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Abstract Global climate change is predicted to cause range shifts in the mosquito species that transmit pathogens to humans and wildlife. Recent modeling studies have sought to improve our understanding of the relationship between temperature and the transmission potential of mosquito‐borne pathogens. However, the role of the vertebrate host population, including the importance of host behavioral defenses on mosquito feeding success, remains poorly understood despite ample empirical evidence of its significance to pathogen transmission. Here, we derived thermal performance curves for mosquito and parasite traits and integrated them into two models of vector–host contact to investigate how vertebrate host traits and behaviors affect two key thermal properties of mosquito‐borne parasite transmission: the thermal optimum for transmission and the thermal niche of the parasite population. We parameterized these models for five mosquito‐borne parasite transmission systems, leading to two main conclusions. First, vertebrate host availability may induce a shift in the thermal optimum of transmission. When the tolerance of the vertebrate host to biting from mosquitoes is limited, the thermal optimum of transmission may be altered by as much as 5°C, a magnitude of applied significance. Second, thresholds for sustained transmission depend nonlinearly on both vertebrate host availability and temperature. At any temperature, sustained transmission is impossible when vertebrate hosts are extremely abundant because the probability of encountering an infected individual is negligible. But when host biting tolerance is limited, sustained transmission will also not occur at low host population densities. Furthermore, our model indicates that biting tolerance should interact with vertebrate host population density to adjust the parasite population thermal niche. Together, these results suggest that vertebrate host traits and behaviors play essential roles in the thermal properties of mosquito‐borne parasite transmission. Increasing our understanding of this relationship should lead us to improved predictions about shifting global patterns of mosquito‐borne disease. 

To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March–December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time. 

The concurrent pressures of rising global temperatures, rates and incidence of species decline, and emergence of infectious diseases represent an unprecedented planetary crisis. Intergovernmental reports have drawn focus to the escalating climate and biodiversity crises and the connections between them, but interactions among all three pressures have been largely overlooked. Non-linearities and dampening and reinforcing interactions among pressures make considering interconnections essential to anticipating planetary challenges. In this Review, we define and exemplify the causal pathways that link the three global pressures of climate change, biodiversity loss, and infectious disease. A literature assessment and case studies show that the mechanisms between certain pairs of pressures are better understood than others and that the full triad of interactions is rarely considered. Although challenges to evaluating these interactions—including a mismatch in scales, data availability, and methods—are substantial, current approaches would benefit from expanding scientific cultures to embrace interdisciplinarity and from integrating animal, human, and environmental perspectives. Considering the full suite of connections would be transformative for planetary health by identifying potential for co-benefits and mutually beneficial scenarios, and highlighting where a narrow focus on solutions to one pressure might aggravate another. 

Quantifying uncertainty in forest assessments is challenging because of the number of sources of error and the many possible approaches to quantify and propagate them. The uncertainty in allometric equations has sometimes been represented by propagating uncertainty only in the prediction of individuals, but at large scales with large numbers of trees uncertainty in model fit is more important than uncertainty in individuals. We compared four different approaches to representing model uncertainty: a formula for the confidence interval, Monte Carlo sampling of the slope and intercept of the regression, bootstrap resampling of the allometric data, and a Bayesian approach. We applied these approaches to propagating model uncertainty at four different scales of tree inventory (10 to 10,000 trees) for four study sites with varying allometry and model fit statistics, ranging from a monocultural plantation to a multi-species shrubland with multi-stemmed trees. We found that the four approaches to quantifying uncertainty in model fit were in good agreement, except that bootstrapping resulted in higher uncertainty at the site with the fewest trees in the allometric data set (48), because outliers could be represented multiple times or not at all in each sample. The uncertainty in model fit did not vary with the number of trees in the inventory to which it was applied. In contrast, the uncertainty in predicting individuals was higher than model fit uncertainty when applied to small numbers of trees, but became negligible with 10,000 trees. The importance of this uncertainty source varied with the forest type, being largest for the shrubland, where the model fit was most poor. Low uncertainties were observed where model fit was high, as was the case in the monoculture plantation and in the subtropical jungle where hundreds of trees contributed to the allometric model. In all cases, propagating uncertainty only in the prediction of individuals would underestimate allometric uncertainty. It will always be most correct to include both uncertainty in predicting individuals and uncertainty in model fit, but when large numbers of individuals are involved, as in the case of national forest inventories, the contribution of uncertainty in predicting individuals can be ignored. When the number of trees is small, as may be the case in forest manipulation studies, both sources of allometric uncertainty are likely important and should be accounted for. 

Abstract Few previous studies have described the patterns of leaf characteristics in response to nutrient availability and depth in the crown. Sugar maple has been studied for both sensitivity to light, as a shade-tolerant species, and sensitivity to soil nutrient availability, as a species in decline due to acid rain. To explore leaf characteristics from the top to bottom of the canopy, we collected leaves along a vertical gradient within mature sugar maple crowns in a full-factorial nitrogen (N) by phosphorus (P) addition experiment in three forest stands in central New Hampshire, USA. Thirty-two of the 44 leaf characteristics had significant relationships with depth in the crown, with the effect of depth in the crown strongest for leaf area, photosynthetic pigments and polyamines. Nitrogen addition had a strong impact on the concentration of foliar N, chlorophyll, carotenoids, alanine and glutamate. For several other elements and amino acids, N addition changed patterns with depth in the crown. Phosphorus addition increased foliar P and boron (B); it also caused a steeper increase of P and B with depth in the crown. Since most of these leaf characteristics play a direct or indirect role in photosynthesis, metabolic regulation or cell division, studies that ignore the vertical gradient may not accurately represent whole-canopy performance. 

Historically, emerging and reemerging infectious diseases have caused large, deadly, and expensive multinational outbreaks. Often outbreak investigations aim to identify who infected whom by reconstructing the outbreak transmission tree, which visualizes transmission between individuals as a network with nodes representing individuals and branches representing transmission from person to person. We compiled a database, called OutbreakTrees, of 382 published, standardized transmission trees consisting of 16 directly transmitted diseases ranging in size from 2 to 286 cases. For each tree and disease, we calculated several key statistics, such as tree size, average number of secondary infections, the dispersion parameter, and the proportion of cases considered superspreaders, and examined how these statistics varied over the course of each outbreak and under different assumptions about the completeness of outbreak investigations. We demonstrated the potential utility of the database through 2 short analyses addressing questions about superspreader epidemiology for a variety of diseases, including Coronavirus Disease 2019 (COVID-19). First, we found that our transmission trees were consistent with theory predicting that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events. Additionally, we investigated patterns in how superspreaders are infected. Across trees with more than 1 superspreader, we found preliminary support for the theory that superspreaders generate other superspreaders. In sum, our findings put the role of superspreading in COVID-19 transmission in perspective with that of other diseases and suggest an approach to further research regarding the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry. 

Short-term forecasts of the dynamics of coronavirus disease 2019 (COVID-19) in the period up to its decline following mass vaccination was a task that received much attention but proved difficult to do with high accuracy. However, the availability of standardized forecasts and versioned datasets from this period allows for continued work in this area. Here, we introduce the Gaussian infection state space with time dependence (GISST) forecasting model. We evaluate its performance in one to four weeks ahead forecasts of COVID-19 cases, hospital admissions and deaths in the state of California made with official reports of COVID-19, Google’s mobility reports and vaccination data available each week. Evaluation of these forecasts with a weighted interval score shows them to consistently outperform a naive baseline forecast and often score closer to or better than a high-performing ensemble forecaster. The GISST model also provides parameter estimates for a compartmental model of COVID-19 dynamics, includes a regression submodel for the transmission rate and allows for parameters to vary over time according to a random walk. GISST provides a novel, balanced combination of computational efficiency, model interpretability and applicability to large multivariate datasets that may prove useful in improving the accuracy of infectious disease forecasts. 

Early warning indicators based on critical slowing down have been suggested as a model-independent and low-cost tool to anticipate the (re)emergence of infectious diseases. We studied whether such indicators could reliably have anticipated the second COVID-19 wave in European countries. Contrary to theoretical predictions, we found that characteristic early warning indicators generally decreased rather than increased prior to the second wave. A model explains this unexpected finding as a result of transient dynamics and the multiple timescales of relaxation during a non-stationary epidemic. Particularly, if an epidemic that seems initially contained after a first wave does not fully settle to its new quasi-equilibrium prior to changing circumstances or conditions that force a second wave, then indicators will show a decreasing rather than an increasing trend as a result of the persistent transient trajectory of the first wave. Our simulations show that this lack of timescale separation was to be expected during the second European epidemic wave of COVID-19. Overall, our results emphasize that the theory of critical slowing down applies only when the external forcing of the system across a critical point is slow relative to the internal system dynamics. 

Helminths are parasites that cause disease at considerable cost to public health and present a risk for emergence as novel human infections. Although recent research has elucidated characteristics conferring a propensity to emergence in other parasite groups (e.g. viruses), the understanding of factors associated with zoonotic potential in helminths remains poor. We applied an investigator-directed learning algorithm to a global dataset of mammal helminth traits to identify factors contributing to spillover of helminths from wild animal hosts into humans. We characterized parasite traits that distinguish between zoonotic and non-zoonotic species with 91% accuracy. Results suggest that helminth traits relating to transmission (e.g. definitive and intermediate hosts) and geography (e.g. distribution) are more important to discriminating zoonotic from non-zoonotic species than morphological or epidemiological traits. Whether or not a helminth causes infection in companion animals (cats and dogs) is the most important predictor of propensity to cause human infection. Finally, we identified helminth species with high modelled propensity to cause zoonosis (over 70%) that have not previously been considered to be of risk. This work highlights the importance of prioritizing studies on the transmission of helminths that infect pets and points to the risks incurred by close associations with these animals. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.