that (i) the balance of how vaccines reduce transmission versus mortality and (ii) individual variability in protection conferred both shape the evolution of pathogen virulence. Epidemiological (burden of disease) and evolutionary (pathogen virulence) outcomes are both worse when vaccines confer smaller reductions in transmission than in mortality. Furthermore, outcomes are modulated by variability in vaccine effects, with increased variability limiting the extent of virulence evolution but in some cases preventing eradication. These findings are pertinent to current concerns about the global resurgence of pertussis and the efficacy of pertussis vaccines, as the two classes of these vaccines may reduce disease symptoms without preventing infection and differ in their ability to reduce transmission. Furthermore, these findings point to the importance of generating precise predictions for virulence evolution in Bordetella pertussis (and other similar pathogens) by incorporating empirical characterizations of vaccine effects into models capturing the epidemiological details of this system.

Quantitative uniqueness of human brain evolution revealed through phylogenetic comparative analysis.

While the human brain is clearly large relative to body size, less is known about the timing of brain and brain component expansion within primates and the relative magnitude of volumetric increases. Using Bayesian phylogenetic comparative methods and data for both extant and fossil species, we identified that a distinct shift in brain-body scaling occurred as hominins diverged from other primates, and again as humans and Neanderthals diverged from other hominins. Within hominins, we detected a pattern of directional and accelerating evolution towards larger brains, consistent with a positive feedback process in the evolution of the human brain. Contrary to widespread assumptions, we found that the human neocortex is not exceptionally large relative to other brain structures. Instead, our analyses revealed a single increase in relative neocortex volume at the origin of haplorrhines, and an increase in relative cerebellar volume in apes.

Estimating infection prevalence: Best practices and their theoretical underpinnings.

Accurately estimating infection prevalence is fundamental to the study of population health, disease dynamics, and infection risk factors. Prevalence is estimated as the proportion of infected individuals ("individual-based estimation"), but is also estimated as the proportion of samples in which evidence of infection is detected ("anonymous estimation"). The latter method is often used when researchers lack information on individual host identity, which can occur during noninvasive sampling of wild populations or when the individual that produced a fecal sample is unknown. The goal of this study was to investigate biases in individual-based versus anonymous prevalence estimation theoretically and to test whether mathematically derived predictions are evident in a comparative dataset of gastrointestinal helminth infections in nonhuman primates. Using a mathematical model, we predict that anonymous estimates of prevalence will be lower than individual-based estimates when (a) samples from infected individuals do not always contain evidence of infection and/or (b) when false negatives occur. The mathematical model further predicts that no difference in bias should exist between anonymous estimation and individual-based estimation when one sample is collected from each individual. Using data on helminth parasites of primates, we find that anonymous estimates of prevalence are significantly and substantially (12.17%) lower than individual-based estimates of prevalence. We also observed that individual-based estimates of prevalence from studies employing single sampling are on average 6.4% higher than anonymous estimates, suggesting a bias toward sampling infected individuals. We recommend that researchers use individual-based study designs with repeated sampling of individuals to obtain the most accurate estimate of infection prevalence. Moreover, to ensure accurate interpretation of their results and to allow for prevalence estimates to be compared among studies, it is essential that authors explicitly describe their sampling designs and prevalence calculations in publications.